**Figure 7.** RGB mosaic of ALOS PALSAR-1 FBD data for the Northeastern US. The red channel shows the HH intensity, the green channel the HV intensity and the blue channel the HH/HV ratio. The black lines denote different mapping zones. The image on the right-hand side illustrates for one mapping zone the number of available FBD acquisitions.

#### **4.1. Retrieval algorithm**

Complex physically-based approaches for the modeling of backscatter as function of forest biophysical attributes have been developed that consider various aspects of the forest structure (e.g., the size and orientation of stem, branches and leaves) as well as scattering mechanisms. However, when aiming at retrieval the model formulation needs to be simple enough so that it can be inverted. A relatively simple physically-based Water-Cloud type of model [22] that has been tested extensively for retrieval with C- and L-band backscatter data models the backscatter from forest, *σ0 for*, as a sum of three contributions [23]:

$$
\sigma\_{\hat{\rho}r}^{0} = \left(1 - \eta\right) \sigma\_{\hat{\g}r}^{0} + \eta \sigma\_{\hat{\g}r}^{0} e^{-\alpha h} + \eta \sigma\_{\text{reg}}^{0} \left(1 - e^{-\alpha h}\right) \tag{1}
$$

The first term describes the direct backscatter from the forest floor, *σ0 gr*, through gaps in the canopy. The parameter *η* represents the percentage to which the ground is covered by the canopy. The second term describes the backscatter from the ground that was attenuated in the canopy. Herein, the exponential represents the two-way tree transmissivity, which is a function of the tree height, *h*, and the two-way signal attenuation, *α*. The last term describes the volume backscatter, *σ0 veg*, from an opaque canopy without gaps. The model can be re-written in the following form:

$$
\sigma\_{fir}^{0} = \sigma\_{gr}^{0} T\_{fir} + \sigma\_{veg}^{0} \left(1 - T\_{fir}\right) \tag{2}
$$

where *Tfor* represents the forest transmissivity:

**4. Mapping AGB across the Northeastern US using multi-temporal ALOS**

Based on a multi-temporal ALOS PALSAR-1 dataset comprising 655 FBD dual polarization

**•** the feasibility of an automated model training and inversion approach, similar to those presented in [7, 21] for ENVISAT ASAR intensity and ERS-1/2 InSAR data in the L-band

**•** the retrieval performance at different spatial scales, considering the influence of the imaging

**Figure 7.** RGB mosaic of ALOS PALSAR-1 FBD data for the Northeastern US. The red channel shows the HH intensity, the green channel the HV intensity and the blue channel the HH/HV ratio. The black lines denote different mapping zones. The image on the right-hand side illustrates for one mapping zone the number of available FBD acquisitions.

Complex physically-based approaches for the modeling of backscatter as function of forest biophysical attributes have been developed that consider various aspects of the forest structure (e.g., the size and orientation of stem, branches and leaves) as well as scattering mechanisms. However, when aiming at retrieval the model formulation needs to be simple enough so that it can be inverted. A relatively simple physically-based Water-Cloud type of model [22] that has been tested extensively for retrieval with C- and L-band backscatter data models the

*for*, as a sum of three contributions [23]:

 hs  a

( ) ( ) 0 00 0 1 1 *h h for gr gr veg e e* a

data for the Northeastern US (Figure 7), following two topics have investigated:

conditions and the benefit of having multi-temporal data stacks.

**PALSAR-1 data**

72 Land Applications of Radar Remote Sensing

**4.1. Retrieval algorithm**

backscatter from forest, *σ0*

s

 h s hs

case;

$$T\_{fir} = \left(1 - \eta\right) + \eta e^{-\alpha h} \tag{3}$$

The model in Eq. (1) expresses the forest backscatter as a function of height and gap fraction. According to scatterometer experiments at X- and C-bands [24], *Tfor* can also be expressed as function of growing stock volume, GSV [m3 /ha]:

$$T\_{far} = e^{-\beta GSV} \tag{4}$$

with *β* being an empirical parameter. Since, in this study, AGB was the observable and GSV is commonly considered a proxy for AGB (i.e. AGB can be estimated from GSV using agedependent expansion factors, e.g., IPCC, 2006), we replaced the volume with biomass, *B* [t/ha]:

$$
\sigma\_{\hat{\rho}v}^{0} = \sigma\_{gr}^{0}e^{-\delta B} + \sigma\_{veg}^{0} \left(1 - e^{-\delta B}\right) \tag{5}
$$

In Eq. (5), *β* has been replaced with *δ* to underline that the forest transmissivity is now expressed as function of biomass.

Two of the three unknowns in the model in Eq. (5) are related to the backscatter from open ground not covered by vegetation (*σ0 gr*) and to what is considered the backscatter from opaque forest canopies with infinite biomass (*σ0 veg*). In [7, 21], it was shown for C-band that the backscatter properties of open ground and dense forest canopies, and their temporal and spatial variations, could be identified with the aid of MODIS VCF by masking the intensity images for areas with low and high VCF canopy cover and taking the mean or median of the measured intensities in the masked areas, respectively. In the case of open ground with low canopy cover, ancillary datasets have to be used to exclude land cover classes (settlements, industrial areas, water surfaces, etc.) for which the backscatter can differ substantially from that of open ground. In the case of the intensity observed over dense forests, denoted as *σ0 df*, an additional compensation for residual backscatter contributions from the ground has to be carried out. The compensation of *σ0 df* for residual ground contributions can be accomplished with:

$$\sigma\_{\text{reg}}^{0}\left(B\_{df}\right) = \frac{\sigma\_{df}^{0} - \sigma\_{gr}^{0}e^{-\delta B\_{df}}}{1 - e^{-\delta B\_{df}}} \tag{6}$$

where *Bdf* represents the AGB of dense forest. The estimation of *σ0 veg* requires knowledge of *δ* and *Bdf*.

Once the parameters have been estimated, the model can be inverted to estimate the biomass from the SAR data. When inverting the model, some intensity measurements might in fact exceed the range of modeled intensities between *σ0 gr* and *σ0 veg*. Inversion for intensities lower than *σ0 gr* would result in negative biomass estimates, which is why a biomass of 0 t/ha has to be assigned. In the case of high intensity values exceeding *σ0 veg*, an inversion is not possible and for intensities slightly lower than *σ0 veg*, the inversion could result in biomass estimates far exceeding the range of realistic biomass values, which is why a maximum biomass level, *Bmax*, has to be defined up to which inversion is carried out. In case of multi-temporal datasets, a weighted combination of the biomass estimates from each image covering a particular pixel location, *Bi* , can be computed to obtain new multi-temporal estimates, *Bmt*. Weights can be calculated using the difference between *σ0 veg* and *σ0 gr*, referred to hereafter as the dynamic range. The larger the dynamic range, the more weight is given to the particular biomass estimate.

#### **4.2. AGB retrieval**

Model training and inversion were carried out for each of the 1310 intensity images (655 HH and 655 HV PALSAR FBD scenes, off-nadir angle of 34.3°) available for the Northeastern United States. The Landsat-based canopy density maps and land cover maps from the National Land Cover Database [25] were used to identify sparse and dense forests in each backscatter image and to mask settlements, industrial areas, agricultural land and water surfaces, respec‐ tively. The forest transmissivity parameter *δ*, which describes the backscatter trend with increasing biomass, could be expected to depend on 1) the imaging conditions, and 2) the forest structural characteristics [26]. Model simulations indicated, however, that in the case of the ALOS PALSAR-1 data available for the Northeastern United States, the potential variations in the forest transmissivity parameter as function of the forest type and imaging conditions could be expected to be minor and that the use of a fixed value for *δ* (0.008 ha/t) for the biomass retrieval represented a justifiable compromise. For a detailed discussion of the forest trans‐ missivity as function of AGB refer to [15]. The AGB of dense forests, *Bdf* (Eq. 6), was determined via the plot data from the Forest Inventory and Analysis (FIA) plot network of the US Forest Service [27] with the 90th percentile of the regional plot biomass distribution [7]. A fixed biomass offset, *ΔB*, was then used with respect to *Bdf* to define the maximum retrievable biomass, *Bmax* (*Bmax*=*Bdf*+*ΔB*), when inverting the models to estimate the biomass for all pixels in the intensity images for which the NLCD land cover map reported forest. *Bmax* affects the retrieval results in the higher biomass ranges and therefore (primarily) derived population statistics such as county totals. That is why *ΔB* was adjusted so that the differences between the total biomass estimates per county and FIA county-level estimates were minimized; this was the case when setting *ΔB* to 30 t/ha (see below). The multi-temporal combination and mosaicing of the single image biomass maps were carried out in a single step for each of six mapping zones in the Northeastern United States. Both, HH- as well as HV-intensity based biomass estimates were considered for the multi-temporal combination. The resulting AGB map is shown in Figure 8.

an additional compensation for residual backscatter contributions from the ground has to be

0 0

*df gr*

Once the parameters have been estimated, the model can be inverted to estimate the biomass from the SAR data. When inverting the model, some intensity measurements might in fact

exceeding the range of realistic biomass values, which is why a maximum biomass level, *Bmax*, has to be defined up to which inversion is carried out. In case of multi-temporal datasets, a weighted combination of the biomass estimates from each image covering a particular pixel

*veg* and *σ0*

range. The larger the dynamic range, the more weight is given to the particular biomass

Model training and inversion were carried out for each of the 1310 intensity images (655 HH and 655 HV PALSAR FBD scenes, off-nadir angle of 34.3°) available for the Northeastern United States. The Landsat-based canopy density maps and land cover maps from the National Land Cover Database [25] were used to identify sparse and dense forests in each backscatter image and to mask settlements, industrial areas, agricultural land and water surfaces, respec‐ tively. The forest transmissivity parameter *δ*, which describes the backscatter trend with increasing biomass, could be expected to depend on 1) the imaging conditions, and 2) the forest structural characteristics [26]. Model simulations indicated, however, that in the case of the ALOS PALSAR-1 data available for the Northeastern United States, the potential variations in the forest transmissivity parameter as function of the forest type and imaging conditions could be expected to be minor and that the use of a fixed value for *δ* (0.008 ha/t) for the biomass retrieval represented a justifiable compromise. For a detailed discussion of the forest trans‐ missivity as function of AGB refer to [15]. The AGB of dense forests, *Bdf* (Eq. 6), was determined via the plot data from the Forest Inventory and Analysis (FIA) plot network of the US Forest Service [27] with the 90th percentile of the regional plot biomass distribution [7]. A fixed biomass offset, *ΔB*, was then used with respect to *Bdf* to define the maximum retrievable biomass, *Bmax* (*Bmax*=*Bdf*+*ΔB*), when inverting the models to estimate the biomass for all pixels

*e*

*df*

*gr* and *σ0*

*B*

d

*df*

*e*

d

*gr* would result in negative biomass estimates, which is why a biomass of 0 t/ha has to

, can be computed to obtain new multi-temporal estimates, *Bmt*. Weights can be

1

*veg df B*

s s

( )

*B*

0

where *Bdf* represents the AGB of dense forest. The estimation of *σ0*

s

be assigned. In the case of high intensity values exceeding *σ0*

exceed the range of modeled intensities between *σ0*

and for intensities slightly lower than *σ0*

calculated using the difference between *σ0*

*df* for residual ground contributions can be accomplished

*veg*, the inversion could result in biomass estimates far

(6)

*veg* requires knowledge of *δ*

*veg*. Inversion for intensities lower

*veg*, an inversion is not possible

*gr*, referred to hereafter as the dynamic

carried out. The compensation of *σ0*

74 Land Applications of Radar Remote Sensing

with:

and *Bdf*.

than *σ0*

location, *Bi*

estimate.

**4.2. AGB retrieval**

**Figure 8.** Forest AGB map for the Northeastern United States produced from 655 ALOS PALSAR-1 dual polarization intensity images acquired in 2007/08.

Since the ALOS PALSAR-1 data could not be compared directly to the plot data of the FIA database (note that the exact FIA plot locations are not publicly available), we compared the ALOS PALSAR-1 biomass maps to the biomass maps from the National Biomass and Carbon Dataset 2000 (NBCD) that were produced through the fusion of SRTM and NED elevation, Landsat TM and the FIA forest inventory data [17, 28]. The algorithm performance was assessed with the root mean square difference (RMSD) between the ALOS PALSAR-1 and NBCD maps calculated separately for different mapping zones (see Figure 7), which were adapted from the National Land Cover Database (NLCD) [25].

