Image-driven empirical correction procedures have been suggested [57, 84, 85] for use with the Hyperspectral Imager for the Coastal Ocean (HICO), airborne visible/infrared imaging spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (CASI-2), and Hyperion [86]. The empirical correction approach is based on the facts that clear ocean waters have water-leaving reflectance above 800 nm close to zero and sunglint and cirrus reflectance in the 400–1000 nm region. In this dissertation, we use the empirical line approach, which is an atmospheric correction method that serves as an alternative to radiative transfer modeling approaches [87]. This method calculates the empirical relation between radiance and reflectance using a dark and a bright target, well-characterized by field and image spectra. Our targets were measured in the area during data collection for optimal representation. This method has been applied to correct both land and ocean data [88] and has shown great success with both coarser spatial resolution satellite sensor data and airborne data approaches [87]. This technique is only suitable for regional data correction where reflectance properties of bright and dark targets such as sand and water over uniform areas are measured coincidentally with the aircraft or airborne

A minimum of two known materials is required to use this method to carry out the calibration, and selecting one bright object and one dark object is also crucial for this exercise. This calibration method is recommended to use on two targets; however, using more targets will better estimate the relationship between target reflectance and at-sensor radiance [87, 88]. Using the image and field spectra, the two targets are regressed linearly against the reflectance spectra measured on the

*ARCHER color composite (RGB of 726 nm, 668 nm, and 551 nm) for areas around Edinburg, the NorthFork of Shenandoah River in Virginia, USA. Image location: 38°49′55. 32″N 78°33'1. 53″W, North Edinburg, NF.*

(DNs) to a substantial value to correct atmospheric instrument effect.

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

skylight).

overpass [89].

*Processing and Analysis of Hyperspectral Data*

spectroscopic data.

the water column [73, 80]. Using the simple methods like the empirical method, optically, shallow water can result in an overestimation of water column constituents caused by high reflectance values primarily from the bottom reflectance [81]. Comparing empirical and analytical approaches, it can be noted that analytical and semi-analytical methods are preferred for subsequent reasons: (1) they can be used to estimate both optically profound and shallow water optical properties, and the bottoms of optically shallow waters with physics-based modeling; (2) the approach does not require in situ water quality measurements to model, resulting in its independence; and (3) analytical and semi-analytical methods can be applied regionally in multiple lakes, reservoirs, and rivers with varied circumstances. Notwithstanding these benefits, nonetheless, they are computationally intensive and more expensive and difficult to use, thus requiring information of the inherent optical properties of the water body [73]. This research relies on the analytical approach to analyzing

**2.2 Use of hyperspectral remote sensing methods and standard water quality**

Although the standard methods provide accurate measurement for a point in time and space, spatial or temporal view of water quality required for precise assessment of large water systems is usually not available [72]. It is necessary to integrate the use of calibrated image data with field spectral measurements to solve this problem, so as entirely to deploy the spatial and spectral information of hyperspectral remote sensing data. Hyperspectral images are critical for the water quality assessments where field data collection is planned to coincide with flight overpasses followed by the retrieval of the apparent and inherent optical properties

An in situ sampling water quality survey for nutrients is necessary at multiple sites in the study area, using the EPA-approved quality control/quality assurance procedures. A sample collected procedure is required, and we recommend 15 to 20 samples from each sampling area separated by at least 100 m from each other; using handheld spectrometer and paying particular attention to just the deep portions of the river for sample collection, above surface water reflectance was also measured. In situ data for chlorophyll a and other nutrients of interest can also be obtained from water quality databases, which contain data for fixed monitoring stations

Using the handheld spectrometer to measure all the relevant quantities from above the surface, three types of measurements were carried out at each sampling site with the spectrometer: total upwelling radiance (LT), downwelling sky radiance (LSky), and "gray-card" radiance (LG, 3) reflected from a diffuse reflector (Spectralon®) [71]. All measurements were carried out at about 2:30 pm (local time), under clear

0.5 m above the water surface using a canoe. The above-water reflectance needs to be measured at 40° from the nadir and 90° from the azimuth and the sky reflectance measured in the same plane as the water, except for the angle from the zenith, which was 40°. To determine the downwelling irradiance, the Spectralon is assumed to have a Lambertian reflector in which, Ed = πLG/R, where LG is the average of the four grayscale scans and RG the reflectance of the diffuse reflector (~10%) [71].

For the quantitative assessments of water quality parameters, detectable from hyperspectral data, data preprocessing is required by performing robust corrections

, and very calm water, at roughly

**approach in measuring the water quality parameters**

of the basin or watershed of interest.

throughout the watershed of interest.

**2.3 Hyperspectral image processing**

skies, minor cloud cover, a wind speed of 4 m s−1

**112**

for atmospheric effects of adjacency effects and those effects occurring at the water surface level (sunglint, specular reflection of direct irradiance, and diffuse skylight).

Hyperspectral imagery requires an atmospheric correction to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects such as water vapor and other trace gasses. In an atmospheric correction, the radiance values are transformed into reflectance data to obtain water reflectance by removing surface reflectance [82], measuring the fraction of radiation reflected from the surface [83]. This procedure is particularly important for quantitative image analysis or change detection using hyperspectral data; image calibration is essential for remote sensing (**Figure 1**) to convert the instrument's digital numbers (DNs) to a substantial value to correct atmospheric instrument effect.

Image-driven empirical correction procedures have been suggested [57, 84, 85] for use with the Hyperspectral Imager for the Coastal Ocean (HICO), airborne visible/infrared imaging spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (CASI-2), and Hyperion [86]. The empirical correction approach is based on the facts that clear ocean waters have water-leaving reflectance above 800 nm close to zero and sunglint and cirrus reflectance in the 400–1000 nm region. In this dissertation, we use the empirical line approach, which is an atmospheric correction method that serves as an alternative to radiative transfer modeling approaches [87]. This method calculates the empirical relation between radiance and reflectance using a dark and a bright target, well-characterized by field and image spectra. Our targets were measured in the area during data collection for optimal representation.

This method has been applied to correct both land and ocean data [88] and has shown great success with both coarser spatial resolution satellite sensor data and airborne data approaches [87]. This technique is only suitable for regional data correction where reflectance properties of bright and dark targets such as sand and water over uniform areas are measured coincidentally with the aircraft or airborne overpass [89].

A minimum of two known materials is required to use this method to carry out the calibration, and selecting one bright object and one dark object is also crucial for this exercise. This calibration method is recommended to use on two targets; however, using more targets will better estimate the relationship between target reflectance and at-sensor radiance [87, 88]. Using the image and field spectra, the two targets are regressed linearly against the reflectance spectra measured on the

### **Figure 1.**

*ARCHER color composite (RGB of 726 nm, 668 nm, and 551 nm) for areas around Edinburg, the NorthFork of Shenandoah River in Virginia, USA. Image location: 38°49′55. 32″N 78°33'1. 53″W, North Edinburg, NF.*

field to derive the gain and offset coefficients [88]. Once the gains and offsets are obtained, they are then applied to the entire image to derive surface reflectance, by producing reflectance values that are comparable to field measured values [88] (**Figures 1** and **2**). The empirical line method uses the following equation to calculate the gains and offsets:

Reflectance (field spectrum) = gain\* radiance (input data) + offset.

Remote sensing data is also impeded by the effect of wave-induced sun glint [90], and this has become a limiting factor in estimating water quality efficiently from airborne data with high accuracy. The environmental and atmospheric effects resulting in inaccuracies in remote sensing classification results remain a growing concern in remote sensing classification [91]. For an adequate estimation of water quality with remote sensing data that is void of inaccuracies, the sun glint needs to be examined. After performing atmospheric correction on our image, sunglint removal was required to correct atmospheric effects on the visible wavelength region (0.45–0.69 μm). The sunglint is the specular reflection of sunlight directly transmitted from the air-water interfaces [92]. Under clear skies and irregular water surface, specular reflectance can result in sun glint on the image, which reduces the accuracy of retrievals [93]. The sunglint often occurs on an image when the orientation of the water surface is directly reflected toward the sensor as a function of the position of the sun, the viewing angle, and the state of the water surface [92].

These circumstances have resulted to the more excellent specular reflection of light from water "than the water-leaving radiance from the sub-surface features." The necessity to remove the sun glint contribution for better image classification or information retrieval has been recognized by several researchers [90]. The approach adopted for this research estimates the amount of glint in the image by using data from the near-infrared (NIR), with the assumption that water-leaving radiance is negligible in this part of the spectrum, and any NIR signal left after atmospheric correction is undoubtedly from the sunglint. A relationship is established between the NIR and glint radiance while using the spectrum of the deep-water part of the image [92]. We use the shallow water sunglint removal approach that assumes that all the radiance from the NIR reaching the sensor is from atmospheric scattering and surface reflection, and any signal at the NIR after atmospheric correction is sunglint [92].

## **Figure 2.**

*Atmospherically corrected ARCHER using empirical line calibration approach with a color composite of RGB 726 nm, 668 nm, and 551 nm for areas around Edinburg (above), for North Fork of the Shenandoah River taken on July 12, 2014. Image location: 38°49′55. 32″N 78°33'1. 53″W, North Edinburg, NF.*

**115**

**Figure 3.**

*atmosphere.*

*Use of Hyperspectral Remote Sensing to Estimate Water Quality*

**3. Sunglint background and removal approach**

There are five critical processes through which a remote sensing detector

Several approaches have been proposed for glint correction for estimating the contribution of glint to the " the sensor reaching radiance, and then subtract it from the received signal" [92]. Hochberg et al. [94] proposed a sunglint removal method, which assumed that the NIR brightness is only made up of sunglint and a spatially constant ambient NIR component. This method also believes that the sunglint present in the visible band is linearly related to the brightness of the NIR band. However, all two assumptions were proven weak because the first assumption models a constant ambient NIR brightness, which is removed from all pixels during analysis, and secondly, only two pixels are used to establish a linear relationship assumption. Selecting only one bright and one dark pixel could result to a bright pixel chosen from the land, which necessitated masking for results from this method to be efficient, and, this makes it very difficult and time-consuming. Thus, the difficulty of being able to identify an appropriate bright pixel can result in significant errors, which undermine the effectiveness of the method proposed by

Hedley et al. [95], after acknowledging how sensitive this approach was to outlier pixel, proposed a revised method in which glint intensity is obtained using several pixels rather than two to establish a linear relationship between regression between the NIR and visible bands to allow sunglint contribution removal [90]. Hedley et al. [93] proposes using single or several regions on the image where sunglint is evident with consistent spectral brightness. The linear regression uses NIR brightness (x-axis) against the visible band's intensity (**Figure 4**) of all the

As recommended by Hedley et al. [93], the first step is to select the minimum NIR brightness NIR Min deep-water pixels having a variety of glint intensities from which a sample is calculated. The next step in deglinting the image is to use each visible spectrum (VIS) Band i and perform a linear regression on the NIR pixel

*Diagram showing routes by which light can arrive at a remote sensing detector from Kay et al. (2005).*

 *(A) Molecules or aerosols scattering in the atmosphere, which is either single or multiple. (B) Surface-water scattering from the atmosphere followed by reflection to the detector—known as*

*"sky glint." (C) Whitecaps reflections from the sea surface. (D) Surface-water specular reflection directly transmitted from the sun to the atmosphere to the surface and from the surface to the detector—also called "sun glint." (E) Atmosphere and air-water interface transmission, which is followed by reflection or scattering below the water surface and transfer back to the detector through the*

receives radiance reaching it, as shown in **Figure 3** from Kay et al. [92].

