**2.2. Spatial, spectral, and temporal resolutions for urban climate studies**

Although the operating principles of various imaging instruments have changed over the years, the spectrum of applications and usability of imagery have been largely determined by their spatial, spectral, and temporal resolutions. Image resolution characteristics play a major role in determining the size and properties of the features or phenomena that can be discriminated in remotely sensed imagery.

The spectral signal is one of the most important properties of urban land surfaces measured with remote sensing. Most satellite sensors are multispectral systems, meaning they sense the earth surface with a few broad spectral bands. Urban environments possess a high spectral heterogeneity and they are characterized by a large diversity of materials. Therefore, increased spectral resolution is a requirement for urban remote sensing.

The spatial resolution of remote sensor is a function of the altitude of the platform relative to the earth surface and the resolving power of the sensor. The spatial resolution is often expressed as the ground sampling distance of the sensor at nadir. The spatial resolution required for a given study could be determined by the size of the smallest element to be mapped. However, due to several factors, the element spatial resolution is not sufficient to detect urban objects. The radiation measured for one pixel is affected by the radiation of its neighboring pixels, due to scattering effects that complicate the analysis. Moreover, an object can only be positively identified if it is represented by several pixels. The ideal spatial resolution of an image for a given application will, therefore, be several times smaller than the size of the smallest object that needs to be identified.

The temporal resolution of a remote sensing system is the theoretical or the operational capability for acquiring repetitive imagery over some time interval. The spatial extent of images

**Figure 3.** Relationship between objects under consideration and spatial resolution in urban sites: (a) pixels significantly larger than objects, (b) pixel and objects sizes are of the same order, and (c) pixels are significantly smaller than object.

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A wide variety of EO systems acquiring data with various resolutions can be useful for urban studies. Medium resolution remote sensor data have been used to examine large dimensional urban phenomena or processes since early 1970s when NASA successfully launched the first Landsat. Over a period of nearly four decades, the Landsat program has acquired a scientifically valuable image archive unmatched in quality, details, coverage, and length, which has been the primary source of data for urbanization studies at the regional, national, and global scales. Since July 1982, with the launch of Landsat 4, thermal sensors are included in Landsat missions enabling surface temperature studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), in orbit since December 18, 1999, includes a multispectral thermal instrument, which provides accurate estimates of emissivity and surface temperature in high spatial resolution. Yet, its on-demand acquisition mode limits the spatial resolution.

The Sentinels constitute the first series of the ESA operational satellites for the Copernicus Programme. Copernicus is the continuity of the Global Monitoring for Environment and Security (GMES) Programme, which is launched to provide data, information, services, and knowledge that support Europe's goals regarding sustainable development and global governance of the environment. Copernicus is a European system for monitoring the Earth. It consists of a complex set of systems, which collect data from multiple sources: EO satellites and *in-situ* sensors and provides up-to-date information through a set of services related to environmental and security issues. The five Sentinel missions are based on constellations of two satellites to fulfill revisit and coverage requirements, providing robust datasets for Copernicus.

depends on the swath width, and influences the resulting temporal resolution.

**2.3. The Sentinels**

The Nyquist sampling theorem [13] establishes a sufficient condition for a sample rate that permits a discrete sequence of samples to capture all the information from a continuoustime signal of finite bandwidth and it is the theoretical basis for the spatial resolution needed to map individual objects. The Nyquist theorem suggests that an object should be of the order of one-tenth of the dimension of the pixel in order to ensure that it will be completely independent of its random position and orientation relative to the sampling scheme. A schematic representation of the relationship between the spatial resolution and the objects under consideration is given in **Figure 3**, although applicable thresholds are not easy to define. The three situations outlined in **Figure 3** require different approaches to unravel information for the underling objects. The urban surface objects (i.e., buildings, roads, etc.) have small spatial extent. Given the large amount of spatial heterogeneity, most analyses in urban areas rely upon high spatial resolution imagery usually from aerial photography or drones.

EO data and the advances in remote sensing techniques, though, can provide an alternative when working with larger scales than the objects to identify. The so-called sub-pixel classification methods resolve the radiance of a single pixel and identify percentages of separate components. These methods are particularly useful for material mappings when used with hyperspectral data [14], but there are examples in the literature of sub-pixel classification methods used with coarser spectral and spatial resolution data [15, 16].

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**Figure 3.** Relationship between objects under consideration and spatial resolution in urban sites: (a) pixels significantly larger than objects, (b) pixel and objects sizes are of the same order, and (c) pixels are significantly smaller than object.

The temporal resolution of a remote sensing system is the theoretical or the operational capability for acquiring repetitive imagery over some time interval. The spatial extent of images depends on the swath width, and influences the resulting temporal resolution.