At full resolution, the RMSDs were large (>80 t/ha). This might on one side have been a consequence of noise in the ALOS PALSAR-1 data (residual speckle, small scale environmental effects) but it also has to be considered that at the level of 30 m pixels, the NBCD map contains considerable uncertainty as well; note that the NBCD map was validated at segment (i.e. hectare) level. The comparison of the maps at 30 m pixel size was therefore of limited explan‐ atory value. Hence, the comparison at different pixel aggregation scales between 150 m and 6 km have been repeated. When aggregating the maps by means of simple block-averaging, the agreement increased substantially (Figure 9, right). The largest improvement could be observed when aggregating up to ~1 km pixel size, for which the RMSD was in a range of 20 to 25 t/ha. In Figure 9 (left), the ALOS PALSAR-1 biomass estimates for two mapping zones have been plotted against the NBCD biomass estimates for pixel sizes of 150 and 600 m. The comparison of the ALOS PALSAR-1 and NBCD biomass maps revealed a good agreement along the 1:1 line. The spread along the 1:1 line reduced substantially with increasing pixel size, which can be seen in Figure 9 (left) from the decreasing length of the vertical bars. The ALOS PALSAR-1 biomass estimates, however, tended to be lower than the NBCD estimates when approaching a biomass of 200 t/ha, indicating saturation effects in the L-band data.

**Figure 9.** Left: Average (circles) and standard deviation (vertical bars) of the multi-temporal ALOS PALSAR-1 biomass estimates for intervals of NBCD biomass at zones 61 and 64. Right: Root Mean Square Difference (RMSD) between the ALOS PALSAR-1 and NBCD biomass maps as a function of the pixel aggregation scale.

#### **4.3. Importance of multi-temporal acquisitions**

To evaluate the importance of having multi-temporal stacks of L-band intensity, the retrieval performance for single images is discussed first. Figure 10 shows for the L-band intensity images covering one of the mapping zones the RMSD between the biomass estimates from single intensity images and NBCD as function of the dynamic range (at 150 m pixel size).

At full resolution, the RMSDs were large (>80 t/ha). This might on one side have been a consequence of noise in the ALOS PALSAR-1 data (residual speckle, small scale environmental effects) but it also has to be considered that at the level of 30 m pixels, the NBCD map contains considerable uncertainty as well; note that the NBCD map was validated at segment (i.e. hectare) level. The comparison of the maps at 30 m pixel size was therefore of limited explan‐ atory value. Hence, the comparison at different pixel aggregation scales between 150 m and 6 km have been repeated. When aggregating the maps by means of simple block-averaging, the agreement increased substantially (Figure 9, right). The largest improvement could be observed when aggregating up to ~1 km pixel size, for which the RMSD was in a range of 20 to 25 t/ha. In Figure 9 (left), the ALOS PALSAR-1 biomass estimates for two mapping zones have been plotted against the NBCD biomass estimates for pixel sizes of 150 and 600 m. The comparison of the ALOS PALSAR-1 and NBCD biomass maps revealed a good agreement along the 1:1 line. The spread along the 1:1 line reduced substantially with increasing pixel size, which can be seen in Figure 9 (left) from the decreasing length of the vertical bars. The ALOS PALSAR-1 biomass estimates, however, tended to be lower than the NBCD estimates when approaching a biomass of 200 t/ha, indicating saturation effects in the L-band data.

76 Land Applications of Radar Remote Sensing

**Figure 9.** Left: Average (circles) and standard deviation (vertical bars) of the multi-temporal ALOS PALSAR-1 biomass estimates for intervals of NBCD biomass at zones 61 and 64. Right: Root Mean Square Difference (RMSD) between the

To evaluate the importance of having multi-temporal stacks of L-band intensity, the retrieval performance for single images is discussed first. Figure 10 shows for the L-band intensity images covering one of the mapping zones the RMSD between the biomass estimates from single intensity images and NBCD as function of the dynamic range (at 150 m pixel size).

ALOS PALSAR-1 and NBCD biomass maps as a function of the pixel aggregation scale.

**4.3. Importance of multi-temporal acquisitions**

**Figure 10.** RMSD between the biomass estimates from single HH (+) and HV (o) intensity images and NBCD as func‐ tion of the dynamic range (zone 64, 150 m pixel size).

The figure clearly shows that the dynamic range can be considered one of the main factors influencing the retrieval performance. The figure also shows that the RMSDs for HV intensity images tended to be lower (40-80 t/ha) than for HH intensity images (50-100 t/ ha). For a given FBD HH and HV image pair, the RMSD was always lower for the HV image (5 to 10 t/ha). The differences in the dynamic ranges were most likely a conse‐ quence of differing imaging conditions. For the images covering New York State, we compared the dynamic ranges with the weather conditions at the time of the sensor overpasses. The comparison with the weather data revealed no correlation with tempera‐ ture; note that the temperature was consistently above the freezing point for all images so that no major temperature related fluctuations of the dielectric properties of the trees were to be expected. Weak negative correlations were observed when relating the dynamic range to the total amount of rain in the days prior to the sensor overpasses. In both polariza‐ tions, there was a trend towards lower dynamic ranges with increasing amounts of precipitation (i.e. with increasing wetness of the soils and vegetation). The Pearson correlation coefficients were between -0.3 and -0.5 depending on which timeframe prior to the sensor overpasses was considered. This result is consistent with previous finding [29].

Figure 11 demonstrates the benefit of having multi-temporal data for an area where five FBD images were available. The dashed line shows the RMSD for each intensity image (HV: 55-59 t/ha, HH: 58-71 t/ha, 150 m pixel size), the solid line shows the change in RMSD when successively integrating the particular images into the multi-temporal retrieval.

The multi-temporal combination resulted in a clear improvement of the RMSD when com‐ bining the available 5 HV images for about 10 t/ha (compared to the best HV image). When, in addition, integrating the corresponding HH images, only slight additional improvements of the RMSD for about 3 t/ha were achieved. These results confirmed that the multi-temporal combination allowed for significant improvements of the biomass estimates. However, it has to be noted that the multi-temporal coverage acquired by ALOS PALSAR-1 was not consistent

**Figure 11.** Effect of the multi-temporal combination on the agreement of the ALOS and NBCD biomass maps (at 150 m pixel size). The circles connected by the dashed line denote the RMSD for each image and those connected by the solid line show how the RMSD changed when successively integrating the single image estimates into the multi-tem‐ poral retrieval.

across larger areas as regionally between one and five images were acquired per year. As a result of the varying multi-temporal coverage (see Figure 7), the accuracy of the map in Figure 8 was not consistent across the entire study area. The comparison with NBCD confirmed that locally, the performance of the retrieval depended strongly on the multi-temporal coverage (i.e. the number of images) as well as the weather conditions under which the particular set of images have been acquired. The results therefore stress the need for consistent multi-temporal acquisition strategies for upcoming spaceborne L-band missions.

#### **4.4. Accuracy at county scale**

The comparison of the ALOS PALSAR-1 biomass maps with NBCD indicated that, at least at aggregated scales, the spatial distribution of biomass could be depicted with the retrieval approach presented. To further assess the performance of the retrieval algorithm, ALOS PALSAR-1 biomass maps have been compared to FIA county-level total AGB statistics for 143 counties in Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York and New Jersey. The county statistics were obtained via the EVALIDator online inventory tool of the US Forest Service. For the comparison of the ALOS PALSAR-1 and FIA estimates, the ALOS PALSAR-1 per-pixel (30x30 m2 ) biomass estimates were summed per county. The comparison of the ALOS PALSAR-1 and FIA county-level estimates of total AGB resulted in a coefficient of determination (*R2* ) of 0.98 and a root mean square error (RMSE) of 2.75 106 t. When calculating the average biomass per county (i.e. the total biomass divided by the county size in hectares), the RMSE was 12.9 t/ha and the R2 was 0.86 (Figure 12).

**Figure 12.** Average AGB according to the ALOS PALSAR-1 biomass map and FIA biomass statistics for 143 counties in the Northeastern United States.

The FIA county statistics included information about the sampling error, which could be used to approximate the confidence intervals of the FIA estimates [27]. The sampling error was between 2 % and 110 % for the largest counties (with the largest number of sample plots) and for the smallest counties, respectively; the size of the counties ranged from 64 to 17, 686 km2 . The ALOS PALSAR-1 total AGB estimates were within the 95 % confidence intervals of the FIA estimates for most (92 %) of the 143 counties.

across larger areas as regionally between one and five images were acquired per year. As a result of the varying multi-temporal coverage (see Figure 7), the accuracy of the map in Figure 8 was not consistent across the entire study area. The comparison with NBCD confirmed that locally, the performance of the retrieval depended strongly on the multi-temporal coverage (i.e. the number of images) as well as the weather conditions under which the particular set of images have been acquired. The results therefore stress the need for consistent multi-temporal

**Figure 11.** Effect of the multi-temporal combination on the agreement of the ALOS and NBCD biomass maps (at 150 m pixel size). The circles connected by the dashed line denote the RMSD for each image and those connected by the solid line show how the RMSD changed when successively integrating the single image estimates into the multi-tem‐

The comparison of the ALOS PALSAR-1 biomass maps with NBCD indicated that, at least at aggregated scales, the spatial distribution of biomass could be depicted with the retrieval approach presented. To further assess the performance of the retrieval algorithm, ALOS

acquisition strategies for upcoming spaceborne L-band missions.

**4.4. Accuracy at county scale**

78 Land Applications of Radar Remote Sensing

poral retrieval.

### **5. Synergetic use of multi-temporal ALOS PALSAR-1 and ENVISAT ASAR data for forest and agricultural mapping at national scale in Africa**

The forest cover in Malawi is approximately 32, 000 sqkm corresponding to 34% of country surface [30]. Natural forests represent the remainder of the Miombo (Swahili word for *Brachystegia*) forests that once covered almost the whole country. The area of natural forests (primary forest 29%, other naturally regenerated forest 60%) over the years has remained unchanged – annual change rate between 2005 and 2010 is less than 1% – with the exception of forest reserves that have continued to grow in number. Characteristically, the trees shed their leaves for a short period in the dry season (June to October) to reduce water loss, and produce a flush of new leaves just before the onset of the rainy season (November-December to March-April). Planted forests consist of softwood (mainly *Pinus patula*) and hardwood species (mainly *Eucalyptus*).

Thanks to the availability of a multi-year ALOS PALSAR-1 FBD data set acquired in a systematic way between 2006 and 2011 [9] and a seasonal ENVISAT ASAR Alternating Polarization (AP) data stack planned and regularly acquired during the wet season, the possibility is explored to generate countrywide a forest map and a cultivated area map during the crop wet season. In synthesis, the method is based on the synergetic use of multi-temporal data by considering the different data characteristics and given acquisition modes, the acquisition periods, and the vegetation phenology during the acquisitions. Moreover, since the ultimate goal is to provide this type of information over large areas, i.e. at least national scale, and to upscale the proposed method to other regions [31], the processing chain has been developed in such a way that the products are generated automatically. Finally, it is worth mentioning (cf. *Fusion of interferometric SAR and photogrammetric Elevation Data*) that using the same ALOS PALSAR-1 repeat-pass InSAR data, a DEM with higher quality than the SRTM one can be provided in those nearly equatorial regions characterizes by non-dense forest condition.

### **5.1. Method**

The data processing flow can be divided into two distinct steps. The first one converts the multi-temporal SLC data into terrain geocoded backscattering coefficient (σ°). In addition, for the 46-days ALOS-PALSAR-1 and 1-day Cosmo-SkyMed Stripmap interferometric data, coherence (γ) is computed and terrain geocoded. Noteworthy, but not discussed here, are the ionospheric effects in the equatorial region observed in the L-band data [35]. In this case, less than 10% of the ALOS PALSAR-1 images have been omitted from the processing due to severe (several dB) and non-systematic striping along the azimuth. However, in other cases [31] around one fourth of the ALOS PALSAR-1 data could not be used. In the second step, the forest and the cultivated area map are generated.

	- **•** Co-registration Images acquired with the same observation geometry and mode are co-registered in slant range geometry. This step is mandatory to allow time-series speckle filtering.
	- **•** Time-series speckle filtering Within the multi-temporal filtering an optimum weight‐ ing filter is introduced to balance differences in reflectivity between images at different times [32]: this allows enhancing significantly the radiometric resolution and preserve the spatial one. Multi-temporal filtering is based on the assumption that the same

resolution element on the ground is illuminated by the radar beam in the same way, and corresponds to the same slant range coordinates in all images of the time series. The reflectivity can change from one time to the next due to a change in the dielectric and geometrical properties of the elementary scatters, but should not change due to a different position of the resolution element with respect to the radar.