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

Hochberg et al. [94].

selected pixels.

*Processing and Analysis of Hyperspectral Data*

calculate the gains and offsets:

field to derive the gain and offset coefficients [88]. Once the gains and offsets are obtained, they are then applied to the entire image to derive surface reflectance, by producing reflectance values that are comparable to field measured values [88] (**Figures 1** and **2**). The empirical line method uses the following equation to

Remote sensing data is also impeded by the effect of wave-induced sun glint [90], and this has become a limiting factor in estimating water quality efficiently from airborne data with high accuracy. The environmental and atmospheric effects resulting in inaccuracies in remote sensing classification results remain a growing concern in remote sensing classification [91]. For an adequate estimation of water quality with remote sensing data that is void of inaccuracies, the sun glint needs to be examined. After performing atmospheric correction on our image, sunglint removal was required to correct atmospheric effects on the visible wavelength region (0.45–0.69 μm). The sunglint is the specular reflection of sunlight directly transmitted from the air-water interfaces [92]. Under clear skies and irregular water surface, specular reflectance can result in sun glint on the image, which reduces the accuracy of retrievals [93]. The sunglint often occurs on an image when the orientation of the water surface is directly reflected toward the sensor as a function of the position of the sun, the viewing angle, and the state of the water surface [92]. These circumstances have resulted to the more excellent specular reflection of light from water "than the water-leaving radiance from the sub-surface features." The necessity to remove the sun glint contribution for better image classification or information retrieval has been recognized by several researchers [90]. The approach adopted for this research estimates the amount of glint in the image by using data from the near-infrared (NIR), with the assumption that water-leaving radiance is negligible in this part of the spectrum, and any NIR signal left after atmospheric correction is undoubtedly from the sunglint. A relationship is established between the NIR and glint radiance while using the spectrum of the deep-water part of the image [92]. We use the shallow water sunglint removal approach that assumes that all the radiance from the NIR reaching the sensor is from atmospheric scattering and surface reflection, and any signal at the NIR after atmospheric correction is sunglint [92].

*Atmospherically corrected ARCHER using empirical line calibration approach with a color composite of RGB 726 nm, 668 nm, and 551 nm for areas around Edinburg (above), for North Fork of the Shenandoah River*

*taken on July 12, 2014. Image location: 38°49′55. 32″N 78°33'1. 53″W, North Edinburg, NF.*

Reflectance (field spectrum) = gain\* radiance (input data) + offset.

**114**

**Figure 2.**

## **3. Sunglint background and removal approach**

There are five critical processes through which a remote sensing detector receives radiance reaching it, as shown in **Figure 3** from Kay et al. [92].

Several approaches have been proposed for glint correction for estimating the contribution of glint to the " the sensor reaching radiance, and then subtract it from the received signal" [92]. Hochberg et al. [94] proposed a sunglint removal method, which assumed that the NIR brightness is only made up of sunglint and a spatially constant ambient NIR component. This method also believes that the sunglint present in the visible band is linearly related to the brightness of the NIR band. However, all two assumptions were proven weak because the first assumption models a constant ambient NIR brightness, which is removed from all pixels during analysis, and secondly, only two pixels are used to establish a linear relationship assumption. Selecting only one bright and one dark pixel could result to a bright pixel chosen from the land, which necessitated masking for results from this method to be efficient, and, this makes it very difficult and time-consuming. Thus, the difficulty of being able to identify an appropriate bright pixel can result in significant errors, which undermine the effectiveness of the method proposed by Hochberg et al. [94].

Hedley et al. [95], after acknowledging how sensitive this approach was to outlier pixel, proposed a revised method in which glint intensity is obtained using several pixels rather than two to establish a linear relationship between regression between the NIR and visible bands to allow sunglint contribution removal [90]. Hedley et al. [93] proposes using single or several regions on the image where sunglint is evident with consistent spectral brightness. The linear regression uses NIR brightness (x-axis) against the visible band's intensity (**Figure 4**) of all the selected pixels.

As recommended by Hedley et al. [93], the first step is to select the minimum NIR brightness NIR Min deep-water pixels having a variety of glint intensities from which a sample is calculated. The next step in deglinting the image is to use each visible spectrum (VIS) Band i and perform a linear regression on the NIR pixel

## **Figure 3.**

*Diagram showing routes by which light can arrive at a remote sensing detector from Kay et al. (2005). (A) Molecules or aerosols scattering in the atmosphere, which is either single or multiple. (B) Surface-water scattering from the atmosphere followed by reflection to the detector—known as "sky glint." (C) Whitecaps reflections from the sea surface. (D) Surface-water specular reflection directly transmitted from the sun to the atmosphere to the surface and from the surface to the detector—also called "sun glint." (E) Atmosphere and air-water interface transmission, which is followed by reflection or scattering below the water surface and transfer back to the detector through the atmosphere.*

**Figure 4.**

*Graphical interpretation of sunglint correction method from Hedley et al. [93].*

brightness R NIR against the pixel value of VIS band R i. A user-based selection process is used to collect the samples, and land or cloud masking is not necessary. The product of slope bi and RNIR minus MinNIR is subtracted from Ri to obtain the pixel Ri with glint removed using the following equation:

$$R\ '\_i \equiv R\_i - b\_i \left( R\_{MR} - \text{Min}\_{MR} \right) \tag{1}$$

where *bi* is the regression slope.

*Ri* is the visible band.

*RNIR* is the NIR pixel value.

*MinNIR* is the ambient NIR value, which is NIR pixel with no sunglint, which is either estimated from the figure above or from the entire image, and it is less prone to outliers caused by nonoptically deep pixels.

The result of the sunglint corrects brightness in band i, by minimizing outlier effects caused by surface objects [92]. This approach can be applied on either before or after atmospheric correction since it works entirely on the relative magnitude of values, and the pixel units are not very necessary for image deglinting. We initially corrected out the image with the empirical line method before removing the sunglint. It should, however, be mentioned that, if there are variations in the atmosphere properties, this will also affect the regression slope, thus making glint effect to be confounded [92]. As outlined by Hedley et al. [93], this approach is attained in four steps:

## **Step-by-step implementation**

**117**

**Author details**

Mbongowo Mbuh

North Dakota, USA

*Use of Hyperspectral Remote Sensing to Estimate Water Quality*

The combination of several hundred spectral bands in a single acquisition has been made feasible by hyperspectral systems, which produce more detailed spectral data. Before advances in hyperspectral remote sensing, the multispectral imagery was the only data source in land and water observational remote sensing from airborne and spacecraft operations since the 1960s [10]. However, multispectral remote sensing data were only collected in three to six spectral bands in a single observation from the visible near-infrared and shortwave infrared regions of the electromagnetic spectrum, making it challenging to examine water quality from this data source. The present chapter covered hyperspectral remote sensing data analysis using field spectrometer data and remote sensing of water quality. Research has shown that remote sensing, GIS, and hydrological models can be integrated to solve hydrological problems [96, 97]. Here we review relevant literature on research in hyperspectral remote sensing that examines water quality parameters like suspended sediments, turbidity, chlorophyll a, and total phosphorus as investigated by numerous researchers. Unique characteristics of hyperspectral remote sensing data are introduced. This chapter shows that field observations/ spectroscopy, and water quality modeling is very instrumental in the accuracy of remote sensing analysis. We also presented the methodology for the study of visible to infrared hyperspectral remote sensing data from ARCHER aircraft and data collected with a handheld field portable spectroradiometer, to retrieve and establish a relationship between water quality parameters like chlorophyll a, colored dissolved organic matter,

turbidity, phosphorus, and nitrogen in the Shenandoah River Basin.

Department of Geography and Geographic Information Science, University of

© 2019 The Author(s). Licensee IntechOpen. 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,

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

**4. Conclusion**

## **4. Conclusion**

*Processing and Analysis of Hyperspectral Data*

brightness R NIR against the pixel value of VIS band R i. A user-based selection process is used to collect the samples, and land or cloud masking is not necessary. The product of slope bi and RNIR minus MinNIR is subtracted from Ri to obtain the

*MinNIR* is the ambient NIR value, which is NIR pixel with no sunglint, which is either estimated from the figure above or from the entire image, and it is less prone

The result of the sunglint corrects brightness in band i, by minimizing outlier effects caused by surface objects [92]. This approach can be applied on either before or after atmospheric correction since it works entirely on the relative magnitude of values, and the pixel units are not very necessary for image deglinting. We initially corrected out the image with the empirical line method before removing the sunglint. It should, however, be mentioned that, if there are variations in the atmosphere properties, this will also affect the regression slope, thus making glint effect to be confounded [92]. As outlined by Hedley et al. [93], this approach is

2.Area of the image displaying a range of sun glint, with a more or less homogeneous surface, is selected. The minimum NIR brightness value is determined.