A wide variety of EO systems acquiring data with various resolutions can be useful for urban studies. Medium resolution remote sensor data have been used to examine large dimensional urban phenomena or processes since early 1970s when NASA successfully launched the first Landsat. Over a period of nearly four decades, the Landsat program has acquired a scientifically valuable image archive unmatched in quality, details, coverage, and length, which has been the primary source of data for urbanization studies at the regional, national, and global scales. Since July 1982, with the launch of Landsat 4, thermal sensors are included in Landsat missions enabling surface temperature studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), in orbit since December 18, 1999, includes a multispectral thermal instrument, which provides accurate estimates of emissivity and surface temperature in high spatial resolution. Yet, its on-demand acquisition mode limits the spatial resolution.

#### **2.3. The Sentinels**

**2.2. Spatial, spectral, and temporal resolutions for urban climate studies**

increased spectral resolution is a requirement for urban remote sensing.

nated in remotely sensed imagery.

132 Multi-purposeful Application of Geospatial Data

of the smallest object that needs to be identified.

tography or drones.

Although the operating principles of various imaging instruments have changed over the years, the spectrum of applications and usability of imagery have been largely determined by their spatial, spectral, and temporal resolutions. Image resolution characteristics play a major role in determining the size and properties of the features or phenomena that can be discrimi-

The spectral signal is one of the most important properties of urban land surfaces measured with remote sensing. Most satellite sensors are multispectral systems, meaning they sense the earth surface with a few broad spectral bands. Urban environments possess a high spectral heterogeneity and they are characterized by a large diversity of materials. Therefore,

The spatial resolution of remote sensor is a function of the altitude of the platform relative to the earth surface and the resolving power of the sensor. The spatial resolution is often expressed as the ground sampling distance of the sensor at nadir. The spatial resolution required for a given study could be determined by the size of the smallest element to be mapped. However, due to several factors, the element spatial resolution is not sufficient to detect urban objects. The radiation measured for one pixel is affected by the radiation of its neighboring pixels, due to scattering effects that complicate the analysis. Moreover, an object can only be positively identified if it is represented by several pixels. The ideal spatial resolution of an image for a given application will, therefore, be several times smaller than the size

The Nyquist sampling theorem [13] establishes a sufficient condition for a sample rate that permits a discrete sequence of samples to capture all the information from a continuoustime signal of finite bandwidth and it is the theoretical basis for the spatial resolution needed to map individual objects. The Nyquist theorem suggests that an object should be of the order of one-tenth of the dimension of the pixel in order to ensure that it will be completely independent of its random position and orientation relative to the sampling scheme. A schematic representation of the relationship between the spatial resolution and the objects under consideration is given in **Figure 3**, although applicable thresholds are not easy to define. The three situations outlined in **Figure 3** require different approaches to unravel information for the underling objects. The urban surface objects (i.e., buildings, roads, etc.) have small spatial extent. Given the large amount of spatial heterogeneity, most analyses in urban areas rely upon high spatial resolution imagery usually from aerial pho-

EO data and the advances in remote sensing techniques, though, can provide an alternative when working with larger scales than the objects to identify. The so-called sub-pixel classification methods resolve the radiance of a single pixel and identify percentages of separate components. These methods are particularly useful for material mappings when used with hyperspectral data [14], but there are examples in the literature of sub-pixel classification

methods used with coarser spectral and spatial resolution data [15, 16].

The Sentinels constitute the first series of the ESA operational satellites for the Copernicus Programme. Copernicus is the continuity of the Global Monitoring for Environment and Security (GMES) Programme, which is launched to provide data, information, services, and knowledge that support Europe's goals regarding sustainable development and global governance of the environment. Copernicus is a European system for monitoring the Earth. It consists of a complex set of systems, which collect data from multiple sources: EO satellites and *in-situ* sensors and provides up-to-date information through a set of services related to environmental and security issues. The five Sentinel missions are based on constellations of two satellites to fulfill revisit and coverage requirements, providing robust datasets for Copernicus.

The Sentinel-2 mission provides continuity to services relying on multispectral high-spatialresolution optical observations (like Landsat and SPOT satellites): it carries a Multispectral Instrument (MSI) covering the electromagnetic spectrum from the visible to the shortwave infrared with a pixel resolution from 10 to 60 m. Two satellites in orbit will provide data at a 5 days interval at the equator. Sentinel-2 combines a large swath, frequent revisit, and systematic acquisition of all land surfaces at high-spatial resolution and with a large number of spectral bands [17]. The pair of Sentinel-2 satellites routinely delivers high-resolution optical images globally, providing enhanced continuity of SPOT and Landsat type data.

Sentinel-3 mission represents the continuity of the ENVISAT sensors, i.e., MERIS (MEdium Resolution Imaging Spectrometer) and Advanced Along Track Scanning Radiometer (AATSR). In particular, the Sea and Land Surface Temperature Radiometer (SLSTR) will provide TIR data at 1 km resolution with daily revisit at the equator [18]. Among the Sentinel-3 mission objectives is to monitor the land surface temperature with high-end accuracy and reliability in support of climate monitoring.