*Brachystegia*) forests that once covered almost the whole country. The area of natural forests (primary forest 29%, other naturally regenerated forest 60%) over the years has remained unchanged – annual change rate between 2005 and 2010 is less than 1% – with the exception of forest reserves that have continued to grow in number. Characteristically, the trees shed their leaves for a short period in the dry season (June to October) to reduce water loss, and produce a flush of new leaves just before the onset of the rainy season (November-December to March-April). Planted forests consist of softwood (mainly *Pinus patula*) and hardwood

Thanks to the availability of a multi-year ALOS PALSAR-1 FBD data set acquired in a systematic way between 2006 and 2011 [9] and a seasonal ENVISAT ASAR Alternating Polarization (AP) data stack planned and regularly acquired during the wet season, the possibility is explored to generate countrywide a forest map and a cultivated area map during the crop wet season. In synthesis, the method is based on the synergetic use of multi-temporal data by considering the different data characteristics and given acquisition modes, the acquisition periods, and the vegetation phenology during the acquisitions. Moreover, since the ultimate goal is to provide this type of information over large areas, i.e. at least national scale, and to upscale the proposed method to other regions [31], the processing chain has been developed in such a way that the products are generated automatically. Finally, it is worth mentioning (cf. *Fusion of interferometric SAR and photogrammetric Elevation Data*) that using the same ALOS PALSAR-1 repeat-pass InSAR data, a DEM with higher quality than the SRTM one can be provided in those nearly equatorial regions characterizes by non-dense forest

The data processing flow can be divided into two distinct steps. The first one converts the multi-temporal SLC data into terrain geocoded backscattering coefficient (σ°). In addition, for the 46-days ALOS-PALSAR-1 and 1-day Cosmo-SkyMed Stripmap interferometric data, coherence (γ) is computed and terrain geocoded. Noteworthy, but not discussed here, are the ionospheric effects in the equatorial region observed in the L-band data [35]. In this case, less than 10% of the ALOS PALSAR-1 images have been omitted from the processing due to severe (several dB) and non-systematic striping along the azimuth. However, in other cases [31] around one fourth of the ALOS PALSAR-1 data could not be used. In the second step, the forest

**•** Co-registration – Images acquired with the same observation geometry and mode are co-registered in slant range geometry. This step is mandatory to allow time-series

**•** Time-series speckle filtering – Within the multi-temporal filtering an optimum weight‐ ing filter is introduced to balance differences in reflectivity between images at different times [32]: this allows enhancing significantly the radiometric resolution and preserve the spatial one. Multi-temporal filtering is based on the assumption that the same

species (mainly *Eucalyptus*).

80 Land Applications of Radar Remote Sensing

condition.

**5.1. Method**

and the cultivated area map are generated. **1.** Sigma nought (σ°) is derived as follows:

speckle filtering.

show noticeable lower (bare soil, water, dry or low vegetation) or higher (settlements) values. It has to be pointed out that this net discrimination between the different land covers is given by the combination of two factors. First, the selected dry season period, which, due to the very limited vegetation development, considerably reduces the confusion between low forest biomass and others vegetation types, agriculture (i.e. maize) in particular. Second, the long wavelength, on one hand tends to smooth out the radar backscatter from land covers characterized by limited surface roughness, on the other hand it guarantees a relatively high backscatter of those land covers, like forest, where double bounce and volume scattering prevail. Note that this separability is not obtainable at shorter wavelengths, due to similarities in the volume scattering contributions.

	- **a.** The crop start of season, which is identified when there is a relative minimum followed by a maximum increment between two subsequent acquisitions;
	- **b.** The crop peak of season, which is identified when there is a relative maximum followed by a minimum increment between two subsequent acquisitions;
	- **c.** The pixel, which is classified as crop if:
		- **•** conditions 1 and 2 are satisfied;
		- **•** the range between relative minimum and maximum attains a minimum value;
		- **•** the temporal duration between 1 and 2 is within a given duration.

#### **5.2. Forest and cultivated area products**

To cover the entire country, 65 ALOS PALSAR-1 FBD/FBS frames distributed over 5 adjacent tracks are necessary. Three to four coverages per dry season per year during four years have been used, resulting in the around 900 SLC scenes. A coherence-intensity mosaic using 46-days interferometric ALOS PALSAR-1 FBS data acquired during the wet season (January-February 2008) has been additionally generated. A total of 225 ENVISAT ASAR AP images acquired five times from October 2007 to April 2008 are used to monitor the crop growth along the whole wet season. The step to transform the SLC data into geo-referenced σ° and γ products is doubtless the most time consuming one. For this reason, the data processing is performed by means of a PC based cluster solution. Noteworthy, the algorithms have been written in a such way to fully exploit the characteristics of the processors. This setting allows to carry out the processing without any intervention of the operator and to process the data sets within few days.

show noticeable lower (bare soil, water, dry or low vegetation) or higher (settlements) values. It has to be pointed out that this net discrimination between the different land covers is given by the combination of two factors. First, the selected dry season period, which, due to the very limited vegetation development, considerably reduces the confusion between low forest biomass and others vegetation types, agriculture (i.e. maize) in particular. Second, the long wavelength, on one hand tends to smooth out the radar backscatter from land covers characterized by limited surface roughness, on the other hand it guarantees a relatively high backscatter of those land covers, like forest, where double bounce and volume scattering prevail. Note that this separability is not obtainable at shorter wavelengths, due to similarities in the volume scattering contributions. **3.** Cultivated area – Pre-requisite for the generation of this product is to obtain a seasonal data set as far as possible regularly acquired along the whole crop season and according to the crop practices. This allows i) to reduce the confusion between cropped areas and the surrounding vegetated (non crop) areas; and ii) to monitor the crop development [36, 37]. The specific sensitivity of active microwave short wavelength sensors (C- and X-band) to soil properties, such as roughness and moisture content, enables the possibility to detect these changes already at the earliest stage during the field preparation, i.e. ploughing (high backscatter) and harrowing (low backscatter). During the second phase (namely from flowering to plant drying stage), the dielectric and structure properties of the plant are the key factors determining the high reflectivity at C-band HH polarization. Finally, the lower reflectivity during the plant drying is caused by the loss of plant moisture. As

proposed in [36, 37], an efficient way to quantitatively describe the σo

product according to:

82 Land Applications of Radar Remote Sensing

**c.** The pixel, which is classified as crop if: **•** conditions 1 and 2 are satisfied;

**5.2. Forest and cultivated area products**

signature is to derive appropriate temporal features, i.e. the relative minimum and maximum including corresponding dates; their difference; the minimum and maximum ratio between two subsequent acquisitions. These features are used to generate the

**a.** The crop start of season, which is identified when there is a relative minimum followed by a maximum increment between two subsequent acquisitions;

**b.** The crop peak of season, which is identified when there is a relative maximum followed by a minimum increment between two subsequent acquisitions;

**•** the range between relative minimum and maximum attains a minimum value;

**•** the temporal duration between 1 and 2 is within a given duration.

To cover the entire country, 65 ALOS PALSAR-1 FBD/FBS frames distributed over 5 adjacent tracks are necessary. Three to four coverages per dry season per year during four years have been used, resulting in the around 900 SLC scenes. A coherence-intensity mosaic using 46-days interferometric ALOS PALSAR-1 FBS data acquired during the wet season (January-February

temporal crop

Figure 13 and 14 illustrate four multi-temporal mosaics: the multi-year ALOS PALSAR-1 FBD mosaic acquired during the dry season (Figure 13, left); the seasonal (October to April) ENVISAT ASAR HH mosaic (Figure 13 right); an ENVISAT ASAR HH mosaic (October and January, the months showing the most significant radiometric changes) combined with the ALOS PALSAR-1 HV July one (Figure 14 left); the ALOS PALSAR-1 HH coherence-intensity mosaic (Figure 14 centre).

**Figure 13.** (left) Multi-year ALOS PALSAR-1 FBD mosaic, 15m acquired during the dry season (mean HH=red, mean HV=green, mean HH / mean HV=blue); (right) Seasonal ENVISAT ASAR HH mosaic, 15m (ASAR HH October=red, ASAR HH December=green, ASAR HH January=blue).

**Figure 14.** (left) ENVISAT ASAR HH-ALOS PALSAR-1 HV mosaic, 15m (ASAR HH December=red, PALSAR-1 HV Ju‐ ly=green, ASAR HH January=blue); (centre) ALOS PALSAR-1 HH (FBS) coherence-intensity mosaic, 10m (coherence Jan‐ uary-February=red, mean intensity=green, intensity difference=blue); (top right) detail of figure left; (bottom right) detail of figure centre.

These examples clearly show that depending upon the selected sensor, the acquisition mode and time, and a smart data integration, different types of information (i.e. products) can be derived. It is worth mentioning that the purpose of data synergy is not exclusively to obtain higher accuracies or more information, but it is also intended to simplify and automatize the products generation. *Conditio sine qua non* is, on one hand, to recognize the sensor capabilities and limitations – including the processing techniques – on the other hand, to understand the object, its characteristics, and the environmental surrounding conditions.

The multi-year ALOS PALSAR-1 FBD mosaic (Figure 13 left) acquired during the dry season undoubtedlyshows a cleardistinctionbetweenforest(greencolour) andother covertypes (blue tonalities). Note that this net separation is the outcome of i) the excellent quality of the multitemporal speckle filtering, and ii) the averaging (omitting some rare outliers) of the HH intensities, and the HV respectively over the dry season period and years (total of 14 images). Both operations contribute to considerably improve the signal-to-noise ratio andto enhance the level of detail. Concerning the temporal averaging, it has to be pointed out that this proce‐ dure is more than reasonable, because the SAR data have been exclusively selected in the dry period, where the vegetation phenology is stable, in terms of roughness and dielectric proper‐ ties, and, the forest extent variations are negligible. In order to demonstrate the usefulness of

this approach, for a selected area, a 1-day interferometric Cosmo-SkyMed Stripmap image pair (3m resolution) acquired in September has been acquired and processed. Figure 15 shows, for comparison purposes, the single-date and multi-year ALOS PALSAR-1 products, and the 1 day interferometric Cosmo-Skymed Stripmap pair (3m resolution). The high level of detail of the multi-year ALOS PALSAR-1 product (bottom left) is particularly appreciable by compar‐ ing the speckle effect in the forest in the two ALOS-PALSAR-1 products, and the better feature delineations (for instance in the riparian forest) in the multi-year one. Furthermore, by visually inspecting the single trees in the multi-year ALOS PALSAR-1 product and in the Cosmo-SkyMed one, it is noticeable that almost all single trees identifiable in the 3m image are easily recognisable in the 15m multi-year scene, hardly in 15m single-date one.With respectto the use of X-band data for forest applications, it should be shortly remarked that, in general, the 1 day Cosmo-SkyMed coherence or the bistatic TerraSAR-X-Tandem one are essential, because the limited σo dynamic range at this frequency significantly reduces the discrimination capabilitiesinvegetatedareas,ifexclusivelyintensityisused.FordetailsrefertoHoleczetal.[38].

**Figure 14.** (left) ENVISAT ASAR HH-ALOS PALSAR-1 HV mosaic, 15m (ASAR HH December=red, PALSAR-1 HV Ju‐ ly=green, ASAR HH January=blue); (centre) ALOS PALSAR-1 HH (FBS) coherence-intensity mosaic, 10m (coherence Jan‐ uary-February=red, mean intensity=green, intensity difference=blue); (top right) detail of figure left; (bottom right)

These examples clearly show that depending upon the selected sensor, the acquisition mode and time, and a smart data integration, different types of information (i.e. products) can be derived. It is worth mentioning that the purpose of data synergy is not exclusively to obtain higher accuracies or more information, but it is also intended to simplify and automatize the products generation. *Conditio sine qua non* is, on one hand, to recognize the sensor capabilities and limitations – including the processing techniques – on the other hand, to understand the

The multi-year ALOS PALSAR-1 FBD mosaic (Figure 13 left) acquired during the dry season undoubtedlyshows a cleardistinctionbetweenforest(greencolour) andother covertypes (blue tonalities). Note that this net separation is the outcome of i) the excellent quality of the multitemporal speckle filtering, and ii) the averaging (omitting some rare outliers) of the HH intensities, and the HV respectively over the dry season period and years (total of 14 images). Both operations contribute to considerably improve the signal-to-noise ratio andto enhance the level of detail. Concerning the temporal averaging, it has to be pointed out that this proce‐ dure is more than reasonable, because the SAR data have been exclusively selected in the dry period, where the vegetation phenology is stable, in terms of roughness and dielectric proper‐ ties, and, the forest extent variations are negligible. In order to demonstrate the usefulness of

object, its characteristics, and the environmental surrounding conditions.

detail of figure centre.

