3. SAR wetland applications

#### 3.1. Change detection

play an important role in the scattering process [6]. The alpha angle α (0 ≤ α ≤ 90o

<sup>α</sup> <sup>¼</sup> <sup>X</sup> 3

i¼1

where cos(αi) in the magnitude of the first component of the coherency matrix eigenvector ei

Another widely used polarimetric decomposition method is the Freeman-Durden method [18]. Contrary to the Cloude-Pottier decomposition, which is a purely mathematical construct, the Freeman-Durden decomposition method is a physically model-based incoherent decomposition based on the polarimetric covariance matrix. It relies on the conversion of a covariance matrix to a three-component model. The results of this decomposition are three coefficients corresponding to the weights of different model components. A polarimetric covariance matrix [C] can be decomposed to a sum of three components, corresponding to volume, surface, and

where fv, fs, and fd are the three coefficients corresponding to volume, surface, and double bounce scattering, respectively. The Freeman-Durden decomposition is particularly well adapted to the study of vegetated areas [18]. Thus, it is widely used for multitemporal wetland monitoring to

Scattering mechanism information can also be obtained using compact polarimetric SAR data. Two decomposition methods are commonly used. The first is the m-δ decomposition method [11], which is based on the degree of polarization of the backscattered signal m <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

r

r

r

r

where Vd, Vv, and Vs refer to double bounce, volume, and surface scattering mechanisms, respectively. The second decomposition method is the m-χ decomposition [25], which is based

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0m ð Þ <sup>1</sup> � sin <sup>δ</sup> 2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0ð Þ <sup>1</sup> � <sup>m</sup> <sup>p</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0m ð Þ <sup>1</sup> <sup>þ</sup> sin <sup>δ</sup> 2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0m ð Þ <sup>1</sup> <sup>þ</sup> sin 2<sup>χ</sup> 2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0ð Þ <sup>1</sup> � <sup>m</sup> <sup>p</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g0m ð Þ <sup>1</sup> � sin 2<sup>χ</sup> 2

information about the type of scattering mechanism

70 Wetlands Management - Assessing Risk and Sustainable Solutions

double bounce scattering mechanisms [18]:

track changes of shallow open water to flooded vegetation [26].

=g0 and the relative phase δ ¼ atan g3=g2

Vd Vv Vs

on the degree of polarization m and the ellipticity χ ¼ asin �g3=mg0

Pd Pv Ps

2 6 4

2 6 4

(i = 1, 2, 3).

g2 <sup>1</sup> <sup>þ</sup> g2

q

<sup>2</sup> <sup>þ</sup> <sup>g</sup><sup>2</sup> 3 ) provides

(14)

(15)

Piα<sup>i</sup> (12)

½ �¼ C fv½ � C <sup>v</sup> þ fs½ � C <sup>s</sup> þ fd½ � C <sup>d</sup> (13)

� � and has the form [11]:

� �=2, and has the form [25]:

The accurate, effective, and continuous identification and tracking of changes in wetlands is necessary for monitoring human, climatic and other effects on these ecosystems and better understanding of their response. Wetlands are expected to be even more dynamic in the future with rapid and frequent changes due to the human stresses on environment and the global warming [27]. Different methodologies can be adopted to detect and track changes in wetlands using SAR imagery, depending on the type of the change and the available polarization option. For example, a change in the surface water level of a wetland area due to e.g. heavy rainfall could extend the wetland water surface, causing flooding in the surrounding areas. Such a change can be easily detected using SAR amplitude images before and after the event acquired with similar acquisition geometry. The specular scattering of the radar signal can highlight the open water areas (dark areas due to low returned signal). Spatiotemporal changes in wetlands as dynamic ecosystems could be interpreted using SAR amplitude imagery only. This is because changes within wetlands could change the surface type illuminated by the radar. Sometimes, the change could be more complex with alternations in surface water, flooded vegetation and upland boundaries. In this case, the additional polarimetric information from full or compact polarimetric SAR is necessary for the detection and interpretation of changes within wetlands.