3.The newly created region of interest is used as a subset to create a new image with only the glare pixel subset and all image bands saved individually in ASCII. A linear regression of NIR brightness (x-axis) against the visible band (y-axis) is performed using the selected pixels in Excel to remove the sunglint from each band. The output of interest from the linear regression analysis for

4.To individually glint each band i or all pixels in the image, the product of bi and NIR brightness of the pixel (minus MinMIN) subtracted the pixel value in

each band is the slope, which is called bi in the equation above.

(*RNIR* - *MinNIR*) (1)

pixel Ri with glint removed using the following equation:

*Graphical interpretation of sunglint correction method from Hedley et al. [93].*

 =*Ri* - *bi*

is the regression slope.

to outliers caused by nonoptically deep pixels.

*R* ′*i*

 is the visible band. *RNIR* is the NIR pixel value.

attained in four steps:

**Step-by-step implementation**

1.Image is radiometrically corrected.

band i as illustrated in Eq. (1).

where *bi*

*Ri*

**Figure 4.**

**116**

The combination of several hundred spectral bands in a single acquisition has been made feasible by hyperspectral systems, which produce more detailed spectral data. Before advances in hyperspectral remote sensing, the multispectral imagery was the only data source in land and water observational remote sensing from airborne and spacecraft operations since the 1960s [10]. However, multispectral remote sensing data were only collected in three to six spectral bands in a single observation from the visible near-infrared and shortwave infrared regions of the electromagnetic spectrum, making it challenging to examine water quality from this data source. The present chapter covered hyperspectral remote sensing data analysis using field spectrometer data and remote sensing of water quality. Research has shown that remote sensing, GIS, and hydrological models can be integrated to solve hydrological problems [96, 97]. Here we review relevant literature on research in hyperspectral remote sensing that examines water quality parameters like suspended sediments, turbidity, chlorophyll a, and total phosphorus as investigated by numerous researchers. Unique characteristics of hyperspectral remote sensing data are introduced. This chapter shows that field observations/ spectroscopy, and water quality modeling is very instrumental in the accuracy of remote sensing analysis. We also presented the methodology for the study of visible to infrared hyperspectral remote sensing data from ARCHER aircraft and data collected with a handheld field portable spectroradiometer, to retrieve and establish a relationship between water quality parameters like chlorophyll a, colored dissolved organic matter, turbidity, phosphorus, and nitrogen in the Shenandoah River Basin.

## **Author details**

Mbongowo Mbuh

Department of Geography and Geographic Information Science, University of North Dakota, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. 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.

## **References**

[1] Mbuh MJ, Houser PR, Heidari A. Water quality estimation using combined water chemistry and field spectroscopy in the Shenandoah River, Virginia. International Journal of Applied Geospatial Research (IJAGR). 2016;**7**(2):14-37. DOI: 10.4018/ ijagr.2016040102

[2] Mcgwire K, Minor T, Fenstermaker L. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sensing of Environment. 2000;**72**:360-374

[3] Hinrichsen D, Tacio H. The Coming Freshwater Crisis is Already Here. Population and Water. 2019. p. 26. Available from: https://www. wilsoncenter.org/sites/default/files/ popwawa2.pdf

[4] UNESCO. Water for a sustainable world. Paris: UNESCO.Unesco 2015a, Water for a sustainable world. pdf. 2015. Available from: https:// sustainabledevelopment.un.org/ content/documents/1711Water%20 for%20a%20Sustainable%20World. pdf

[5] Cook SE, Fisher MJ, Andersson MS, Rubiano J, Giordano M. Water, food and livelihoods in river basins. Water International. 2009;**34**(1):13-29. DOI: 10.1080/02508060802673860

[6] Mustapha A, Aris AZ. Spatial aspects of surface water quality in the Jakara Basin, Nigeria, using chemometric analysis. Journal of Environmental Science and Health, Part A. 2012;**47**(10):1455-1465

[7] Milovanovic M. Water quality assessment and determination of pollution sources along the Axios/ Vardar River, Southeast Europe. Desalination. 2007;**213**:159-173

[8] Verma S. Analysis of Water Quality by Physico-Chemical Parameters in Fateh Sagar Talab in Bagar, Dist. Of Jhunjhunu (Raj.), India; 2015

[9] Sheffield J, Wood EF, Pan M, Beck H, Coccia G, Serrat-Capdevila A, et al. Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions. Water Resources Research. 2018;**54**(12):9724-9758

[10] Landgrebe D. Some fundamentals and methods for hyperspectral image data analysis. In: Systems and Technologies for Clinical Diagnostics and Drug Discovery II. Vol. 3603. Society of Photo-Optical Instrumentation Engineers; 1999. 6 pp

[11] Okin SG, Roberts AD, Murray B, William J, Okin JW. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment. 2001;**77**:212-225

[12] Wang X, Yang W. Water quality monitoring and evaluation using remote-sensing techniques in China: A systematic review. Ecosystem Health and Sustainability. 2019;**5**(1):47-56

[13] Cosgrove WJ, Loucks DP. Water management: Current and future challenges and research directions. Water Resources Research. 2015;**51**(6):4823-4839

[14] Bhateria R, Jain D. Water quality assessment of lake water: A review. Sustainable Water Resources Management. 2016;**2**(2):161-173

[15] David MK, Dennis HR. Nutrients in the Nation's Waters—Too Much of a Good Thing? (Report No. 1136). Vol. 1136. U.S. Geological Survey Circular; 1996. 24p. DOI: 10.3133/cir1136

**119**

2017;**7**(1):17497

*Use of Hyperspectral Remote Sensing to Estimate Water Quality*

ammonia in an oligohaline, subtropical

[24] Guo M, Li J, Sheng C, Xu J, Wu L. A review of wetland remote sensing. Sensors. 2017;**17**(4):777. DOI: 10.3390/

[26] Vos RJ, Hakvoort JHM, Jordans RWJ, Ibelings BW. Multiplatform optical monitoring of eutrophication in temporally and spatially variable lakes. Science of the Total Environment.

[25] Gholizadeh MH, Melesse AM, Reddi L. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Basel, Switzerland). 2016;**16**(8). DOI: 10.3390/s16081298

marsh. Ecological Engineering.

2003;**21**:153-163

2003;**312**:221-243

[27] Gao Y, Gao J, Yin H, Liu C, Xia T, Wang J, et al. Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques. Journal of Environmental

Management. 2015;**151**:33-43

[28] Koponen S, Pulliainen J, Kallio K, Kallikainen M. Lake water quality classification with airborne hyperspectral spectrometer and

of Environment. 2002;**79**:51-59

[29] Lillesand TM, Johnson WL,

[30] Lathrop RG, Lillesand TM. Monitoring water quality and river plume transport in Green Bay, Lake Michigan with SPOT-1 imagery. Photogrammetric Engineering and Remote Sensing. 1989;**55**(3):349-354

simulated MERIS data. Remote Sensing

Deuell RL, Lindstrom OM, Meisner DE. Use of Landsat data to predict the trophic state of Minnesota Lakes. Photogrammetric Engineering and Remote Sensing. 1983;**49**(2):219-229

s17040777

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

[16] Dunca A-M. Water Pollution and Water Quality Assessment of Major Transboundary Rivers from Banat (Romania) [Research Article]. 2018

[17] Fataei E. Assessment of surface water quality using principle component analysis and factor analysis. World Journal of Fish and Marine Sciences.

[18] Meng W, Zhang N, Zhang Y, Zhang BH. Integrated assessment of river health based on water quality, aquatic life, and physical habitat. Journal of Environmental Sciences.

2011;**3**:159-166

2009;**21**:1017-1027

[19] Govender M, Chetty K,

Water SA. 2006;**2007**:33(2)

[20] Mbuh MJ. Optimization of airborne real-time cueing

(ARCHER) imagery, in situ data with chemometrics to evaluate nutrients in the Shenandoah River, Virginia. Geocarto International. 2018;**33**(12):1326-1349. DOI: 10.1080/10106049.2017.1343395

Bulcock H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies.

Hyperspectral enhanced reconnaissance

[21] Stumpf RP. Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms. Human and Ecological Risk Assessment. 2001;**7**:1363-1368

[22] Kang W, Chai H, Xiang Y, Chen W, Shao Z, He Q. Assessment of low concentration wastewater treatment operations with dewatered alum sludgebased sequencing batch constructed wetland system. Scientific Reports.

[23] Tilley DR, Ahmed M, Son JH, Badrinarayanan H. Hyperspectral reflectance of emergent macrophytes as an indicator of water column

*Use of Hyperspectral Remote Sensing to Estimate Water Quality DOI: http://dx.doi.org/10.5772/intechopen.89222*

[16] Dunca A-M. Water Pollution and Water Quality Assessment of Major Transboundary Rivers from Banat (Romania) [Research Article]. 2018

[17] Fataei E. Assessment of surface water quality using principle component analysis and factor analysis. World Journal of Fish and Marine Sciences. 2011;**3**:159-166

[18] Meng W, Zhang N, Zhang Y, Zhang BH. Integrated assessment of river health based on water quality, aquatic life, and physical habitat. Journal of Environmental Sciences. 2009;**21**:1017-1027

[19] Govender M, Chetty K, Bulcock H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA. 2006;**2007**:33(2)

[20] Mbuh MJ. Optimization of airborne real-time cueing Hyperspectral enhanced reconnaissance (ARCHER) imagery, in situ data with chemometrics to evaluate nutrients in the Shenandoah River, Virginia. Geocarto International. 2018;**33**(12):1326-1349. DOI: 10.1080/10106049.2017.1343395

[21] Stumpf RP. Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms. Human and Ecological Risk Assessment. 2001;**7**:1363-1368

[22] Kang W, Chai H, Xiang Y, Chen W, Shao Z, He Q. Assessment of low concentration wastewater treatment operations with dewatered alum sludgebased sequencing batch constructed wetland system. Scientific Reports. 2017;**7**(1):17497

[23] Tilley DR, Ahmed M, Son JH, Badrinarayanan H. Hyperspectral reflectance of emergent macrophytes as an indicator of water column

ammonia in an oligohaline, subtropical marsh. Ecological Engineering. 2003;**21**:153-163

[24] Guo M, Li J, Sheng C, Xu J, Wu L. A review of wetland remote sensing. Sensors. 2017;**17**(4):777. DOI: 10.3390/ s17040777

[25] Gholizadeh MH, Melesse AM, Reddi L. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Basel, Switzerland). 2016;**16**(8). DOI: 10.3390/s16081298

[26] Vos RJ, Hakvoort JHM, Jordans RWJ, Ibelings BW. Multiplatform optical monitoring of eutrophication in temporally and spatially variable lakes. Science of the Total Environment. 2003;**312**:221-243

[27] Gao Y, Gao J, Yin H, Liu C, Xia T, Wang J, et al. Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques. Journal of Environmental Management. 2015;**151**:33-43

[28] Koponen S, Pulliainen J, Kallio K, Kallikainen M. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment. 2002;**79**:51-59

[29] Lillesand TM, Johnson WL, Deuell RL, Lindstrom OM, Meisner DE. Use of Landsat data to predict the trophic state of Minnesota Lakes. Photogrammetric Engineering and Remote Sensing. 1983;**49**(2):219-229

[30] Lathrop RG, Lillesand TM. Monitoring water quality and river plume transport in Green Bay, Lake Michigan with SPOT-1 imagery. Photogrammetric Engineering and Remote Sensing. 1989;**55**(3):349-354

**118**

*Processing and Analysis of Hyperspectral Data*

[1] Mbuh MJ, Houser PR, Heidari A. Water quality estimation using combined water chemistry and field spectroscopy in the Shenandoah River, Virginia. International Journal of Applied Geospatial Research (IJAGR).