84 Land Applications of Radar Remote Sensing

**Figure 15.** (top left) Single-date ALOS PALSAR-1 FBD mosaic, 15m (mean HH=red, mean HV=green, mean HH / mean HV=blue); (bottom left) Multi-year ALOS PALSAR-1 FBD mosaic, 15m; (bottom right) Cosmo-SkyMed Stripmap coher‐ ence-intensity, 3m (1 day coherence=red, mean intensity=green, intensity difference=blue).

As expected, due to the shorter wavelength, forest is quite less distinguishable in the seasonal ENVISAT ASAR mosaic (Figure 13 right) confirming the conclusions presented in the comparative study carried out by Mitchell et al. [39]. The predominant colours in this image are the cyan (southward) and blue-violet (northward), corresponding to the crop growth after the start of the rain (around November). This is reflected in the December (green) and January (blue) acquisition, where the C-band backscattering coefficient almost attains its highest values due to the rapid crop growth (i.e. increase in the surface and volume scattering) and the high crop moisture content (increase in the dielectric constant). It is interesting to note the corre‐ spondence between the blue areas in the ALOS PALSAR-1 FBD mosaic (bare soil during the dry season) becoming brightly coloured in the ENVISAT ASAR mosaic (cultivated area during the wet season). This is becoming obvious in Figure 14 left (and detail, top right), where the ALOS PALSAR-1 HV July mosaic (green) has been merged with the ENVISAT ASAR HH mosaic of December (red) and January (blue). This colour composite demonstrates, in a qualitative but evident way, that data synergy is undoubtedly beneficial, if exploited in a wise manner.

Coherence is unquestionably a complementary and valuable source of information. However, in practice, useful interferometric correlation is often not easy to obtain due to unfavourable baseline conditions and unsystematic atmospheric effects. Moreover, the temporal decorrela‐ tion sometimes causes interpretation uncertainties. Figure 14 centre (and detail, bottom right) shows a 46-days repeat-pass ALOS PALSAR-1 HH coherence-intensity mosaic generated from an FBS image pair acquired in January and February 2008. As expected, forest has a low correlation and an average high intensity (green), meaning that the forest didn't vary in this gap of time; blue (large intensity difference), particularly visible in the detail bottom right, represents the growing fields; the red tonalities correspond to those areas where the cover changes were minimal, i.e. meaning primarily rough bare soil areas. Nonetheless, it should be considered that tiny crops at L-band are almost transparent, hence resulting into a relatively medium coherence, as the rough bare soil. It turns out that the cultivated area is underesti‐ mated in favour of bare soil. In synthesis, a forest map could be generated even if, due to the long repeat-pass and the different baselines, the uncertainties can be relevant; these ambigu‐ ities are significantly higher for the cultivated area also considering that for this product long acquisition time intervals are unsuitable [37]. A final example on coherence is illustrated in Figure 16, which it has been obtained from a July-August FBD image pair.

Observing the coherence, the Miombo forest is not distinguishable in the HH polarization (average γ is 0.6), hardly detectable in the HV (average γ is 0.5). In the multi-year ALOS PALSAR-1 FBD intensity colour composite, as extensively presented and discussed above, forest is clearly separable from the surrounding land covers. It is anticipated that the perpen‐ dicular baseline of the interferometric pair is 280m, which for this frequency, is thereby appropriate for thematic analysis. The Miombo forest – bare in the dry season – on average has a tree height significantly less than 10m and a diameter breast height ranging from 10 to 20 cm corresponding to a relatively low biomass (often considerably less than 100 tons/ha). This means that the main scattering contribution is the volume, primarily induced by the tree branches; hence the HH radar backscatter is markedly attenuated. This is reflected in both, HH

**Figure 16.** (left) ALOS PALSAR-1 46-days HH July-August coherence; (centre) HV July-August coherence; (right) multiyear ALOS PALSAR-1 FBD image, 15m resolution (mean HH=red, mean HV=green, mean HH / mean HV=blue).

coherence (Figure 16 left) and HH intensity (Figure 16 right). At HV intensity, forest is well recognizable in green (Figure 16 right). This distinction is mainly given by the temporal averaging over the dry seasons and four years, which strongly enhances the separability of the various land types. On the opposite, for the HV coherence (Figure 16 centre), the discrimination between forest and the surrounding area (both having a relatively high coherence) is very limited: this is due, on one hand, to 46-days temporal decorrelation, on the other hand, to the impossibility to perform a temporal averaging. In summary, coherence is doubtless a valuable source of information, however, it should be used with care.

Based on the above considerations and evaluations, the most suitable solution is to use the multi-year ALOS PALSAR FBD data set for the forest area product, and the seasonal ENVISAT ASAR one for the cultivated area. Furthermore, in order to understand the contribution of the seasonal ENVISAT ASAR for the forest area (Figure 17 left), the obtained cultivated area (Figure 17 right) is merged in IF condition with the forest area one.

The two maps are performed using a hierarchical prior knowledge-based classifier. For the forest area the input data were the mean HH intensity and the corresponding mean HV intensity of the multi-year ALOS PALSAR FBD data set. Concerning the cultivated area, the temporal features of the seasonal ENVISAT ASAR data set have been used.

### **5.3. Accuracy at national scale**

As expected, due to the shorter wavelength, forest is quite less distinguishable in the seasonal ENVISAT ASAR mosaic (Figure 13 right) confirming the conclusions presented in the comparative study carried out by Mitchell et al. [39]. The predominant colours in this image are the cyan (southward) and blue-violet (northward), corresponding to the crop growth after the start of the rain (around November). This is reflected in the December (green) and January (blue) acquisition, where the C-band backscattering coefficient almost attains its highest values due to the rapid crop growth (i.e. increase in the surface and volume scattering) and the high crop moisture content (increase in the dielectric constant). It is interesting to note the corre‐ spondence between the blue areas in the ALOS PALSAR-1 FBD mosaic (bare soil during the dry season) becoming brightly coloured in the ENVISAT ASAR mosaic (cultivated area during the wet season). This is becoming obvious in Figure 14 left (and detail, top right), where the ALOS PALSAR-1 HV July mosaic (green) has been merged with the ENVISAT ASAR HH mosaic of December (red) and January (blue). This colour composite demonstrates, in a qualitative but evident way, that data synergy is undoubtedly beneficial, if exploited in a wise

Coherence is unquestionably a complementary and valuable source of information. However, in practice, useful interferometric correlation is often not easy to obtain due to unfavourable baseline conditions and unsystematic atmospheric effects. Moreover, the temporal decorrela‐ tion sometimes causes interpretation uncertainties. Figure 14 centre (and detail, bottom right) shows a 46-days repeat-pass ALOS PALSAR-1 HH coherence-intensity mosaic generated from an FBS image pair acquired in January and February 2008. As expected, forest has a low correlation and an average high intensity (green), meaning that the forest didn't vary in this gap of time; blue (large intensity difference), particularly visible in the detail bottom right, represents the growing fields; the red tonalities correspond to those areas where the cover changes were minimal, i.e. meaning primarily rough bare soil areas. Nonetheless, it should be considered that tiny crops at L-band are almost transparent, hence resulting into a relatively medium coherence, as the rough bare soil. It turns out that the cultivated area is underesti‐ mated in favour of bare soil. In synthesis, a forest map could be generated even if, due to the long repeat-pass and the different baselines, the uncertainties can be relevant; these ambigu‐ ities are significantly higher for the cultivated area also considering that for this product long acquisition time intervals are unsuitable [37]. A final example on coherence is illustrated in

Figure 16, which it has been obtained from a July-August FBD image pair.

Observing the coherence, the Miombo forest is not distinguishable in the HH polarization (average γ is 0.6), hardly detectable in the HV (average γ is 0.5). In the multi-year ALOS PALSAR-1 FBD intensity colour composite, as extensively presented and discussed above, forest is clearly separable from the surrounding land covers. It is anticipated that the perpen‐ dicular baseline of the interferometric pair is 280m, which for this frequency, is thereby appropriate for thematic analysis. The Miombo forest – bare in the dry season – on average has a tree height significantly less than 10m and a diameter breast height ranging from 10 to 20 cm corresponding to a relatively low biomass (often considerably less than 100 tons/ha). This means that the main scattering contribution is the volume, primarily induced by the tree branches; hence the HH radar backscatter is markedly attenuated. This is reflected in both, HH

manner.

86 Land Applications of Radar Remote Sensing

Validation involves the collection of ground reference data for the validation of remote sensing based products. Usually it is carried out by sampling units, i.e. points unambiguously identified by co-ordinates. As shown in Figure 18, systematic grids are used with randomly selected starting corner co-ordinates in order to ensure a representative and spatially welldistributed sample.

**Figure 17.** (left) Forest area generated from multi-year ALOS PALSAR-1 FBD data; (right) Cultivated area generated from seasonal ENVISAT ASAR data.

From an operational perspective, the grouping of sample points in clusters is encouraged. Although this approach may introduce some degree of statistical bias, by significantly reducing travelling time between sample locations, it leaves more resources available for data collection in the field. The relevant parameters of this systematic cluster approach are: 1) distance between clusters; 2) number of points per cluster; 3) distance of points within cluster. The values can be fixed considering several criteria and constraints: 1) available budget; 2) size and shape of the area of interest; 3) resolution of the remote sensing images; 4) logistics; 5) average dimension of the area to be classified. In this case, it has been opted for 1km distance between the clusters, 16 points per cluster, and 250m distance between the points within the cluster. A total of 868 valid points have been collected.

Table 2 shows the obtained confusion matrices for the forest product exclusively based on the multi-year ALOS PALSAR-1 FBD (top) and for the forest product generated by combining the forest and cultivated area product (bottom). In general, the obtained accuracies are high. The main difference between the two tables is in the reduction of the omission errors of the classes sugar cane and crop, and urban. Sugar cane is a tall crop with a long crop season (typically one year). This fully explains the large omission error. By merging the cultivated area map, this error could be reduced by 30% only, because the cultivated area product, as defined here, exclusively considers the crop planted at the start of the rainy season. The same explanation is valid for the class crop. In essence, in order to almost completely remove this omission error, an annual (and not just seasonal) ENVISAT ASAR monitoring should be carried out. Con‐ cerning the class urban (mainly small cabins in the rural areas), the omission error has been reduced by 40%, leading to a 10% error. In this specific case, the radar backscatter is often random, therefore the combination of the two frequencies strongly contributes to better detect this land cover type.

**Figure 17.** (left) Forest area generated from multi-year ALOS PALSAR-1 FBD data; (right) Cultivated area generated

From an operational perspective, the grouping of sample points in clusters is encouraged. Although this approach may introduce some degree of statistical bias, by significantly reducing travelling time between sample locations, it leaves more resources available for data collection in the field. The relevant parameters of this systematic cluster approach are: 1) distance between clusters; 2) number of points per cluster; 3) distance of points within cluster. The values can be fixed considering several criteria and constraints: 1) available budget; 2) size and shape of the area of interest; 3) resolution of the remote sensing images; 4) logistics; 5) average dimension of the area to be classified. In this case, it has been opted for 1km distance between the clusters, 16 points per cluster, and 250m distance between the points within the

Table 2 shows the obtained confusion matrices for the forest product exclusively based on the multi-year ALOS PALSAR-1 FBD (top) and for the forest product generated by combining the forest and cultivated area product (bottom). In general, the obtained accuracies are high. The

from seasonal ENVISAT ASAR data.

88 Land Applications of Radar Remote Sensing

cluster. A total of 868 valid points have been collected.

With respect to the cultivated area, the obtained overall accuracy is 80%. For details refer to Holecz et al. [36, 40]. In summary, in that work, it was recognized that the limiting factor for cultivated area estimation in small plot agriculture in Africa is the spatial resolution. A possible way to overcome this limitation is on the synergetic use of sensors with different spatial resolutions and characteristics, therefore by optimizing the spatial and temporal resolution in a way that both dynamics are taken into account (Cf. *Estimation of cultivated areas using multitemporal SAR data*).