As shown in Figure 7, a change within a wetland from wet soil with a high dielectric constant to open water is usually accompanied with a change in the radar backscattering from surface scattering with a strong returned signal (Figure 7a) to specular reflection with a weak returned signal (Figure 7b). The change in wetland could also be due to its seasonal development over time. Hence, intermediate marsh with large vegetation stems properly oriented could allow for double bounce scattering mechanism (Figure 7c). As the marsh develops, the strong observed double bounce scattering mechanism gradually decreases in favor of the volume scattering (Figure 7d) from the dense canopy of the fully developed marsh [28]. Thus, polarimetric decomposition methods enable the identification of wetland classes (e.g. flooded vegetation) and monitoring changes within these classes by means of the temporal change in the backscattering mechanisms. The role of decomposition methods for identification and monitoring of wetlands was highlighted in a number of recent studies [26, 29–31]. Another way of monitoring changes within wetlands could be through polarimetric change detection methodologies using full [10], compact [10, 32], or even coherent dual [33] polarized SAR imagery. These methodologies are based on polarimetric coherency/covariance matrices. Herein, changes are flagged without information about the scattering mechanisms, which occurred during the scattering process. Test statistics, such as those proposed in [34, 35], were proven effective for polarimetric change detection over wetlands.

As one goes up the polarization hierarchy from single channel intensity only data to dualchannel, compact polarimetry, and full polarimetry data sets the information content increases and the wetland classification subsequently improves [11, 46–50]. In general, dual channel SAR's and polarization ratios outperform single channel intensity only data systems and compact polarimetric data is better than dual channel data. Fully polarimetric data consistently shows the best information content for wetland classification by using polarimetric parameters derived from the data matrices, or polarimetric decompositions such as the Cloude and Pottier [17], Freeman-Durden [18], or Touzi [24] decompositions. You can use the decompositions or polarimetric parameters such as the polarization phase difference to identify flooded vegetation due to the double bounce effect increasing intensity and producing the phase shift. The Shannon Entropy has also proven useful for wetland mapping [51] and may have some benefit for finding the transition from flooded to saturated soil and between flooded vegetation and open water. This is a two-parameter model with one parameter relating to intensity and the other polarization diversity, and it may be simpler than using the

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

http://dx.doi.org/10.5772/intechopen.80224

73

There have been numerous frequency effect evaluations since the early observations of enhanced scattering from flooded vegetation on SEASAT imagery [52]. This effect for swamps and many vegetated wetlands with high biomass is quite evident in L-band data due to the increased canopy penetration and better interaction with the water/trunk/stem interface, resulting in the double bounce scattering mechanism [53, 54]. It is also evident at C-band and in some cases at Xband depending on the biomass and density of the canopy and the subsequent wavelength dependent penetration [41, 55–60]. In general, X- and C-band are preferred for herbaceous wetlands and less dense canopies while L-band is preferred for woody wetlands such as swamps

SAR data has also proven effective for mapping peatlands, which is becoming more important because of climate change and carbon emission issues [61–64]. Due to the penetration of these longer wavelengths and the ability to penetrate beneath the plant canopy, there have been some indications that L-band polarimetric SAR can be used to differentiate between bog and fen peatlands due to the sensitivity of the water flow characteristics beneath the surface [65]. SAR does not penetrate water so provides little information on invasive aquatic submersive plants, but L-band and to a lesser degree C-band have shown some success at identifying invasive Phragmites [66]. This tall dense invasive provides significant SAR backscatter and can be separated from other land-cover due to this characteristic and its location in the landscape. It helps to use LiDAR as well as SAR due to the relative height and landscape position of the

In general, one wants to use a steep incidence angle for woody wetlands or flooded vegetation mapping in order to enhance the penetration to reach the water surface and realize the enhanced scattering effect due to double bounce scattering between the vegetation and the water surface. A shallow angle may be preferred if the focus is on the open water mapping as this can enhance the contrast between the specular scattering of surface water and the flooded vegetation with volume and double bounce scattering. Recent reviews of wetland remote

decompositions.

Phragmites [67].

and other wetland classes with high biomass.

sensing and SAR are provided in [40, 41, 68–70].