[8] Verma S. Analysis of Water Quality by Physico-Chemical Parameters in Fateh Sagar Talab in Bagar, Dist. Of Jhunjhunu (Raj.), India; 2015

[9] Sheffield J, Wood EF, Pan M, Beck H, Coccia G, Serrat-Capdevila A, et al. Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions. Water Resources Research. 2018;**54**(12):9724-9758

[10] Landgrebe D. Some fundamentals and methods for hyperspectral image data analysis. In: Systems and Technologies for Clinical Diagnostics

[11] Okin SG, Roberts AD, Murray B, William J, Okin JW. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment.

[12] Wang X, Yang W. Water quality monitoring and evaluation using remote-sensing techniques in China: A systematic review. Ecosystem Health and Sustainability. 2019;**5**(1):47-56

[13] Cosgrove WJ, Loucks DP. Water management: Current and future challenges and research directions.

[15] David MK, Dennis HR. Nutrients in the Nation's Waters—Too Much of a Good Thing? (Report No. 1136). Vol. 1136. U.S. Geological Survey Circular; 1996. 24p. DOI: 10.3133/cir1136

Water Resources Research. 2015;**51**(6):4823-4839

[14] Bhateria R, Jain D. Water quality assessment of lake water: A review. Sustainable Water Resources Management. 2016;**2**(2):161-173

and Drug Discovery II. Vol. 3603. Society of Photo-Optical Instrumentation Engineers; 1999. 6 pp

2001;**77**:212-225

2016;**7**(2):14-37. DOI: 10.4018/

modeling for quantifying sparse vegetation cover in arid environments. Remote Sensing of Environment.

[3] Hinrichsen D, Tacio H. The Coming Freshwater Crisis is Already Here. Population and Water. 2019. p. 26. Available from: https://www. wilsoncenter.org/sites/default/files/

[4] UNESCO. Water for a sustainable world. Paris: UNESCO.Unesco 2015a, Water for a sustainable world. pdf. 2015. Available from: https:// sustainabledevelopment.un.org/ content/documents/1711Water%20 for%20a%20Sustainable%20World.

[5] Cook SE, Fisher MJ, Andersson MS, Rubiano J, Giordano M. Water, food and livelihoods in river basins. Water International. 2009;**34**(1):13-29. DOI:

[6] Mustapha A, Aris AZ. Spatial aspects of surface water quality in the Jakara Basin, Nigeria, using chemometric analysis. Journal of Environmental Science and Health, Part A. 2012;**47**(10):1455-1465

10.1080/02508060802673860

[7] Milovanovic M. Water quality assessment and determination of pollution sources along the Axios/ Vardar River, Southeast Europe. Desalination. 2007;**213**:159-173

Fenstermaker L. Hyperspectral mixture

[2] Mcgwire K, Minor T,

ijagr.2016040102

**References**

2000;**72**:360-374

popwawa2.pdf

pdf

[31] Harding LW, Itsweire EC, Esaias WE. Algorithm development for recovering chlorophyll concentrations int Chesapeake Bay using aircraft remote sensing 1989-91. Photogrammetric Engineering and Remote Sensing. 1995;**61**:177-185

[32] Zhang Y, Giardino C, Li L. Water optics and water colour remote sensing. Remote Sensing. 2017;**9**(8):818

[33] Devlin MJ, Petus C, Da Silva E, Tracey D, Wolff NH, Waterhouse J, et al. Water quality and river plume monitoring in the great barrier reef: An overview of methods based on ocean colour satellite data. Remote Sensing. 2015;**7**(10):12909-12941

[34] Ruiz-Azuara P. Multitemporal analysis of "simultaneous" Landsat imagery (MSS and TM) for monitoring primary production in a small tropical coastal lagoon. Photogrammetric Engineering & Remote Sensing. 1995;**61**(2):877-198

[35] Bagheri S, Dios RA. Utility of hyperspectral data for bathymetric mapping in a turbid estuary. International Journal of Remote Sensing. 1998;**19**(6):1179-1188

[36] Brezonik P, Menken KD, Bauer M. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolvedorganic matter (CDOM). Lake and Reservoir Management. 2005;**21**(4):373-382

[37] Goodenough A. Evaluating water quality monitoring with hyperspectral imagery [Senior Research Thesis]. Rochester Institute of Technology: Center for Imaging Science; 2001

[38] Shafique NA, Fulk F, Autrey BC, Flotemersch J. Hyperspectral remote sensing of water quality parameters for large rivers in the Ohio River Basin. In: Proceedings of the 1st Interagency Conference on Research in the Watersheds. Benson, AZ, USA; 27-30 October 2003

[39] Usali N, Ismail MH. Use of remote sensing and GIS in monitoring water quality. Journal of Sustainable Development. 2010;**3**(3):228. DOI: 10.5539/jsd.v3n3p228

[40] Yang S, Ding P, Zhu J, Zhao Q, Mao Z. Tidal flat morphodynamic processes of the Yangtze estuary and their engineering implications. China Ocean Engineering. 2000;**14**:307-320

[41] Gokul EA, Shanmugam P. An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India: Detecting algal blooms. Journal of Geophysical Research, Oceans. 2016;**121**(6):4097-4127

[42] Mcilwaine B, Casado MR, Leinster P. Using 1st derivative reflectance signatures within a remote sensing framework to identify macroalgae in marine environments. Remote Sensing. 2019;**11**(6):704

[43] Hakvoorth H, deHaah J, Jordan R, Vos R, Rijkeboer MP. Towards airborne remote sensing of water quality in the Netherlands-validation and error analysis. Journal of Photogrammetry and Remote Sensing. 2002;**57**:171-183

[44] Gao B-C, Li R-R. FVI—A floating vegetation index formed with three near-ir channels in the 1.0-1.24 μm spectral range for the detection of vegetation floating over water surfaces. Remote Sensing. 2018;**10**(9):1421

[45] Shafique NA, Autrey BC, Fulk F, Cormier SM. Hyperspectral narrow wavebands selection for optimizing water quality monitoring on the great Miami River, Ohio. Journal of Spatial Hydrology. 2001;**1**(1):1-22

**121**

*Use of Hyperspectral Remote Sensing to Estimate Water Quality*

[53] Fell F., Fischer J, Schaale M, Schroeder T. Retrieval of chlorophyll

measurements in the spectral range of sun-induced chlorophyll fluorescence.

Moline MA, Matthew O, Schofield OM. Continuous hyperspectral absorption measurements of colored dissolved

systems. Journal of Applied Optics.

[55] Rochelle-Newall EJ, Fisher TR, Fan C, Glibert PM. Dynamics of chromophoric dissolved organic matter and dissolved organic carbon in experimental mesocosm. International

[56] Rochelle-Newall EJ, Fisher TR. Chromophoric dissolved organic matter and dissolved organic carbon in Chesapeake Bay. Marine Chemistry.

[57] Brando V, Dekker A. Satellite hyperspectral remote sensing estimatingestuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing.

[58] Kneubühler M, Gemperli C, Schläpfer D, Zah R, Itten K. Determination of water quality parameters in Indian ponds using remote sensing methods. In:

Zagajewski B, Sobczak M, Wrzesień M, editors. Proceedings of 4th EARSeL Workshop on Imaging Spectroscopy. New Quality in Environmental Studies. Zurich Open Repository and Archive (ZORA), University of Zurich; 2005.

concentration from MERIS

2003. DOI: 10.1117/12.467267

[54] Kirkpatrick GJ, Orrico C,

organic material in aquatic

Journal of Remote Sensing.

1999;**20**(3):627-641

2002;**77**:23-41

2003;**41**(6):1378-1381

DOI: 10.5167/uzh-97035

[59] Dingtian Y, Delu P, Xiaoyu Z, Xiaofeng Z, Xianqiang H, Shujing L. Retrieval of chlorophyll a and

2003;**42**(33):6564

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

[46] Helder D, Markham B, Morfitt R, Storey J, Barsi J, Gascon F, et al. Observations and recommendations for the calibration of Landsat 8 OLI and sentinel 2 MSI for improved data interoperability. Remote Sensing.

[47] Ortega S, Fabelo H, Iakovidis DK, Koulaouzidis A, Callico GM. Use of Hyperspectral/multispectral imaging in gastroenterology. Shedding some– different–light into the dark. Journal of

[48] Jensen D, Simard M, Cavanaugh K, Sheng Y, Fichot CG, Pavelsky T, et al. Improving the transferability of suspended solid estimation in wetland and deltaic waters with an empirical hyperspectral approach. Remote Sensing. 2019;**11**(13):1629

[49] Xi H, Hieronymi M, Röttgers R, Krasemann H, Qiu Z. Hyperspectral differentiation of phytoplankton taxonomic groups: A comparison between using remote sensing reflectance and absorption spectra. Remote Sensing. 2015;**7**(11):14781-14805

[50] Nelson JR, Guarda S. Particulate and dissolved spectral absorption on the continental shelf of the southeastern United States. Journal of Geophysical

DeGranpre MD, Peltzer ET, Nelson RK. Seasonal variations of CDOM and DOC in the middle Atlantic bight: Terrestrial inputs and photooxidation.