**Table 2.** Confusion matrices forest area product – (top) Multi-year ALOS PALSAR-1 FBD; (bottom) Multi-year ALOS PALSAR-1 FBD and seasonal ENVISAT ASAR.

### **Acknowledgements**

We are grateful to the Japan Aerospace Exploration Agency, the European Space Agency, NASA, and the Italian Space Agency (ASI) for the provision of the ALOS PALSAR-1, ENVISAT ASAR, and Cosmo-SkyMed data. Parts of the work included in this article have been carried out within the JAXA Kyoto and Carbon initiative and the ESA Global Monitoring for Forest Security project. Support is acknowledged from the Gordon and Betty Moore Foundation, The David and Lucile Packard Foundation, and the Google.org Foundation.

### **Author details**

Josef Kellndorfer1\*, Oliver Cartus1 , Jesse Bishop1 , Wayne Walker1 and Francesco Holecz2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Woods Hole Research Center, MA, USA

2 Sarmap, Purasca, Switzerland

### **References**

**Table 2.** Confusion matrices forest area product – (top) Multi-year ALOS PALSAR-1 FBD; (bottom) Multi-year ALOS

We are grateful to the Japan Aerospace Exploration Agency, the European Space Agency, NASA, and the Italian Space Agency (ASI) for the provision of the ALOS PALSAR-1, ENVISAT ASAR, and Cosmo-SkyMed data. Parts of the work included in this article have been carried out within the JAXA Kyoto and Carbon initiative and the ESA Global Monitoring for Forest Security project. Support is acknowledged from the Gordon and Betty Moore Foundation, The

David and Lucile Packard Foundation, and the Google.org Foundation.

, Jesse Bishop1

, Wayne Walker1

and Francesco Holecz2

PALSAR-1 FBD and seasonal ENVISAT ASAR.

90 Land Applications of Radar Remote Sensing

**Acknowledgements**

**Author details**

Josef Kellndorfer1\*, Oliver Cartus1

2 Sarmap, Purasca, Switzerland

\*Address all correspondence to<EMAIL_ADDRESS>
1 Woods Hole Research Center, MA, USA

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[15] Cartus O., Santoro M., Kellndorfer J., Mapping Forest Aboveground Biomass in the Northeastern United States with ALOS PALSAR Dual-Polarization L-Band, Remote

[16] Moghaddam M., Dungan J., Acker S., Forest variable estimation from fusion of SAR and multispectral optical data, IEEE Transactions on Geoscience and Remote Sensing

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92 Land Applications of Radar Remote Sensing

1978.

### **Chapter 3**

### **Estimation of Cultivated Areas Using Multi-Temporal SAR Data**

Nada Milisavljević, Francesco Collivignarelli and Francesco Holecz

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58282

### **1. Introduction**

[37] Holecz F., Collivignarelli F., Barbieri M., Estimation of cultivated area in small plot agriculture in Africa for food security purposes, ESA Living Planet Symposium,

[38] Holecz F., Barbieri M., Eyre C., Mönnig N., Forest Management – Mapping, monitor‐ ing, and inference of biophysical parameters using ALOS PALSAR and Cosmo-

[39] Mitchell A.L, Williams M., Tapley I., Milne A.K., Interoperability of multi-frequency SAR data for forest information extraction in support of national MRV systems, Geo‐

[40] Holecz F., Barbieri M., Global Monitoring for Food Security, Malawi Service Valida‐

SkyMed data, JAXA Kyoto and Carbon Initiative, Tokyo, 2010.

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94 Land Applications of Radar Remote Sensing

Estimation of cultivated areas in small plot agriculture is an important issue for food security purposes in Africa. One way to obtain this information is through classical field surveys and aerial photography, which are both time and resource consuming. Multi-temporal high resolution Synthetic Aperture Radar (SAR) systems, as sources of reliable and overall infor‐ mation [1], [2], [3], [4], are an alternative solution, satisfying also the demand of continuous monitoring. Namely, for food security purposes, large scale agricultural products are request‐ ed at different times throughout the rain-fed crop season. Concerning the cultivated area, typically, a first product is required after the fields preparation; a second one prior to the harvesting time. In this respect, it is worth mentioning that i) the cultivated area product at start of crop season – today not available in food security services – is an excellent indicator to quantify the overall situation of the upcoming rain-fed crop season; ii) SAR systems – on the contrary to optical sensors – are suitable to map these areas due to their sensitivity to the soil roughness, a typical characteristic of the fields at this stage.

Based on these considerations, a three-step approach for estimation of cultivated area in small plot agriculture in Malawi is envisaged and presented in this chapter. The first step of this approach is the estimation of crop extent prior to the crop season. The estimation of the potential area at start of the crop season is the second step, while the third step consists in determining the crop growth extent during the rain-fed crop season. Taking into account that various vegetation types grow during the rainy season, the key issue is to know what is really cultivated and not simply vegetated. The final result is crucial when dealing with food security and agriculture in developing countries, where available land-cover map is either inaccurate,

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

out of date or it does not even exist. Once derived, this global information, which should give a basis for deciding where to perform more detailed analysis, should be relevant for a longer period of time in normal situations, so it should not need to be updated annually.

As shown in [5], [6], [7], [8], radar-only approaches possess an important operational advant‐ age w.r.t. optical-only or optical-radar approaches, especially in cloud-prone regions, due to their all-weather working ability, high spatial resolution and sensitivity to biomass and moisture. In addition, the penetration depth into crop canopy depends on SAR frequency (the longer the wavelength, the longer the penetration depth) so using multi-frequency data (L-, X- and C-bands) in function of the phenological stages of crops should bring improvements in the estimation of cultivated areas [9], [10]. Finally, since the growth periods of different crops are not equal, in order to differentiate cultivated areas in small-plot agriculture from other areas with different plants and crops that are out of our interest, multi-temporal data are needed.

The main goal of the work presented here on deriving the potential crop extent prior to the start of the rain-fed season is to provide a first information layer regarding the extent of the bare soil area where crop will potentially grow. Multi-temporal L-band SAR data having resolution in the order of 15 m should be sufficient to extract this type of information. In a next step, where potential cultivated area at start of season should be looked for, a very high resolution sensor is needed. The acquisition coverage of this very high resolution sensor (such as X-band Cosmo-SkyMed with the resolution of 3 m) can be limited thanks to the output of the first step. In the final step, monitoring of the crop growth, multi-temporal ASAR (C-band, 15 m resolution) are used, starting from the period before December (in order to have the reference bare soil) as well as covering the period from December to April (the completion of crop season). Finally, several voting strategies are tested for the combination of the outputs of each of the three sensors.

The key aspect of the proposed approach is the generation of three independent and comple‐ mentary products – each one with a clear meaning within agriculture and food security – derived from different spaceborne SAR sensors, which in turn are fused, by yielding the cultivated area product. Moreover, it is also intended to demonstrate the usefulness of SAR data for the targeted application. Note that in this context, the meaning of cultivated area is the effective cropped land (i.e., cultivated and not fellow land) during the rain-fed season. Commercial and irrigated fields, typically cultivated during the dry season, are out of our interest.

The chapter is organized as follows. In the following section, we briefly describe the food security situation in Malawi. Then we present the three steps of our method, one by one. For each of them, we describe the data used, the procedure, we show the obtained results and their validation using ground-truth information. After that, we present the final combination by describing our fusion methods, obtained results and their validation. Finally, we derive some conclusions.

### **2. Food security situation in Malawi**

out of date or it does not even exist. Once derived, this global information, which should give a basis for deciding where to perform more detailed analysis, should be relevant for a longer

As shown in [5], [6], [7], [8], radar-only approaches possess an important operational advant‐ age w.r.t. optical-only or optical-radar approaches, especially in cloud-prone regions, due to their all-weather working ability, high spatial resolution and sensitivity to biomass and moisture. In addition, the penetration depth into crop canopy depends on SAR frequency (the longer the wavelength, the longer the penetration depth) so using multi-frequency data (L-, X- and C-bands) in function of the phenological stages of crops should bring improvements in the estimation of cultivated areas [9], [10]. Finally, since the growth periods of different crops are not equal, in order to differentiate cultivated areas in small-plot agriculture from other areas with different plants and crops that are out of our interest, multi-temporal data are

The main goal of the work presented here on deriving the potential crop extent prior to the start of the rain-fed season is to provide a first information layer regarding the extent of the bare soil area where crop will potentially grow. Multi-temporal L-band SAR data having resolution in the order of 15 m should be sufficient to extract this type of information. In a next step, where potential cultivated area at start of season should be looked for, a very high resolution sensor is needed. The acquisition coverage of this very high resolution sensor (such as X-band Cosmo-SkyMed with the resolution of 3 m) can be limited thanks to the output of the first step. In the final step, monitoring of the crop growth, multi-temporal ASAR (C-band, 15 m resolution) are used, starting from the period before December (in order to have the reference bare soil) as well as covering the period from December to April (the completion of crop season). Finally, several voting strategies are tested for the combination of the outputs of

The key aspect of the proposed approach is the generation of three independent and comple‐ mentary products – each one with a clear meaning within agriculture and food security – derived from different spaceborne SAR sensors, which in turn are fused, by yielding the cultivated area product. Moreover, it is also intended to demonstrate the usefulness of SAR data for the targeted application. Note that in this context, the meaning of cultivated area is the effective cropped land (i.e., cultivated and not fellow land) during the rain-fed season. Commercial and irrigated fields, typically cultivated during the dry season, are out of our

The chapter is organized as follows. In the following section, we briefly describe the food security situation in Malawi. Then we present the three steps of our method, one by one. For each of them, we describe the data used, the procedure, we show the obtained results and their validation using ground-truth information. After that, we present the final combination by describing our fusion methods, obtained results and their validation. Finally, we derive some

period of time in normal situations, so it should not need to be updated annually.

needed.

interest.

conclusions.

each of the three sensors.

96 Land Applications of Radar Remote Sensing

According to [11], [12], [13], [14], [15], Malawi is one of the poorest and least developed countries in Sub-Saharan Africa and a huge challenge facing Malawian agriculture is produc‐ ing more food for a growing population. Many rain-fed smallholder farmers in the country have been shifting to farming systems that are increasing food crop yields and household food security. Urban poverty is increasing in Malawi as well and a pragmatic solution is seen in urban agriculture (i.e., "food production conducted in or around urban regions" [16]). Nevertheless, it is important that the amount available to small plot agriculture, especially in urban conditions, is extended with an understanding of local environmental conditions and that a careful assessment of cultivated areas in small plot agriculture is performed. As a result, the real extent of the food insecurity conditions can be estimated in order to facilitate an adequate humanitarian response if necessary and diminish the country's vulnerability to hunger.

**Figure 1.** Seasonal calendar and critical events timeline in Malawi [17]

In Figure 1, the seasonal calendar for a typical year and critical events timeline in Malawi are shown. This country consists in three agro-ecological zones: 1) high altitude areas (more than 1300 m above sea level, cool temperatures, agricultural areas are rain-fed, with wheat and beans as main crops), 2) low altitude areas (less than 600 m above sea level, irrigated, rice, maize and beans are main crops) and 3) medium altitude areas (600-1300 m above sea level). In this chapter, the region around Lilongwe is analyzed. It belongs to medium altitude areas of Malawi that comprise about 60% of the total cultivated surface of the country. These areas are characterized by moderate temperature and a fairly long rainy season (December to February/March) and their major agriculture practice is maize. The crop calendar for maize in these areas is given in Figure 2.

**Figure 2.** Generic maize crop calendar for medium altitude areas of Malawi [18]

### **3. First step**

### **3.1. Multi-year L-band data**

The aim of this step is to define the potential cultivable extent of the region prior to the start of the rain-fed season, in order to: 1) provide a first layer of information with the potential crop extent in bare soil conditions, i.e. the bare soil area where crop will potentially grow; 2) limit the acquisition coverage of the very high resolution sensor. The generation of this product is relevant when the available land cover map does not exist, is not updated, is inaccurate or the spatial scale is not appropriate (i.e. small scale), which are typical problems when dealing with food security and agriculture in developing countries.

Multi-temporal L-band SAR data having resolution in the order of 15 m should be sufficient to extract this type of information. Namely, in dry conditions, L-band HH/HV data have a potential of distinguishing between bare soil and other land cover classes (sparse to strong vegetation, forest, settlement, bush, wetlands, water).