Figure 7. (a) Surface scattering mechanism from wet soil, (b) radar signal reflection from shallow open water, (c) double bounce scattering mechanism from signal interaction with vegetation stems and water surface and (d) volume scattering due to random scattering within the dense flooded vegetation canopy.

### 3.2. Wetland mapping

Ever since the launch of the Earth Resources Technology Satellite (ERTS) in 1972 there has been interest in using satellite remote sensing as a tool for wetland mapping and classification because the traditional air photo and field visit approaches are too costly and time consuming [36]. Wetlands are difficult to map and classify due to a large degree of spatial and temporal variability as well as structural and spectral similarities between wetland classes. Over the last decade or so a state-of-the-art approach for wetland classification has emerged. This is an object based classification approach using multi-source input data (optical and SAR) with a machine learning classification algorithm and a quality Digital Elevation Model (DEM) for identifying terrain suitability for wetlands or surface water [37–40]. Using this approach, greater than 90% accuracy is often achieved for a wide variety of wetland classification systems [41].

The early satellite SAR systems produced single channel intensity only output data, which limited its value for land cover and wetland classification. This type of data when used synergistically with optical data improved the wetland classification compared to using optical data alone but only to a minor degree [37, 42–45]. This is largely due to the ability of the SAR wavelengths to penetrate wetland vegetation and "see" the underlying water, thereby improving the flooded vegetation class discrimination. The flooded vegetation tends to produce a double bounce scattering mechanism, as explained earlier, which increases the intensity of the backscatter. HH polarization is best for this due to the enhanced penetration in vegetation. As one goes up the polarization hierarchy from single channel intensity only data to dualchannel, compact polarimetry, and full polarimetry data sets the information content increases and the wetland classification subsequently improves [11, 46–50]. In general, dual channel SAR's and polarization ratios outperform single channel intensity only data systems and compact polarimetric data is better than dual channel data. Fully polarimetric data consistently shows the best information content for wetland classification by using polarimetric parameters derived from the data matrices, or polarimetric decompositions such as the Cloude and Pottier [17], Freeman-Durden [18], or Touzi [24] decompositions. You can use the decompositions or polarimetric parameters such as the polarization phase difference to identify flooded vegetation due to the double bounce effect increasing intensity and producing the phase shift. The Shannon Entropy has also proven useful for wetland mapping [51] and may have some benefit for finding the transition from flooded to saturated soil and between flooded vegetation and open water. This is a two-parameter model with one parameter relating to intensity and the other polarization diversity, and it may be simpler than using the decompositions.

There have been numerous frequency effect evaluations since the early observations of enhanced scattering from flooded vegetation on SEASAT imagery [52]. This effect for swamps and many vegetated wetlands with high biomass is quite evident in L-band data due to the increased canopy penetration and better interaction with the water/trunk/stem interface, resulting in the double bounce scattering mechanism [53, 54]. It is also evident at C-band and in some cases at Xband depending on the biomass and density of the canopy and the subsequent wavelength dependent penetration [41, 55–60]. In general, X- and C-band are preferred for herbaceous wetlands and less dense canopies while L-band is preferred for woody wetlands such as swamps and other wetland classes with high biomass.

SAR data has also proven effective for mapping peatlands, which is becoming more important because of climate change and carbon emission issues [61–64]. Due to the penetration of these longer wavelengths and the ability to penetrate beneath the plant canopy, there have been some indications that L-band polarimetric SAR can be used to differentiate between bog and fen peatlands due to the sensitivity of the water flow characteristics beneath the surface [65].