Research. 1995;**100**:8715-8732

Limnology and Oceanography.

[52] Carder KL, Hawes SK, Baker KA, Smith RC, Steward RG, Mitchell BG. Reflectance model for quantifying chlorophyll-a in the presence of productivity degradation products. Journal of Geophysical Research.

1997;**42**:674-686

1991;**96**:20599-20611

[51] Vodacek A, Blough NV,

Clinical Medicine. 2019;**8**(1):36

2018;**10**(9):1340

## *Use of Hyperspectral Remote Sensing to Estimate Water Quality DOI: http://dx.doi.org/10.5772/intechopen.89222*

[46] Helder D, Markham B, Morfitt R, Storey J, Barsi J, Gascon F, et al. Observations and recommendations for the calibration of Landsat 8 OLI and sentinel 2 MSI for improved data interoperability. Remote Sensing. 2018;**10**(9):1340

*Processing and Analysis of Hyperspectral Data*

In: Proceedings of the 1st Interagency Conference on Research in the Watersheds. Benson, AZ, USA; 27-30

remote sensing and GIS in monitoring water quality. Journal of Sustainable Development. 2010;**3**(3):228. DOI:

[40] Yang S, Ding P, Zhu J, Zhao Q, Mao Z. Tidal flat morphodynamic processes of the Yangtze estuary and their engineering implications. China Ocean Engineering. 2000;**14**:307-320

[41] Gokul EA, Shanmugam P. An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India: Detecting algal blooms. Journal of Geophysical Research, Oceans.

2016;**121**(6):4097-4127

[42] Mcilwaine B, Casado MR, Leinster P. Using 1st derivative reflectance signatures within a remote sensing framework to identify macroalgae in marine environments. Remote Sensing. 2019;**11**(6):704

[43] Hakvoorth H, deHaah J, Jordan R, Vos R, Rijkeboer MP. Towards airborne remote sensing of water quality in the Netherlands-validation and error analysis. Journal of Photogrammetry and Remote Sensing. 2002;**57**:171-183

[44] Gao B-C, Li R-R. FVI—A floating vegetation index formed with three near-ir channels in the 1.0-1.24 μm spectral range for the detection of vegetation floating over water surfaces. Remote Sensing. 2018;**10**(9):1421

[45] Shafique NA, Autrey BC, Fulk F, Cormier SM. Hyperspectral narrow wavebands selection for optimizing water quality monitoring on the great Miami River, Ohio. Journal of Spatial

Hydrology. 2001;**1**(1):1-22

[39] Usali N, Ismail MH. Use of

October 2003

10.5539/jsd.v3n3p228

[31] Harding LW, Itsweire EC, Esaias WE. Algorithm development

concentrations int Chesapeake Bay using aircraft remote sensing 1989-91. Photogrammetric Engineering and Remote Sensing. 1995;**61**:177-185

[32] Zhang Y, Giardino C, Li L. Water optics and water colour remote sensing.

[33] Devlin MJ, Petus C, Da Silva E, Tracey D, Wolff NH, Waterhouse J, et al. Water quality and river plume monitoring in the great barrier reef: An overview of methods based on ocean colour satellite data. Remote Sensing.

[34] Ruiz-Azuara P. Multitemporal analysis of "simultaneous" Landsat imagery (MSS and TM) for monitoring primary production in a small tropical coastal lagoon. Photogrammetric Engineering & Remote Sensing.

[35] Bagheri S, Dios RA. Utility of hyperspectral data for bathymetric mapping in a turbid estuary. International Journal of Remote Sensing. 1998;**19**(6):1179-1188

[36] Brezonik P, Menken KD,

Bauer M. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolvedorganic matter (CDOM). Lake and Reservoir Management.

[37] Goodenough A. Evaluating water quality monitoring with hyperspectral imagery [Senior Research Thesis]. Rochester Institute of Technology: Center for Imaging Science; 2001

[38] Shafique NA, Fulk F, Autrey BC, Flotemersch J. Hyperspectral remote sensing of water quality parameters for large rivers in the Ohio River Basin.

Remote Sensing. 2017;**9**(8):818

2015;**7**(10):12909-12941

1995;**61**(2):877-198

2005;**21**(4):373-382

for recovering chlorophyll

**120**

[47] Ortega S, Fabelo H, Iakovidis DK, Koulaouzidis A, Callico GM. Use of Hyperspectral/multispectral imaging in gastroenterology. Shedding some– different–light into the dark. Journal of Clinical Medicine. 2019;**8**(1):36

[48] Jensen D, Simard M, Cavanaugh K, Sheng Y, Fichot CG, Pavelsky T, et al. Improving the transferability of suspended solid estimation in wetland and deltaic waters with an empirical hyperspectral approach. Remote Sensing. 2019;**11**(13):1629

[49] Xi H, Hieronymi M, Röttgers R, Krasemann H, Qiu Z. Hyperspectral differentiation of phytoplankton taxonomic groups: A comparison between using remote sensing reflectance and absorption spectra. Remote Sensing. 2015;**7**(11):14781-14805

[50] Nelson JR, Guarda S. Particulate and dissolved spectral absorption on the continental shelf of the southeastern United States. Journal of Geophysical Research. 1995;**100**:8715-8732

[51] Vodacek A, Blough NV, DeGranpre MD, Peltzer ET, Nelson RK. Seasonal variations of CDOM and DOC in the middle Atlantic bight: Terrestrial inputs and photooxidation. Limnology and Oceanography. 1997;**42**:674-686

[52] Carder KL, Hawes SK, Baker KA, Smith RC, Steward RG, Mitchell BG. Reflectance model for quantifying chlorophyll-a in the presence of productivity degradation products. Journal of Geophysical Research. 1991;**96**:20599-20611

[53] Fell F., Fischer J, Schaale M, Schroeder T. Retrieval of chlorophyll concentration from MERIS measurements in the spectral range of sun-induced chlorophyll fluorescence. 2003. DOI: 10.1117/12.467267

[54] Kirkpatrick GJ, Orrico C, Moline MA, Matthew O, Schofield OM. Continuous hyperspectral absorption measurements of colored dissolved organic material in aquatic systems. Journal of Applied Optics. 2003;**42**(33):6564

[55] Rochelle-Newall EJ, Fisher TR, Fan C, Glibert PM. Dynamics of chromophoric dissolved organic matter and dissolved organic carbon in experimental mesocosm. International Journal of Remote Sensing. 1999;**20**(3):627-641

[56] Rochelle-Newall EJ, Fisher TR. Chromophoric dissolved organic matter and dissolved organic carbon in Chesapeake Bay. Marine Chemistry. 2002;**77**:23-41

[57] Brando V, Dekker A. Satellite hyperspectral remote sensing estimatingestuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing. 2003;**41**(6):1378-1381

[58] Kneubühler M, Gemperli C, Schläpfer D, Zah R, Itten K. Determination of water quality parameters in Indian ponds using remote sensing methods. In: Zagajewski B, Sobczak M, Wrzesień M, editors. Proceedings of 4th EARSeL Workshop on Imaging Spectroscopy. New Quality in Environmental Studies. Zurich Open Repository and Archive (ZORA), University of Zurich; 2005. DOI: 10.5167/uzh-97035

[59] Dingtian Y, Delu P, Xiaoyu Z, Xiaofeng Z, Xianqiang H, Shujing L. Retrieval of chlorophyll a and

suspended solid concentrations by hyperspectral remote sensing in Taihu Lake, China. Chinese Journal of Oceanology and Limnology. 2006;**24**(4):428-434

[60] Giardino C, Brando EV, Dekker GA, Strömbeck N, Candiani G. Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sensing of Environment. 2007;**109**:183-195

[61] Giardino C, Bresciani M, Pilkaityte R, Bartoli M, Razinkovas A. In situ measurements and satellite remote sensing of case 2 waters: First results from the Curonian lagoon. Oceanologia. 2010;**52**(2):197-210

[62] Santini F, Alberotanza L, Braga F, Cavalli RM, Pignatti S. Hyperspectral applications for the improvement of water quality assessment and monitoring in coastal areas. In: Proc. 'Hyperspectral 2010 Workshop', Frascati, Italy; 17-19 March 2010 (ESA SP-683, May 2010)

[63] Chen Z, Hanson JD, Curran PJ. The form of the relationship between suspended sediment concentration and spectral reflectance: Its implication for the use of Daedalus 1268 data. International Journal of Remote Sensing. 1991;**12**:215-222

[64] Novo EMM et al. The effect of viewing geometry and wavelength on the relationship between reflectance and suspended sediment concentration. International Journal of Remote Sensing. 1989;**10**:1357-1372

[65] Xiao X, Hambright K, Zhang L, Biradar C, Puls R. Integrating in-situ field measurements and satellite remote sensing to monitor harmful algae blooms in Oklahoma lakes. 2012. Available from: http:// Oklahomawatersurvey.org/?p=531

[66] Hommersom A, Kratzer S, Laanen M, Ansko I, Ligi M, Bresciani M, et al. Intercomparison in the field between the new WISP-3 and other radiometers (TriOS Ramses, ASD FieldSpec, and TACCS). Journal of Applied Remote Sensing. 2012;**6**:2012

[67] Maltese A, Capodici F, Ciraolo G, Loggia GL. Coastal zone water quality: Calibration of a waterturbidity equation for MODIS data. European Journal of Remote Sensing. 2013;**46**(1):333-347. DOI: 10.5721/ EuJRS20134619

[68] Kallio K. Remote sensing as a tool for monitoring lake water quality. In: Heinonen P, Ziglio G, van der Beken A, editors. Hydrological and Limnological Aspects of Lake Monitoring. Chichester, England: John Wiley & Sons, Ltd; 2000. pp. 237-245

[69] Kong J-L, Sun X-M, Wong DW, Chen Y, Yang J, Yan Y, et al. A semianalytical model for remote sensing retrieval of suspended sediment concentration in the Gulf of Bohai, China. Remote Sensing. 2015;**7**(5):5373-5397

[70] Knaeps E, Raymaekers D, Sterckx S, Odermatt D. An intercomparison of analytical inversion approaches to retrieve water quality for two distinct inland waters. In: Lacoste-Francis H, et al. editors. Proceedings of the 'Hyperspectral Workshop 2010' ESRIN, Frascati, Italy (ESA SP-683, May 2010); 2010. p. 7

[71] Lee Z-P, Arnone R, Hu C, Werdell PJ, Lubac B. Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm. Applied Optics. 2010;**49**:369-381

[72] Ritchie JC, Zimba PV, Everitt JH. Remote sensing techniques to assess water quality. Photogrammetric Engineering and Remote Sensing. 2003;**69**(6):695-704

**123**

*Use of Hyperspectral Remote Sensing to Estimate Water Quality*

from remote sensing reflectance data in optically shallow waters. Remote Sensing of Environment. 2006;**101**(1):13-24

[81] Lee ZP, Carder KL, Arnone R. Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for

optically deep waters. Applied Optics.