Therefore, in order to estimate crop extent, multi-annual ALOS PALSAR-1 [19] acquired during the dry season are chosen, since we are interested in an average bare soil area, and not in small changes. The archive of these data, acquired by the Japanese Space Agency (JAXA), is consistent and can be processed in a multi-temporal way, which enables the speckle reduction and allows the generation of a more accurate map (based on a multi-temporal classifier) than the one obtained using a single date, because not relevant temporal variations are filtered out. Optionally, ENVISAT ASAR AP, Landsat TM-5 or SPOT-4/5 data can be used; however, in general, the latter options are not suitable, since less performing for the targeted product during the selected period. Concerning the failure of ALOS PALSAR-1 system in March 2011, it should be noticed that it does not represent a major problem, because i) the available archived data is sufficient for the generation of this intermediate product; ii) these archived data can be used even in the years to come, since the crop pattern are usually not rapidly changing; and iii) the launch of several L-band sensors is planned in the next 1-2 years (PALSAR-2, SAOCOM-1/2).

Taking into account that the dry season prior to the start of the rainy season in Malawi is from April/May to October (Figure 2), the multi-annual PALSAR data that cover that period of the year are selected.

### **3.2. Procedure**

**Figure 2.** Generic maize crop calendar for medium altitude areas of Malawi [18]

food security and agriculture in developing countries.

vegetation, forest, settlement, bush, wetlands, water).

The aim of this step is to define the potential cultivable extent of the region prior to the start of the rain-fed season, in order to: 1) provide a first layer of information with the potential crop extent in bare soil conditions, i.e. the bare soil area where crop will potentially grow; 2) limit the acquisition coverage of the very high resolution sensor. The generation of this product is relevant when the available land cover map does not exist, is not updated, is inaccurate or the spatial scale is not appropriate (i.e. small scale), which are typical problems when dealing with

Multi-temporal L-band SAR data having resolution in the order of 15 m should be sufficient to extract this type of information. Namely, in dry conditions, L-band HH/HV data have a potential of distinguishing between bare soil and other land cover classes (sparse to strong

Therefore, in order to estimate crop extent, multi-annual ALOS PALSAR-1 [19] acquired during the dry season are chosen, since we are interested in an average bare soil area, and not in small changes. The archive of these data, acquired by the Japanese Space Agency (JAXA), is consistent and can be processed in a multi-temporal way, which enables the speckle reduction and allows the generation of a more accurate map (based on a multi-temporal classifier) than the one obtained using a single date, because not relevant temporal variations are filtered out. Optionally, ENVISAT ASAR AP, Landsat TM-5 or SPOT-4/5 data can be used; however, in general, the latter options are not suitable, since less performing for the targeted product during the selected period. Concerning the failure of ALOS PALSAR-1 system in March 2011, it should be noticed that it does not represent a major problem, because i) the available archived data is sufficient for the generation of this intermediate product; ii) these archived data can be used even in the years to come, since the crop pattern are usually not

**3. First step**

**3.1. Multi-year L-band data**

98 Land Applications of Radar Remote Sensing

As proposed in [20], preprocessed multi-temporal PALSAR data in HH and HV polarization are the input data for estimating the crop extent. The preprocessing phase consists in: multilooking, orbital correction, co-registration, multi-temporal speckle filtering, terrain geocoding, radiometric calibration and normalization, and anisotropic non-linear diffusion filtering. Such a preprocessing allows us to work at pixel level.

It is possible to classify these preprocessed multi-temporal SAR data using various approaches. Taking into account that our goal is to develop an approach that could be reused in other regions, where we might not have any reliable information about the existing land-cover classes, we opt for an unsupervised classification method. This means that we can either perform classification on each image separately and then combine the classification results or analyze the multi-temporal signatures of pixels or regions and perform classification based on the similarity of signatures. We choose the former in order to cover situations where we have only a few multi-temporal images or where the data are not radiometrically calibrated. The key issue at this step is to determine which pixels change in time (as a potential bare soil) and which pixels remains the same (so they can be used as a mask that covers the regions that are not interesting for further steps of our three-step method). Therefore, to each image of the multi-temporal data set, we need to apply such an unsupervised classification algorithm that preserves the grayscale information so that the classes from one image can be compared with the classes of another image and that the decision whether the class changed or not is mean‐ ingful. Based on this, our final choice is an algorithm using the Principal Components Trans‐ form (PCT) and median cut [21]. This algorithm looks for the most discriminative information based on which it divides the image into a preset number of classes, taking into account the colour (or grayscale) values.

At this step of the global approach, we have to make sure that none of the pixels that might change in time is excluded from further steps (i.e., masked as stable), while misclassifying stable pixels as changing ones is not critical here and will be further corrected in the two following steps. Due to that, we proceed in the following way. Once all the pixels of each image are classified into n classes, labeled 1, 2, …, n, they are compared and as soon as a pixel changes its label, it is marked as 0, i.e., a potential bare soil. Only if the pixel preserves its class label in all images, that is its final label too.

Taking into account that we have both HH and HV data sets, the above procedure is applied to each of the sets and the two outputs are analyzed using ground-truth information. In a final phase of the first step, these two outputs are combined and the result is compared with the

**Figure 3.** An example of the PALSAR HV image classification result

ground-truth as well, in order to verify the usefulness of fusing the HH/HV information at this level.

#### **3.3. Results and validation**

The test site is a relatively flat region of Malawi, around its capital, Lilongwe. We have ten ALOS PALSAR FBD (Fine Beam Double Polarisation) intensity data (so, HH and HV image pairs from ten different dates) acquired from 2007 to 2010 (from April to October each year, Figure 2). Based on the information from field, we classify each image in eight classes. (Note that we have repeated the whole procedure for ten and for twelve classes, and there was no significant change in the final result.) An example of the classified image is shown in Figure 3. The result of comparing the classification results for the HV data set is given in Figure 4, and for the HH data set, it is presented in Figure 5. Label 0 is in gray in all the images.

**Figure 4.** Comparison of the PALSAR HV classification results

ground-truth as well, in order to verify the usefulness of fusing the HH/HV information at this

The test site is a relatively flat region of Malawi, around its capital, Lilongwe. We have ten ALOS PALSAR FBD (Fine Beam Double Polarisation) intensity data (so, HH and HV image pairs from ten different dates) acquired from 2007 to 2010 (from April to October each year, Figure 2). Based on the information from field, we classify each image in eight classes. (Note that we have repeated the whole procedure for ten and for twelve classes, and there was no significant change in the final result.) An example of the classified image is shown in Figure 3. The result of comparing the classification results for the HV data set is given in Figure 4, and

for the HH data set, it is presented in Figure 5. Label 0 is in gray in all the images.

level.

**3.3. Results and validation**

100 Land Applications of Radar Remote Sensing

**Figure 3.** An example of the PALSAR HV image classification result

Finally, a combination of the results of the HV and HH data sets is shown in Figure 6. This combination is based on the idea that if the HH and HV results label differently the pixel, its neighborhood is analyzed in HH and HV results and of the two labels, the one that is more present is chosen. If HH and HV results label equally the pixel, that label is preserved in the combined result.

As a validation, we use the ground-truth information consisting of 422 points that correspond to regions that do not change in time (buildings, water …) and 330 points that belong to arable land. For HV result (Figure 4), 202 points that do not change in time are correctly classified (having label other than 0), while 330 points that change in time are correctly labeled as 0. In the HH case (Figure 5), 91 points that belong to regions that do not change in time are well classified, and 320 points are correctly classified in case of arable land. Finally, the combination result (Figure 6) classified well 242 points that do not change in time and all 330 points of arable land.

The final output of this step, i.e. the resulting PALSAR mask, used in further steps, is given in Figure 7. It is obtained simply by changing the color of the gray pixels from Figure 6 into white (representing potential bare soil) and assigning black colour to all other classes/colours of Figure 6 (since they are out of interest, thus masked).

**Figure 5.** Comparison of the HH classification results

**Figure 6.** Combination of the HH and HV results

### **4. Second step**

result (Figure 6) classified well 242 points that do not change in time and all 330 points of arable

The final output of this step, i.e. the resulting PALSAR mask, used in further steps, is given in Figure 7. It is obtained simply by changing the color of the gray pixels from Figure 6 into white (representing potential bare soil) and assigning black colour to all other classes/colours of

Figure 6 (since they are out of interest, thus masked).

**Figure 5.** Comparison of the HH classification results

land.

102 Land Applications of Radar Remote Sensing

#### **4.1. One-day interferometric X-band data**

The aim of this step is to define the potential cultivated area, in particular to delineate the ensemble of fields, where crops will later grow.

**Figure 7.** PALSAR mask: white – potential bare soil, black – masked.

X-band radars have the least penetration depth of the three bands used in this work, which also makes them more vulnerable to atmospheric effects. The COSMO-SkyMed (CSK) X-band SAR system is capable of acquiring data twice a day, at a high spatial resolution, which allows for very short-term analysis such as one-day correlation. Taking into account its high sensi‐ tivity and poor penetration capabilities, this system provides excellent means for analyzing short-term (lack of) changes at the very start of the crop season, when the crops are only being planted. Thus, potential cultivated area at start of season is derived from one-day interfero‐ metric CSK pair (3 m) acquired during the field preparation period.

In this respect, it is worth mentioning that this step does not have to be performed on a yearly base, if the area and the pattern of the fields remain stable: it should be exclusively updated when changes occur.

### **4.2. Procedure**

X-band radars have the least penetration depth of the three bands used in this work, which also makes them more vulnerable to atmospheric effects. The COSMO-SkyMed (CSK) X-band SAR system is capable of acquiring data twice a day, at a high spatial resolution, which allows for very short-term analysis such as one-day correlation. Taking into account its high sensi‐ tivity and poor penetration capabilities, this system provides excellent means for analyzing short-term (lack of) changes at the very start of the crop season, when the crops are only being planted. Thus, potential cultivated area at start of season is derived from one-day interfero‐

In this respect, it is worth mentioning that this step does not have to be performed on a yearly base, if the area and the pattern of the fields remain stable: it should be exclusively updated

metric CSK pair (3 m) acquired during the field preparation period.

**Figure 7.** PALSAR mask: white – potential bare soil, black – masked.

104 Land Applications of Radar Remote Sensing

when changes occur.

Potential cultivated area at start of season is derived from one-day interferometric CSK pair acquired during the field preparation period (December, Figure 2). Very high resolution optical data would not be useful at this stage since fields are not yet covered by vegetation, so it would be very difficult to discriminate them from the surrounding bare soil area using optical sensors. On the contrary, because of the rough nature and dry conditions of the fields, short wavelength SAR data acquired in an interferometric mode (1-day interval) provide useful information at this second step.

This step should provide information on the status of the fields at the beginning of the crop season in terms of vegetated or bare soil condition. Since the purpose of the overall cultivated area product here is to map the effective crop growth from the start to the end of the crop season, only those fields with bare soil conditions are considered. This means, in terms of interferometric X-band data, that these areas are defined by a medium to high coherence (absence of human activities) and a medium to high backscattering coefficient (rough bare soil).

**Table 1** Combination of two CSK amplitude images taken with one-day interval.

Taking into account that we have one one-day interferometric data set and two corresponding amplitude images, we should first combine the two amplitude images into one. Using the PCT and median cut algorithm mentioned in Subsection 3.2, we split each of the two amplitude images into three classes (L - low, M - medium and H - high amplitude). We combine them as given in Table 1, where: L – low, M – medium, H – high, C – (significant) change, ML - medium low, MH – medium high.

**Table 2** Combination of the combined CSK amplitude image and the corresponding coherence image.

Note that the situations in which there is a strong change in the backscattering coefficient in such a short time most possibly refer to on-going works (therefore, they are most possibly not among the areas of interest).

**Figure 8.** CSK amplitude image taken on December 9, 2010

Once the combined amplitude image is obtained, we combine this information with the corresponding coherence image, split in three classes (L, M and H) using the PCT and median cut algorithm (Subsection 3.2). The combination is based on the idea that, at this period of the year that we consider, neither low coherence nor low amplitude correspond to potential cultivated area. Thus, we combine these two data sets as indicated in Table 2, where: O – out of interest, M1 – medium 1 (one amplitude image has a low value, the other and the coherence have medium values), M2 – medium 2 (one amplitude value is low, the other is medium, coherence value is high), M3 – medium 3 (all images have medium values), M4 – medium 4 (two out of these three values are medium and one is high), H1 – high 1 (two out of these three values are high and one is medium), H2 – high 2 (all images have high values).