Atchafalaya river plume regions using above-surface hyperspectral remote sensing. Journal of Geophysical Research-Oceans. 2011;**116**:C02011

[83] Morillo S. Atmospheric Correction User's Guide; Laboratory for Applied Remote Sensing and Image Processing. Puerto Rico: University of Puerto Rico at

[84] Lee ZP, Hu C. Global distribution

Mayagüez; 2005. pp. 21-30

of Case-1 waters: An analysis from SeaWiFS measurements. Remote Sensing of Environment.

[85] Cao L, Li P, Zhang L, Chen T. Remote Sensing Image-Based Analysis of the Relationship Between Urban Heat Island and Vegetation Fraction. 2008

[86] Pahlevan N, Schott JR. Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013;**6**(2):360-374

[87] Karpouzli E, Malthus T. The empirical line method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing. 2003;**24**(5):1143-1150

[88] Gao BC, Davis CO, Goetz AFH. A review of atmospheric correction

2007;**101**(2006):270-276

[82] Zhu WN, Yu Q, Tian YQ, Chen RF, Gardner GB. Estimation of chromophoric dissolved organic matter in the Mississippi and

2001;**41**:5755-5772

*DOI: http://dx.doi.org/10.5772/intechopen.89222*

[73] Reif M. Remote sensing for inland water quality monitoring: A U.S. Army Corps of Engineers Perspective Engineer Research and Development Center/ Environmental Laboratory Technical Report (ERDC/EL TR)-11-13; 2011. 44pp

[74] Dall'Olmo G, Gitelson AA. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive water: Experimental results. Applied

chlorophyll-a in both terrestrial vegetation and turbid productive waters. Geophysical Research Letters. 2003;**30**:1038. DOI:

Merzlyak MN. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology. 2003;**160**:271-282

[77] Lathrop RG. Landsat thematic mapper monitoring of turbid inland water quality. Photogrammetric Engineering and Remote Sensing.

[78] Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS. Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied

Optics. 2005;**44**(3):412-422

[75] Dall'Olmo G,Gitelson AA, Rundquist DC. Towards a unified approach for remote estimation of

10.1029/2003GL018065

1992;**58**:465-470

Optics. 1999;**14**:417-427

2005;**44**:3576-3592

[79] Mobley CD, Sundman LK, Davis CO, Bowles JH, Downes TV, Leathers RA, et al. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Applied Optics.

[80] Cannizzaro JP, Carder KL.

Estimating chlorophyll-a concentrations

[76] Gitelson AA, Gritz U,

*Use of Hyperspectral Remote Sensing to Estimate Water Quality DOI: http://dx.doi.org/10.5772/intechopen.89222*

[73] Reif M. Remote sensing for inland water quality monitoring: A U.S. Army Corps of Engineers Perspective Engineer Research and Development Center/ Environmental Laboratory Technical Report (ERDC/EL TR)-11-13; 2011. 44pp

*Processing and Analysis of Hyperspectral Data*

et al. Intercomparison in the field between the new WISP-3 and other radiometers (TriOS Ramses, ASD FieldSpec, and TACCS). Journal of Applied Remote Sensing. 2012;**6**:2012

[67] Maltese A, Capodici F,

EuJRS20134619

pp. 237-245

2015;**7**(5):5373-5397

2010. p. 7

Ciraolo G, Loggia GL. Coastal zone water quality: Calibration of a waterturbidity equation for MODIS data. European Journal of Remote Sensing. 2013;**46**(1):333-347. DOI: 10.5721/

[68] Kallio K. Remote sensing as a tool for monitoring lake water quality. In: Heinonen P, Ziglio G, van der Beken A, editors. Hydrological and Limnological Aspects of Lake Monitoring. Chichester, England: John Wiley & Sons, Ltd; 2000.

[69] Kong J-L, Sun X-M, Wong DW, Chen Y, Yang J, Yan Y, et al. A semianalytical model for remote sensing retrieval of suspended sediment concentration in the Gulf of Bohai, China. Remote Sensing.

[70] Knaeps E, Raymaekers D, Sterckx S, Odermatt D. An intercomparison of analytical inversion approaches to retrieve water quality for two distinct inland waters. In: Lacoste-Francis H, et al. editors. Proceedings of the

'Hyperspectral Workshop 2010' ESRIN, Frascati, Italy (ESA SP-683, May 2010);

[71] Lee Z-P, Arnone R, Hu C, Werdell PJ, Lubac B. Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm. Applied

Optics. 2010;**49**:369-381

2003;**69**(6):695-704

[72] Ritchie JC, Zimba PV, Everitt JH. Remote sensing techniques to assess water quality. Photogrammetric Engineering and Remote Sensing.

[60] Giardino C, Brando EV, Dekker GA, Strömbeck N, Candiani G. Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sensing of Environment. 2007;**109**:183-195

Pilkaityte R, Bartoli M, Razinkovas A. In situ measurements and satellite remote sensing of case 2 waters: First results from the Curonian lagoon. Oceanologia. 2010;**52**(2):197-210

[62] Santini F, Alberotanza L, Braga F, Cavalli RM, Pignatti S. Hyperspectral applications for the improvement of water quality assessment and monitoring in coastal areas. In: Proc. 'Hyperspectral 2010 Workshop', Frascati, Italy; 17-19 March 2010 (ESA

[63] Chen Z, Hanson JD, Curran PJ. The form of the relationship between suspended sediment concentration and spectral reflectance: Its implication for the use of Daedalus 1268 data. International Journal of Remote

[61] Giardino C, Bresciani M,

suspended solid concentrations by hyperspectral remote sensing in Taihu Lake, China. Chinese Journal of Oceanology and Limnology.

2006;**24**(4):428-434

SP-683, May 2010)

Sensing. 1991;**12**:215-222

[64] Novo EMM et al. The effect of viewing geometry and wavelength on the relationship between reflectance and suspended sediment concentration.

International Journal of Remote Sensing. 1989;**10**:1357-1372

[66] Hommersom A, Kratzer S,

Laanen M, Ansko I, Ligi M, Bresciani M,

[65] Xiao X, Hambright K, Zhang L, Biradar C, Puls R. Integrating in-situ field measurements and satellite remote sensing to monitor harmful algae blooms in Oklahoma lakes. 2012. Available from: http:// Oklahomawatersurvey.org/?p=531

**122**

[74] Dall'Olmo G, Gitelson AA. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive water: Experimental results. Applied Optics. 2005;**44**(3):412-422

[75] Dall'Olmo G,Gitelson AA, Rundquist DC. Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters. Geophysical Research Letters. 2003;**30**:1038. DOI: 10.1029/2003GL018065

[76] Gitelson AA, Gritz U, Merzlyak MN. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology. 2003;**160**:271-282

[77] Lathrop RG. Landsat thematic mapper monitoring of turbid inland water quality. Photogrammetric Engineering and Remote Sensing. 1992;**58**:465-470

[78] Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS. Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied Optics. 1999;**14**:417-427

[79] Mobley CD, Sundman LK, Davis CO, Bowles JH, Downes TV, Leathers RA, et al. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Applied Optics. 2005;**44**:3576-3592

[80] Cannizzaro JP, Carder KL. Estimating chlorophyll-a concentrations from remote sensing reflectance data in optically shallow waters. Remote Sensing of Environment. 2006;**101**(1):13-24

[81] Lee ZP, Carder KL, Arnone R. Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for optically deep waters. Applied Optics. 2001;**41**:5755-5772

[82] Zhu WN, Yu Q, Tian YQ, Chen RF, Gardner GB. Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above-surface hyperspectral remote sensing. Journal of Geophysical Research-Oceans. 2011;**116**:C02011

[83] Morillo S. Atmospheric Correction User's Guide; Laboratory for Applied Remote Sensing and Image Processing. Puerto Rico: University of Puerto Rico at Mayagüez; 2005. pp. 21-30

[84] Lee ZP, Hu C. Global distribution of Case-1 waters: An analysis from SeaWiFS measurements. Remote Sensing of Environment. 2007;**101**(2006):270-276

[85] Cao L, Li P, Zhang L, Chen T. Remote Sensing Image-Based Analysis of the Relationship Between Urban Heat Island and Vegetation Fraction. 2008

[86] Pahlevan N, Schott JR. Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013;**6**(2):360-374

[87] Karpouzli E, Malthus T. The empirical line method for the atmospheric correction of IKONOS imagery. International Journal of Remote Sensing. 2003;**24**(5):1143-1150

[88] Gao BC, Davis CO, Goetz AFH. A review of atmospheric correction

techniques for hyperspectral remote sensing of land surfaces and ocean colour. In: Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, IGARSS 2006; USA: Denver, CO; 31 July-4 August 2006. pp. 1979-1981

[89] Pahlevan N, Schott JR. Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013;**6**(2):360-374

[90] Kutser T, Vahtmäe E, Praks J. A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal. Remote Sensing of Environment. 2009;**113**(10):2267-2274

[91] Lillesand TM, Johnson WL, Deuell RL, Lindstrom OM, Meisner DE. Use of Landsat data to predict the trophic state of Minnesota Lakes. Photogrammetric Engineering and Remote Sensing. 1983;**49**(2):219-229

[92] Kay S, Hedley JD, Lavender S. Sun glint correction of high and low spatial resolution images of aquatic scenes: A review of methods for visible and nearinfrared wavelengths. Remote Sensing. 2009;**1**(4):697-730

[93] Hedley JD, Harborne AR, Mumby PJ. Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos. International Journal of Remote Sensing. 2005;**26**(10):2107-2112

[94] Hochberg EJ, Andrefouet S, Tyler MR. Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in nearshore environments. IEEE Transactions on Geoscience and Remote Sensing. 2003;**41**(7):1724-1729

[95] Hedley JD, Harborne AR, Mumby PJ. Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos. International Journal of Remote Sensing. 2005;**26**(10):2107-2112

[96] Wang Q, Li S, Jia P, Qi C, Ding F. A review of surface water quality models. The Scientific World Journal. 2013;**2013**:7

[97] Wang X, Gu X, Wu Z, Wang C. Simulation of flood inundation of Guiyang city using remote sensing, GIS, and hydrologic model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2008;**XXXVII**(Part B8)

*Processing and Analysis of Hyperspectral Data*

techniques for hyperspectral remote sensing of land surfaces and ocean colour. In: Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, IGARSS 2006; USA: Denver, CO; 31 July-4 August 2006. pp. 1979-1981

[95] Hedley JD, Harborne AR,

Journal of Remote Sensing. 2005;**26**(10):2107-2112

The Scientific World Journal.