**Figure 9.** Result of combining the classification results of the two amplitude images from Figure 8 according to Table 1, where: L – black, C – blue, ML – green, M – yellow, MH – red, H – white.

In such a way, the order O-M1-M2-M3-M4-H1-H2 corresponds to the increasing probability that the pixel belongs to a cultivated area (bare soil).

### **4.3. Results and validation**

Note that the situations in which there is a strong change in the backscattering coefficient in such a short time most possibly refer to on-going works (therefore, they are most possibly not

Once the combined amplitude image is obtained, we combine this information with the corresponding coherence image, split in three classes (L, M and H) using the PCT and median cut algorithm (Subsection 3.2). The combination is based on the idea that, at this period of the year that we consider, neither low coherence nor low amplitude correspond to potential cultivated area. Thus, we combine these two data sets as indicated in Table 2, where: O – out of interest, M1 – medium 1 (one amplitude image has a low value, the other and the coherence have medium values), M2 – medium 2 (one amplitude value is low, the other is medium, coherence value is high), M3 – medium 3 (all images have medium values), M4 – medium 4

among the areas of interest).

106 Land Applications of Radar Remote Sensing

**Figure 8.** CSK amplitude image taken on December 9, 2010

Two CSK amplitude images of Lilongwe, taken on December 9 and 10, 2010 are used (the one of December 9 is shown in Figure 8, as an illustration). After splitting each of the two images in three classes (L, M and H) and combining them as indicated in Table 1, we obtain the result presented in Figure 9. Once the coherence image is split in three classes (L, M and H) and combined with the combination of the two amplitude images following the rules of Table 2, we get the image in Figure 10.

**Figure 10.** Result of combining the combined amplitude images from Figure 9 with the coherence image according to Table 2, where: O – black, M1 – blue, M2 – green, M3 – yellow, M4 - orange, H1 – red, H2 – white.

As far as validation is concerned, we use the same ground-truth information as described in Subsection 3.3. However, the question here is how to treat different degrees of possibil‐ ity of belonging to cultivated areas, taking into account the variety of output values (O-M1-M2-M3-M4-H1-H2), i.e. where to put the threshold level between not cultivated and cultivated. If we decide to assign everything that is not O to cultivated areas, we obtain 293 (out of 330) correct classifications of bare soil and 184 (out of 422) correct classifica‐ tions of the rest. If we assign O and M1 to not-cultivated areas, then we get 289 (out of 330) correct classifications of bare soil and 212 (out of 422) correct classifications for notcultivated areas. Finally, if we assign O, M1, M2 and M3 to not-cultivated areas, we obtain 280 (out of 330) correct classifications of bare soil and 294 (out of 422) correct classifica‐ tions for not-cultivated areas. In other words, as we move the threshold, the number of correct classifications for cultivated areas slowly decreases while the number of correct classifications for not-cultivated areas significantly increases.

combined with the combination of the two amplitude images following the rules of Table 2,

**Figure 10.** Result of combining the combined amplitude images from Figure 9 with the coherence image according to

As far as validation is concerned, we use the same ground-truth information as described in Subsection 3.3. However, the question here is how to treat different degrees of possibil‐ ity of belonging to cultivated areas, taking into account the variety of output values (O-M1-M2-M3-M4-H1-H2), i.e. where to put the threshold level between not cultivated and cultivated. If we decide to assign everything that is not O to cultivated areas, we obtain 293 (out of 330) correct classifications of bare soil and 184 (out of 422) correct classifica‐ tions of the rest. If we assign O and M1 to not-cultivated areas, then we get 289 (out of 330) correct classifications of bare soil and 212 (out of 422) correct classifications for notcultivated areas. Finally, if we assign O, M1, M2 and M3 to not-cultivated areas, we obtain

Table 2, where: O – black, M1 – blue, M2 – green, M3 – yellow, M4 - orange, H1 – red, H2 – white.

we get the image in Figure 10.

108 Land Applications of Radar Remote Sensing

**Figure 11.** CSK mask: white – potentially cultivated, black – masked

The final output of our approach is the combination of PALSAR (that performs very good for bare soil and moderately good for the rest, as shown in Subsection 3.3), CSK and ASAR. Thus, we should keep the threshold level for CSK in such a way that the classification of notcultivated areas is the best possible (thus, between M3 and M4) and pay attention during the final combination so that complementarities of the three sensors are exploited in a best possible way. Figure 11 contains the image from Figure 10 where LO, M1, M2 and M3 are labeled as black (masked) and M4, H1 and H2 as white (potentially cultivated).

### **5. Third step**

### **5.1. Seasonal C-band data**

At this step, crop growth extent is derived from multi-temporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) data [22] acquired during the crop season. Optionally, Radarsat-1/2 FB can be used at this step. Optical data such as Landsat TM-5, SPOT-4/5, Ikonos, and QuickBird, can also provide information of the crop growth. But, due to the persistent cloud coverage during this period, their use is very difficult, even more if large areas, such as national coverage, are targeted. Ikonos, and QuickBird, can also provide information of the crop growth. But, due to the

Multi-temporal ENVISAT ASAR (15 m) are C-band data proven to be useful in monitoring agricultural activities on a regular basis, i.e. seasonal land cover changes [1], [23], [24], [25]. Generally speaking, C-band SAR data are not hindered by atmospheric effects; their penetra‐ tion capability with respect to vegetation canopies is restricted to the top layers. persistent cloud coverage during this period, their use is very difficult, even more if large areas, such as national coverage, are targeted.  Procedure** Generally speaking, C-band SAR data are not hindered by atmospheric effects; their

The goal of the third step is to monitor the crop growth during the crop season, which explains the necessity of having multi-temporal data acquired regularly all along the crop season. In such a way, the confusion between cropped and the surrounding vegetated, non-crop, areas should be reduced, and at the same time, the crop development could be monitored. penetration capability with respect to vegetation canopies is restricted to the top layers. **5.2. Procedure**

Figure 12.An illustration of the typical behavior of the C-band maize signature (intensity in function of **Figure 12.** An illustration of the typical behavior of the C-band maize signature (intensity in function of time) from the beginning to the end of the season.

time) from the beginning to the end of the season. The goal of the third step is to monitor the crop growth during the crop season, which explains the necessity of having multi-temporal data acquired regularly all along the crop season. In such a way, the confusion between cropped and the surrounding vegetated, noncrop, areas should be reduced, and at the same time, the crop development could be monitored. Our procedure for analyzing the ASAR data is as follows. Firstly, we perform an unsupervised classification based on multi-temporal signatures (so, grouping together the pixels having a similar multi-temporal behavior) in a preset number of classes. As a result, we have an output image where the pixels with similar signature have the same label. As this is an unsupervised classification, we are unable to determine which of these signatures is similar to the one of maize. Thus, in the following level of our analysis, we introduce our knowledge regarding the maize signature at C-band (note that, depending on the area, season and the type of the crop

**5. Third step**

**5.1. Seasonal C-band data**

110 Land Applications of Radar Remote Sensing

coverage, are targeted.

**5.2. Procedure**

**dB**

monitored.

beginning to the end of the season.

**5.2. Procedure**

At this step, crop growth extent is derived from multi-temporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) data [22] acquired during the crop season. Optionally, Radarsat-1/2 FB can be used at this step. Optical data such as Landsat TM-5, SPOT-4/5, Ikonos, and QuickBird, can also provide information of the crop growth. But, due to the persistent cloud coverage during this period, their use is very difficult, even more if large areas, such as national

Multi-temporal ENVISAT ASAR (15 m) are C-band data proven to be useful in monitoring agricultural activities on a regular basis, i.e. seasonal land cover changes [1], [23], [24], [25]. Generally speaking, C-band SAR data are not hindered by atmospheric effects; their penetra‐

Ikonos, and QuickBird, can also provide information of the crop growth. But, due to the persistent cloud coverage during this period, their use is very difficult, even more if large

Multi-temporal ENVISAT ASAR (15 m) are C-band data proven to be useful in monitoring agricultural activities on a regular basis, i.e. seasonal land cover changes [1], [23], [24], [25]. Generally speaking, C-band SAR data are not hindered by atmospheric effects; their penetration capability with respect to vegetation canopies is restricted to the top layers.

Figure 12.An illustration of the typical behavior of the C-band maize signature (intensity in function of

The goal of the third step is to monitor the crop growth during the crop season, which explains the necessity of having multi-temporal data acquired regularly all along the crop season. In such a way, the confusion between cropped and the surrounding vegetated, noncrop, areas should be reduced, and at the same time, the crop development could be

The goal of the third step is to monitor the crop growth during the crop season, which explains the necessity of having multi-temporal data acquired regularly all along the crop season. In such a way, the confusion between cropped and the surrounding vegetated, non-crop, areas

**time**

**Figure 12.** An illustration of the typical behavior of the C-band maize signature (intensity in function of time) from the

Our procedure for analyzing the ASAR data is as follows. Firstly, we perform an unsupervised classification based on multi-temporal signatures (so, grouping together the pixels having a similar multi-temporal behavior) in a preset number of classes. As a result, we have an output image where the pixels with similar signature have the same label. As this is an unsupervised classification, we are unable to determine which of these signatures is similar to the one of maize. Thus, in the following level of our analysis, we introduce our knowledge regarding the maize signature at C-band (note that, depending on the area, season and the type of the crop

should be reduced, and at the same time, the crop development could be monitored.

tion capability with respect to vegetation canopies is restricted to the top layers.

areas, such as national coverage, are targeted.

time) from the beginning to the end of the season.

**Figure 13.** Result of an unsupervised classification based on multi-temporal pixel behavior in eleven ASAR images

we want to distinguish, this step can be easily modified for other applications): at the very beginning of the season, the intensity of the signature is low, and then it grows, reaching rapidly its maximum value (which corresponds to ploughing), and then dropping to its first minimum (sowing). Then the second phase begins, i.e. from flowering to plant drying stage, where the intensity of the signature raises, reaches another maximum, possibly drops a bit and raises again, in function of the plant moisture and the surface scattering at the top of the plant. This behavior is similar to the one illustrated in Figure 12. Since the later stages can vary from season to season, the key indicators we use to select which of the signatures from the unsu‐ pervised classification output resemble to the one of maize are the starting raise of the signature, its sudden drop followed by its next raise. Thus, we analyze the moments when the first maximum, the first minimum and the second maximum occur and label the class(es) with the corresponding tendencies as the one(s) of maize.

**Figure 14.** Grouping of the classes from Figure 13: white – crop growing extent, black – the rest (masked)

#### **5.3. Results and validation**

Once a multi-temporal unsupervised classification of multi-temporal pixel signatures in twelve classes is performed, using eleven ASAR intensity images covering the period from September 2010 to March 2011, we obtain the result given in Figure 13. After analyzing the multi-temporal signatures of each of twelve classes, we select the classes having tendencies similar to the ones of maize (Figure 12), and we mask the rest. The obtained result is given in Figure 14. Note that the classification has been also performed in eight and ten classes and that there was no significant change in the final result.

The validation results are as follows: 303 (out of 330) pixels from cultivated areas are well classified, as well as 235 (out of 422) pixels from non-cultivated areas.

### **6. Final combination**

### **6.1. Procedure**

There are various methods of combining the results obtained from each of the three sensors, depending on the quality of each of the sensors, requested computation speed, application, etc. In our case, looking for a simple, fast yet reliable method, we test several voting combi‐ nation rules:

Note that the difference between ANDc and ANDm is that in the former case, AND voting is applied to the cultivated class while in the latter case, AND voting is applied to the masked class. This makes the two AND voting approaches complementary and it depends on the application which one is more useful than the other. Finally, the difference between MAJ and OR is that the former labels a pixel as potentially cultivated if at least two of the three sensors have given it that label and it is masked otherwise, while the latter labels a pixel as potentially cultivated if any of the three sensors labeled it as such.

#### **6.2. Results and validation**

**Figure 14.** Grouping of the classes from Figure 13: white – crop growing extent, black – the rest (masked)

Once a multi-temporal unsupervised classification of multi-temporal pixel signatures in twelve classes is performed, using eleven ASAR intensity images covering the period from September 2010 to March 2011, we obtain the result given in Figure 13. After analyzing the multi-temporal signatures of each of twelve classes, we select the classes having tendencies similar to the ones of maize (Figure 12), and we mask the rest. The obtained result is given in Figure 14. Note that the classification has been also performed in eight and ten classes and that

The validation results are as follows: 303 (out of 330) pixels from cultivated areas are well

**5.3. Results and validation**

112 Land Applications of Radar Remote Sensing

there was no significant change in the final result.

classified, as well as 235 (out of 422) pixels from non-cultivated areas.

The four voting strategies are applied to the outputs of the three sensors given in Figures 7, 11 and 14. As an illustration, Figure 15 contains the result of ANDc voting, while the result of MAJ voting is shown in Figure 16.

We validate these results using the same validation set as in the previous steps and obtain the following results:

**•** OR voting: all 330 pixels from cultivated fields well classified, as well as 59 out of 422 noncultivated field pixels.

**Figure 15.** Result of ANDc voting: white – crop growing extent, black – masked.

These validation results are in accordance with our expectations. Namely, if we keep as cultivated only those pixels where all three sensors claim that it is cultivated indeed, and label all the rest as masked (non-cultivated), we can expect to have a high detection of noncultivated fields and only modest results for cultivated fields (ANDc voting). With the inverse logic, we preserve all cultivated fields, but also label many non-cultivated as being cultivated (ANDm voting, as well as OR). Finally, with the majority voting (MAJ), it can be expected to obtain results that optimize the two extremes, and benefit from the complementarities of the sensors.

**Figure 16.** Result of MAJ voting: white – crop growing extent, black – masked.

**•** OR voting: all 330 pixels from cultivated fields well classified, as well as 59 out of 422 non-

**Figure 15.** Result of ANDc voting: white – crop growing extent, black – masked.

complementarities of the sensors.

These validation results are in accordance with our expectations. Namely, if we keep as cultivated only those pixels where all three sensors claim that it is cultivated indeed, and label all the rest as masked (non-cultivated), we can expect to have a high detection of noncultivated fields and only modest results for cultivated fields (ANDc voting). With the inverse logic, we preserve all cultivated fields, but also label many non-cultivated as being cultivated (ANDm voting, as well as OR). Finally, with the majority voting (MAJ), it can be expected to obtain results that optimize the two extremes, and benefit from the

cultivated field pixels.

114 Land Applications of Radar Remote Sensing

The same conclusion can be derived from Table 3, which represents the summary of the validation results, for each of the steps and each of the combination approaches tested. From the three steps, step 1 has the best correct classification of pixels belonging to cultivated fields, but 42.64% of pixels belonging to non-cultivated fields are also classified as cultivated. On the other hand, step 2 has the best correct classification of pixels from non-cultivated fields, but 15.15% of cultivated field pixels are also classified as non-cultivated. Regarding the four fusion strategies used, we can conclude that with the MAJ voting, we preserve the maximum number of correct classifications as the one of PALSAR in step 1, while we increase the correct classi‐ fication of non-cultivated fields as well, thanks to the output of CSK and ASAR. Although the priority is not to miss cultivated fields, an improvement in correct classification of noncultivated fields is also an important issue since it leads to a better estimation of the extent of the problem.

**Table 3** Correct classifications: summary of the validation results (in percents)

Note that the validation is performed on the pixel level. If done on the region level, the validation results would certainly have been even better since a region would have been declared as correctly classified if majority of its pixels (and not all of them, as here) were correctly classified.

The achieved accuracy confirms the validity of the methodology, in particular that: 1) the use of very high resolution data is an indispensable condition for the identification of small agricultural plots; 2) the differentiation between cultivated areas (i.e. growing vegetation during the crop season) and other land cover classes is first and foremost possible if multitemporal data are used; 3) the combination of various SAR sensors (bands) improves the final results.

### **7. Conclusion**

Agriculture is the land cover type showing the largest spatial and temporal dynamics during a relatively short period. Therefore, pre-requisite for the generation of an accurate cultivated area product is to combine very high resolution data with multi-temporal high resolution data acquired throughout the whole crop season. This approach has been tested through a threestep procedure for estimation of cultivated area in small plot agriculture in Malawi and the obtained results have proven its validity.

The first step of this procedure is the estimation of crop extent prior to the crop season using multi-temporal L-band PALSAR data. The estimation of the potential area at start of the crop season using X-band COSMO-SkyMed is the second step, while the third step consists in determining the crop growth extent during the rain-fed crop season, with the help of multitemporal C-band ASAR data. The final result is crucial when dealing with food security and agriculture in developing countries, where available land-cover map is either inaccurate, out of date or it does not even exist. Once derived, this global information, which should give a basis for deciding where to perform more detailed analysis, should be relevant for a longer period of time in normal situations, so it should not need to be updated annually.

At each step, the obtained results are validated using ground-truth information.

Four voting combination strategies are tested in the final combination of the three sensors, based on "majority", "or" and "and" logic (two versions of it - one prioritizing cultivated, and the other non-cultivated fields). For our application, the majority voting gives the most interesting results of the four, while for some other applications (such as mined area reduction, for example), one of the other strategies might be useful.

As demonstrated here, the spatial resolution of existing space-borne remote sensing systems and the wise integration of different remote sensing sources enable the achievement of a high level of detail and accuracy, as long as the data are understood, processed and used in the right way. The proposed solution is attractive, less time consuming and less expensive compared to area regression estimators exclusively based on field survey. Furthermore, the remote sensing solution intrinsically provides a monitoring component (as agricultural area can vary during a season): this is often (or fully) not taken into account in the area regression estimator approach, simply because it is too time consuming to frequently repeat the field survey.

In our future work, other combination approaches will be tested, in order to optimize the exploitation of the complementarities of the three SAR sensors.

### **Acknowledgements**

**cultivated fields well classified (out of 330)**

**Table 3** Correct classifications: summary of the validation results (in percents)

correctly classified.

116 Land Applications of Radar Remote Sensing

**7. Conclusion**

obtained results have proven its validity.

results.

step 1 100% 57.35% step 2 84.85% 69.67% step 3 91.82% 55.69% final ANDc 68.18% 89.34% final ANDm 100% 18.48% final MAJ 100% 76.54% final OR 100% 13.98%

Note that the validation is performed on the pixel level. If done on the region level, the validation results would certainly have been even better since a region would have been declared as correctly classified if majority of its pixels (and not all of them, as here) were

The achieved accuracy confirms the validity of the methodology, in particular that: 1) the use of very high resolution data is an indispensable condition for the identification of small agricultural plots; 2) the differentiation between cultivated areas (i.e. growing vegetation during the crop season) and other land cover classes is first and foremost possible if multitemporal data are used; 3) the combination of various SAR sensors (bands) improves the final

Agriculture is the land cover type showing the largest spatial and temporal dynamics during a relatively short period. Therefore, pre-requisite for the generation of an accurate cultivated area product is to combine very high resolution data with multi-temporal high resolution data acquired throughout the whole crop season. This approach has been tested through a threestep procedure for estimation of cultivated area in small plot agriculture in Malawi and the

The first step of this procedure is the estimation of crop extent prior to the crop season using multi-temporal L-band PALSAR data. The estimation of the potential area at start of the crop season using X-band COSMO-SkyMed is the second step, while the third step consists in determining the crop growth extent during the rain-fed crop season, with the help of multitemporal C-band ASAR data. The final result is crucial when dealing with food security and agriculture in developing countries, where available land-cover map is either inaccurate, out of date or it does not even exist. Once derived, this global information, which should give a

**non-cropped fields well classified (out of 422)**

> The European, Japanese, and Italian Space Agency are acknowledged for the provision of the ENVISAT ASAR, ALOS PALSAR-1, and Cosmo-SkyMed data. EFTAS, C-ITA, and the Ministry of Agriculture and Food Security of Malawi are acknowledged for the collection and provision of the ground survey data in Malawi.

> This work has been done in the scope of the SARLAT project launched by the Belgian Ministry of Defense.

### **Author details**

Nada Milisavljević1 , Francesco Collivignarelli2 and Francesco Holecz2

1 Department of Communication, Information Systems & Sensors (CISS), Royal Military Academy, Brussels, Belgium

2 Sarmap, Purasca, Switzerland

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## **Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh**

Andrew Nelson, Mirco Boschetti, Giacinto Manfron, Franceco Holecz, Francesco Collivignarelli, Luca Gatti, Massimo Barbieri, Lorena Villano, Parvesh Chandna and Tri Setiyono

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57443

### **1. Introduction**

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China, 2008.

120 Land Applications of Radar Remote Sensing

1090-1101, 2011.

Operational monitoring of agricultural extent and production is part of several national and international efforts to provide transparent, rapid and accurate information related to food security and food markets. Initiatives such as the G20 Agricultural Market Information System (AMIS), the European Commission's Monitoring Agricultural Resources mission (MARS), the Global Agricultural Monitoring (GEOGLAM) component of GEO, and the United States Department of Agriculture's Global Agricultural Monitoring Foreign Agricultural Service (GLAMFAS), are just some examples of operational services that do, or will require remotesensing–based information on crop status in almost any part of the world.

Of the thousands of edible plants, just three—rice, wheat, and maize —provide 60% of the global population's food energy intake, and the top 15 crops amount to 90% [1]. Seasonal or monthly estimates of production and availability of these staples form a part of many agri‐ cultural bulletins and agricultural outlook reports. These reports are used for decision-making and policies on imports, exports, subsidies, and investments, which, in turn, affect prices. Food security is fundamentally about availability and price; such reports, and the responses to these reports, affect both. International events, such as the food price crisis of 2008, are the unin‐ tended outcome of national and international policy decisions that can detrimentally affect

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

millions of people. More accurate and timely information to better inform policymakers is one way to reduce the likelihood of similar events in the future.

Crop bulletins and other regular assessments of crop area and production are a combination of statistics, surveys, reports from agricultural offices, Met Office forecasts, expert opinion, and remote-sensing information on vegetation, soil moisture, and rainfall. However, there is scope to increase the role of remote sensing in these reports if the information can be provided at the appropriate time, scale, and, wherever possible, with a focus on specific crops. Highly anticipated programmes such as the European Space Agency's (ESA) Sentinel constellation can provide exactly this type of information, although the demands for sustainably acquiring, processing, validating, and delivering the information are considerable.

This chapter provides a proof of concept for how a key staple crop can be monitored on a national scale using existing remote-sensing (RS) products that will soon be complemented and superseded by forthcoming sensors. Our exemplar crop is rice and our test country is Bangladesh (Figure 1). This region is highly suitable for a demonstration because of the triple remote-sensing challenge of pervasive cloud cover, small field size, and complex cropping patterns, which are typical of the vast and important agricultural areas of Asia, Africa, and Latin America. It is these areas where future gains in productivity must and will be made, not the agricultural areas of Europe, the US, and other developed regions where crop monitoring is substantially easier.

We first briefly describe the rice environments of Bangladesh, and then demonstrate how a combination of hypertemporal synthetic-aperture radar (SAR) and optical RS data can be combined to generate both baseline map information and near–real-time monitoring infor‐ mation on crop extent and crop seasonality.

### **2. Rice in Bangladesh**

Agriculture is one of the most important sectors in Bangladesh's economy, accounting for approximately 20% of the gross domestic product (GDP) in 2010. It also accounts for the employment of over 60% of the country's population [2]. Rice is far and away the most important crop in the agricultural sector. Almost 60% of the land area is planted to rice and it provides food, employment, and income for much of the rural population. Bangladesh is one of the most populous countries in Asia, with almost 150 million people, according to the 2011 census. With over 1,000 people per square kilometre, it is one of the most densely populated countries in the world [2]. The country's milled rice consumption per capita is the highest in the world at 173 kg per year or almost 500 g of uncooked rice per day [1].

Rice can be cultivated in any of three seasons in Bangladesh in a myriad of cropping systems such as rice/pulse, rice/maize, rice/wheat, rice/vegetables, rice/shrimp and double-or triplerice monoculture. The dry *boro* season, which runs from November to April, is largely dependent on irrigation and is of growing importance to Bangladesh's rice output. This is followed by the *aus* season (sometimes referred to as early *kharif*), which runs from March to Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for… http://dx.doi.org/10.5772/57443 123

**Figure 1.** Location of Bangladesh in Asia.

millions of people. More accurate and timely information to better inform policymakers is one

Crop bulletins and other regular assessments of crop area and production are a combination of statistics, surveys, reports from agricultural offices, Met Office forecasts, expert opinion, and remote-sensing information on vegetation, soil moisture, and rainfall. However, there is scope to increase the role of remote sensing in these reports if the information can be provided at the appropriate time, scale, and, wherever possible, with a focus on specific crops. Highly anticipated programmes such as the European Space Agency's (ESA) Sentinel constellation can provide exactly this type of information, although the demands for sustainably acquiring,