[97] Wang X, Gu X, Wu Z,

2008;**XXXVII**(Part B8)

of Guiyang city using remote sensing, GIS, and hydrologic model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

2013;**2013**:7

Mumby PJ. Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos. International

[96] Wang Q, Li S, Jia P, Qi C, Ding F. A review of surface water quality models.

Wang C. Simulation of flood inundation

[89] Pahlevan N, Schott JR. Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013;**6**(2):360-374

[90] Kutser T, Vahtmäe E, Praks J. A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal. Remote Sensing of Environment.

[91] Lillesand TM, Johnson WL,

Deuell RL, Lindstrom OM, Meisner DE. Use of Landsat data to predict the trophic state of Minnesota Lakes. Photogrammetric Engineering and Remote Sensing. 1983;**49**(2):219-229

[92] Kay S, Hedley JD, Lavender S. Sun glint correction of high and low spatial resolution images of aquatic scenes: A review of methods for visible and nearinfrared wavelengths. Remote Sensing.

Mumby PJ. Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos. International

2009;**113**(10):2267-2274

2009;**1**(4):697-730

[93] Hedley JD, Harborne AR,

Journal of Remote Sensing. 2005;**26**(10):2107-2112

2003;**41**(7):1724-1729

[94] Hochberg EJ, Andrefouet S, Tyler MR. Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in nearshore environments. IEEE Transactions on Geoscience and Remote Sensing.

**124**

## *Edited by Jie Chen, Yingying Song and Hengchao Li*

Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods.

Published in London, UK © 2020 IntechOpen © Dimiraira / iStock

Processing and Analysis of Hyperspectral Data

Processing and Analysis of

Hyperspectral Data

*Edited by Jie Chen,*

*Yingying Song and Hengchao Li*


**Chapter 9**

[117] Pardridge WM. The blood-brain barrier: Bottleneck in brain drug development. NeuroRx. 2005;**2**(1):3-14

*Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications*

[118] Ha SW, Hwang K, Jin J, Cho AS, Kim TY, Hwang SI, et al. Ultrasoundsensitizing nanoparticle complex for overcoming the blood-brain barrier: An effective drug delivery system. International Journal of Nanomedicine.

2019;**14**:3743-3752

[119] Sugiyama S, Yamashita Y, Kikuchi T, Sonoda Y, Kumabe T, Tominaga T. Enhanced antitumor effect of combined-modality treatment using convection-enhanced delivery of hydrophilic nitrosourea with irradiation

or systemic administration of temozolomide in intracranial brain tumor xenografts. Neurological Research. 2008;**30**(9):960-967

[120] Tosi U, Souweidane MM.

[122] Hoshino T, Wilson CB, Rosenblum ML, Barker M.

1975;**43**(2):127-135

**129**(2–3):97-103

**160**

Chemotherapeutic implications of growth fraction and cell cycle time in glioblastomas. Journal of Neurosurgery.

[123] Chou FH, Tsai KY, Su CY, Lee CC. The incidence and relative risk factors for developing cancer among patients with schizophrenia: A nine-year followup study. Schizophrenia Research. 2011;

2020;**137**:38-42

1396-1401

Longitudinal monitoring of Gd-DTPA following convection enhanced delivery in the brainstem. World Neurosurgery.

[121] Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;**344**(6190):


**1. Introduction**

"In general, pectin is extracted from agricultural products, especially apples, oranges, carrots, etc" [1–5]. But some environmental controls in plant breeding, as well as genetic engineering may increase the production of pectin in the plant. You may also be able to specialize more economically organisms to produce pectin.

For example, "Ajaya K Biswal, et al reported in 2015 that "Mutation of GAUT12 in *Populus deltoides* by RNA silencing results in reduced recalcitrance, increased growth and reduced xylan and pectin in a plant biofuel feedstock". The family name GAUT was coined after the discovery of Arabidopsis galacturonosyltransferase 1 (GAUT1). GAUT1 is a pectin biosynthetic homogalacturonan (HG): α-1,4 galacturonosyltransferase (GalAT) that functions in an HG:GalAT protein complex with GAUT7. The highest amount of Arabidopsis GAUT12 / irx8 was measured in cell tissues containing secondary wall that Its molecular structure is about 61% similar to that of GAUT1 monomers" [1].

"GAUT12 is thought to be a type II membrane protein and a Golgi target. If irx8 / GAUT12 mutates, GX decreases. In the Biswal study, microsomes from irx8 mutant stems didn't show any reduction in xylan XylT activity or xylan GlcAT (glucuronosyltransferase) activity compared to microsomes from wild type (WT)" [1].

Therefore, it's possible to increase pectin production in plants by UP-regulation or GAUT cloning. For any case, it is important to do a thorough study of pectin production control genes first. You may be able to DNA editing and programming for cells with greater, and more cost-effective reproduction.

Also, the environmental conditions' control of the plant, such as light, moisture, soil elements, fertilizers, temperature and etc., may affect of pectin production. Since these plants are primarily edible, this works can hurt food and agricultural policies. For this reason, in the pectin industries are primarily used, agricultural wastes.

"1.3 billion tons of human's food produced consumption is lost or wasted each year. In the meantime, 45% of food wasted are vegetative and herbal." "Food industries based on fruits and plants produce significant amounts of solid waste that can be used for animal feed, fertilizer, biochar production [6] or biogas [7]". However, the bulk of this waste is transported to landfills. While waste includes "avoidable" and "unavoidable" items; Waste of food, such as discarding edible parts of fruits that aren't suitable for the food industry, as well as other untreated/unrecycled waste, causes the spread of greenhouse gases, global warming and "carbon footprints". For this reason, the potential use of agricultural by-products and solid waste of the food industry creates a comprehensive opportunity for the development of functional ingredients industries [3].

"Pectin methyl esterases (PMEs) are important for the regeneration of pectins during plant growth. These enzymes have been reported in both plants and plant pathogens (such as bacteria and fungi). Plant and microbial PMEs aren't the same in enzyme substrate properties and pH tolerance. Furthermore, differences have also been reported between plant PMEs. Different types of PMEs act differently in response to different chemical compounds. For example, the inhibitory composition of plant PMEs, proposed by Melanie L'Enfant Jean et al, had completely no effect on the PMEs of pathogenic fungi. However, this study could open the way to control the properties of pectin methyl esterases" [2].

#### **2. Pectin-like carbohydrates in algae**

Today, algae is considered as one of the best and most cost-effective sources for many organic products. The ability to produce a product per unit time for algae can be much higher than plants. They do not need agricultural land and consume less water and resources. On the other hand, the harvest of algae in a photobioreactor can be about 24 times per year. Studies show that "the ability to produce lipids in algae is higher than plants. The percentage of oil and the quality of essential fatty acids - such as omega-3 and omega-6 - in algae is even higher than in fish oil. For these reasons, the consumption of algae - as human food - has attracted the attention of many in the international community, especially vegetarians. Also, the special properties of algae have made them the main candidates for providing new biofuel production resources" [8]. The algae ability of produce is not limited to lipids, some types of algae produce the high of proteins, carbohydrates, etc. Also with the use genetic engineering or even controls of the culture medium, they can easily be made more specialized.

The use of algae, to provide sources of pectin extraction is very attractive; Because the use of algae as a source, does not require agricultural land, abundant

#### *Biotechnology Applications in the Pectin Industry DOI: http://dx.doi.org/10.5772/intechopen.100470*

water and abundant and expensive resources, also its harvest per unit area is faster and more. On the other hand, increasing the percentage of pectin production in plants can be contrary to global food supply and biofuels policies.

"Magdalena Eder & Ursula Lütz-Meindl, identified pectin-like carbohydrate molecules in the green alga *Netrium digitus*. Pectins known to be involved in cell-wall expansion and representing major components of mucilage were the main objectives of this study. In more evolved plants, low methyl esterified pectins occur at cell corners, in middle lamellae and around air spaces, It's thought that the placement of pectins in these areas was done with the aim of creating resistance to environmental stresses. By forming a stable gel using calciumBridges, low methyl-esterified pectins prevent separation of cells as frequently induced by stress factors" [9].

"Lee, Kyung-Ah et al also reported that they were able to extract pectin-like polysaccharides from marine algae. They extracted Pectin-like from 5 kinds of microalgae and 9 kinds of macroalgae with different extraction methodologies. High yield of PLP was extracted in distilled water (DW) as 21.06±3.5% from *A. maxima*. In general, pectin-like extraction from macroalgae was more satisfactory than microalgae. In acidic condition (AC), PLP from Undaria pinnatifida was not precipitated. However, the yields of galacturonic acid was higher in Hizikia fusiformis (80.28±4.58%) and Laminaria japonica (65.85±0.61%), respectively. In biological activity tests, Fe2+ chelating activity was 26% higher than pectin from citrus peel as control. ABTS scavenging assay showed 100% antioxidative activity based on DW extraction from Ecklonia cava, whereas not detected from other macroalgae. The results of this study show new hopes for the use of gelling and stabilizing properties of PLP in various industries" [10].

"The total amount of carbohydrates in macroalgae is about 40 to 65% and in microalgae is about 10 to 50%. Kyoung-Ah Lee et al reported that they investigated the composition of PT and the anti-oxidant activities of 5 species of micro-algae (Spirulina maxima, Leptolyngbya sp, Tetraselmis sp, Dunaliella sp, and Chlorella sp) and 9 species of macro-algae (Saccharina japonica, Sargassum fulvellum, Undaria pinnatifida, Ecklonia cava, *Gracilaria verrucosa*, *Gelidium amansii*, Sargassum fusiforme, *Ulva pertusa*, and Sargassum horneri). Furthermore, Edirisinghe, S.L. et al and Chandrarathna, H.P.S.U. et al observed immunologic values of Spirulina maxima PT that have the potential to modulate gut microbial population, en- hance the expression of immune related genes, and boost gut morphology in zebrafish larvae" [10, 11].

"Rajapaksha, Dinusha C. et al and Edirisinghe, S.L. et al reported that the performance of pectin-like extracted from Spirulina.maxima was significant for wound healing. Also, D.S. Domozych et al wrote that the green alga Penium margitaraceum shows pectin metabolism in its cell wall. In addition, EDER et al extracted Pectin-like polymers from the cell wall and mucosa of the green alga *Netrium digitus*. In fact, many researchers have been able to extract pectin-like molecules from algae and have studied the properties of these molecules; But we still need more evidence on the effectiveness and industrial value of pectin-like extraction" [11].

"Kyoung-Ah Lee, et al reported, that they were able to extract pectin-like biomolecules from 5 species of microalgae and 9 species of macroalgae. MP extraction yields were higher in the distilled water (DW) extracts of Spirulina maxima and *Ulva pertusa* (yield extractions of 21.90±1.12% and 18.80± 0.97%, respectively). These results confirm that a large amount of MP was extracted by DW from marine algae, and MP is unlike the pectin derived from land plants. The MP extraction conditions were established using different solvents for each marine algae, and optimum extraction conditions exist for each species. Regarding biological activity, The MPs from Ecklonia cava and Sargassum horneri showed 99% ABTS radical scavenging activity, and the Fe2+Chelating activity of the MP from Dunaliella sp. Was confirmed to be higher than those of other MPs. The results of this study potentially indicate the potential for industrialization of pectin-like extraction from algae. This article also shows that the development of the use of DW materials in industry can help reduce environmental pollution" [10].

### **3. Pectin extraction and biotechnology**

"Approximately 30% of the primary cell wall in apples and citrus fruits contains PT pectin. There are several methods for extracting PT; Such as extraction with acid, hot water, enzyme, microwave, ultrasonic as well as combined methods. Acid and hot water extraction are the oldest methods in the pectin industry. The biggest drawback of traditional methods is the degradation of pectin polysaccharides and the long extraction time. In contrast, enzymatic extraction has many advantages such as no degradation of pectin polymers, low extraction temperature, shorter extraction time, reduction of environmental pollution, requires very low acidity and so on. For this reason, various green extraction techniques have received much attention" [11].

• biotechnology methods in pectin extraction include: enzymatic extraction and enzymatic-ultrasonic extraction (and etc). Which we examine.

#### **4. Enzymatic extraction of pectin**

"The analysis in **Table 1** shows that, the polysaccharides xylan and xyloglucan form the crosslinker between pectin, cellulose and hemicellulose. For this reason, the use of cellulase and xylanase can break the bond between these polysaccharides and separate pectin molecules from the cell wall. Based on studies and laboratory evidence, the highest enzymatic extraction efficiency of pectin is in the simultaneous use of cellulase and xylanase. Because using a mixture of cellulase and xylanase, it destroys the bond between xylan, xyloglucan and cellulose and causes the separation of pectin. While enzymatic treatment with xylanase alone reduces the extraction efficiency. The reason for this reduction in efficiency is probably the strong properties of the bonds between hemicellulose xyloses, not the bonds between hemicellulose and pectin or cellulose. The use of xylanase can improve cellulase function by degrading xylan / xyloglucan from the SBP matrix and further release pectin. However, the use of a higher cellulase ratio (2:1) reduced the pectin extraction efficiency in the study by Abou Elseoud et al. However, this may be due to pectinase activity remaining in the cellulase enzyme derived from Trichoderma Iongibrachiatum – reported by him. Because pectinase enzymes cause the hydrolysis of pectin polymers to soluble sugars, thereby reducing the efficiency of pectin extraction.

Comparison of pectin extracted in the best conditions of traditional extraction with sulfuric acid (temperature 85°C, pH = 1, time: 2 h) against pectin extracted by combined treatment of xylanase and cellulase enzymes (8.28%) shows that the extraction efficiency is similarity with the acid extraction method (5.26%). In addition, the maximum enzymatic extraction efficiency of pectin in the Abou Elseoud study is equal to or greater than the acidic extraction of beet pulp" [4].

In enzymatic Pectin extraction GalA efficiency from passion fruit peel were between 17.0 and 25.8 g/100 g of dry peel, which were similar to those obtained for the more commonly used citrus peel substrates with PPase-SE. Efficiencies were also comparable with those obtained from lemon pomace using a different polygalacturonase from Aspergillus niger and from pumpkin using a cellulase from Trichoderma viride and a multi-enzyme crude extract from *Bacillus polymyxa*. GalA efficiency from acid extraction was 15.9 ± 0.1 g/100 g dry peel, Which shows a much lower result

*[3], p. 6.*

*a Considered on dry weight of example [dry weight of extracted pectin [g] ÷dry weight of example [g] ×100] b Considered on dry weight of oil free example [dry weight of extracted pectin [g] ÷dry weight of oil free example [g] ×100]*

*c Considered on dry weight of alcohol insoluble residue of example [dry weight of extracted pectin [g] ÷dry weight alcohol insoluble residue [g] ×100];*

*NA: not applicable / NS: not specified*

#### **Table 1.**

*Repercussion of classical extraction method on the efficiency, quality, degree of esterification and galacturonic acid amount of extracted pectin from tropical and sub tropical fruit /by products.*

than other extraction conditions (p < 0.05). This was probably due to the decomposition of a percentage of soluble GalAs due to very low pH and high temperatures. Therefore, due to the lower extraction temperature as well as the milder pH required for enzymatic extraction; The performance and quality of pectin molecules obtained by enzymatic extraction method have been reported to be significantly higher than the chemical method. A significant number of reports show similar results in studies of pectin from passion fruit. However, most published reports are about the extraction of pectin from the dried peel. Juliana Vasco-Correa and Arley D. Zapata-Zapata [5], used the fresh peel with a relative high particle size, which it need to less energy intensive since high energy would be needed for drying and milling the peel. In Liew et al. study and Kulkarni & Vijayanand obtained maximum efficiency of 14.6 g/100 g of peel and 14.8 g/100 g of peel, respectively, by extracting pectin from passion fruit peel citric acid at pH 2 was used, which are similar to the conditions and results of the chemical extraction performed in the Vasco-Correa's studys. Canteri et al. obtained a slightly higher yield of 20.3 g/100 g of rid flour from passion fruit, using nitric acid for the extraction. Contreras234 Esquivel et al. achieved a yield of 25 g/100 g of dry passion fruit fiber using citric acid and autoclaving for 20 min. Kliemann et al. (2009) Enzyme loading had a significant effect on GalA yield (p < 0.05) (**Table 1**) [5].

The results do not show a significant difference between the performance of GalA prepared at 30 and 40 U / mL. But these two groups recorded higher solubility than 20 U / mL. Therefore, increasing the enzyme charge to 30 U / mL can improve the solubility of GalA, but increasing the enzyme load can not produce more positive results. According to the results, the maximum adsorption of PPase on protopectin (the pure substrate) obtained at 30 U/mL of PGase. So, 30 U/mL can considered as optimum enzyme loading. So, excessive increase in enzyme concentration at 40 U/ml not only increases project costs, but can also degrade soluble pectins, as PPase has significant endopolygalacturonase activity and can degrade internal bonds between GalAs. Although agitation speed has had a significant impact on other PPase-SE processes, in this case, it has had very little effect on GalA performance (p > 0.05). This is due to the excellent permeability and solubility of the enzyme anywhere, at 120–180 rpm. It's also possible that in the Vasco-Correa report, vortex flasks led to better mass transfer. Other reports indicate that agitation speeds do not have a significant effect on enzymatic heterogeneous processes in low solids loading, which in the study was about 2.5 g/100 ml. In addition, temperature and pH variables affect the rate of GalA dissolution (p > 0.05). Maximum yield was measured at 37° C and pH 3.0. This temperature was also the optimum value found for lemon peel pectin extraction using PPase-SE. Minimum yields were obtained at 44°C, perhaps because the enzyme wasn't stable at this heat temperature. While, changing the pH in the range of 4.0–5.0 does not cause much change; GalA performance increased significantly at pH 3.0 (p < 0.05). However, these results contradict the results of the study of pectin extraction with PPase-SE from lemon peel, under similar conditions and higher pH. Therefore, it is necessary to determine the best pH for each system, based on the study of the specific enzyme and substrate of the project. In the acidic extraction method of pectin, the more acidic degree of pH will be in favor of optimizing the extraction process, while the same can cause the destruction of pectin molecules. Acid also kills microbial pathogens.

The report of the periodic study of enzymatic extraction in flasks shows the maximum extraction efficiency of 21.9 g / 100 g of dry peel in 120 minutes. The longer the extraction time, the lower the efficiency and quality of the product, because there is a possibility of unwanted enzymatic hydrolysis due to the endopolygalacturonase activity of the enzyme. While some studies have used enzymatic therapy for longer than 12 to 20 hours, no significant increase in efficiency, quality, or optimization that benefits the pectin extraction industry has been reported.

Generally, yield is normally requested in scale-up of biocatalytic processes, and as the size of the stirred-tank increases, an increment of the mixing time is expected. Agitation speed in the range studied in the bioreactor had a great effect on GalA. The lower the energy consumption of a biochemical process, the greater the potential for industry expansion. This is one of the most important things to consider when planning your business. However, calculating the actual energy consumption for a floating particle heterogeneous system can be difficult without direct measurements" [5].