3.2. Wetland mapping

Ever since the launch of the Earth Resources Technology Satellite (ERTS) in 1972 there has been interest in using satellite remote sensing as a tool for wetland mapping and classification because the traditional air photo and field visit approaches are too costly and time consuming [36]. Wetlands are difficult to map and classify due to a large degree of spatial and temporal variability as well as structural and spectral similarities between wetland classes. Over the last decade or so a state-of-the-art approach for wetland classification has emerged. This is an object based classification approach using multi-source input data (optical and SAR) with a machine learning classification algorithm and a quality Digital Elevation Model (DEM) for identifying terrain suitability for wetlands or surface water [37–40]. Using this approach, greater than 90% accuracy

Figure 7. (a) Surface scattering mechanism from wet soil, (b) radar signal reflection from shallow open water, (c) double bounce scattering mechanism from signal interaction with vegetation stems and water surface and (d) volume scattering

The early satellite SAR systems produced single channel intensity only output data, which limited its value for land cover and wetland classification. This type of data when used synergistically with optical data improved the wetland classification compared to using optical data alone but only to a minor degree [37, 42–45]. This is largely due to the ability of the SAR wavelengths to penetrate wetland vegetation and "see" the underlying water, thereby improving the flooded vegetation class discrimination. The flooded vegetation tends to produce a double bounce scattering mechanism, as explained earlier, which increases the intensity of the backscatter. HH polarization is best for this due to the enhanced penetration in vegetation.

is often achieved for a wide variety of wetland classification systems [41].

due to random scattering within the dense flooded vegetation canopy.

72 Wetlands Management - Assessing Risk and Sustainable Solutions

SAR does not penetrate water so provides little information on invasive aquatic submersive plants, but L-band and to a lesser degree C-band have shown some success at identifying invasive Phragmites [66]. This tall dense invasive provides significant SAR backscatter and can be separated from other land-cover due to this characteristic and its location in the landscape. It helps to use LiDAR as well as SAR due to the relative height and landscape position of the Phragmites [67].

In general, one wants to use a steep incidence angle for woody wetlands or flooded vegetation mapping in order to enhance the penetration to reach the water surface and realize the enhanced scattering effect due to double bounce scattering between the vegetation and the water surface. A shallow angle may be preferred if the focus is on the open water mapping as this can enhance the contrast between the specular scattering of surface water and the flooded vegetation with volume and double bounce scattering. Recent reviews of wetland remote sensing and SAR are provided in [40, 41, 68–70].

### 3.3. Dynamic surface water and flooded vegetation mapping

The specular backscatter from calm water surfaces allows for easy discrimination of open water from upland and flooded vegetation using SAR data. At the same time, the double bounce scattering from flooded vegetation allows discrimination from upland and open water as described earlier. This, combined with the all-weather data collection capabilities, makes SAR an ideal sensor for mapping flood as well as dynamic surface water and flooded vegetation [40].

One of the key issues in using the InSAR observations for assessing wetland hydrology is the calibration of the InSAR observations, which are relative in both space and time. In time, the measurements provide the change in water level (not the actual water level) that occurred between the two data acquisitions. In space, the measurements describe the relative change of water levels in the entire interferogram with respect to a zero change at an arbitrary reference point, because the actual range between the satellite and the surface cannot be determined accurately. However, the relative changes between pixels can be determined at the cm-level. In many other InSAR applications, such as earthquake or volcanic induced deformation, the reference zero change point is chosen to be in the farfield, where changes are known to be negligible [98]. However, in wetland InSAR, the assumption of zero surface change in the far-field does not hold, because flow and water levels can be discontinuous across the various water control structures or other flow

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

http://dx.doi.org/10.5772/intechopen.80224

75

The calibration stage requires additional information on water level changes, which can be derived from various sources. In areas monitored by stage (water level) stations, as in the Everglades, the stage data can be used for the InSAR calibration, as conducted by [90]. Another calibration technique relies on spaceborne radar altimetry, which detects absolute water level changes over a few km wide footprints with accuracy of 5–10 cm [94]. However, the altimetry observations are limited in space and time, as the radar altimeter data can only be acquired along the satellite tracks, which are spaces roughly 100 km apart. Also, the altimetry data is not always synchronized with the InSAR observations, which are acquired

Wetland biomass is of increasing interest due to methane emission contributions to climate change from degraded and thawing wetlands. Wetland change can also be used as an indicator of climate change impacts. Wetland vegetation biomass can therefore be an important indicator of carbon sequestration in wetlands and is essential for understanding the carbon cycle of these ecosystems. SAR data has the potential to estimate vegetation biomass in wetlands because radar is particularly sensitive to the vegetation canopy over an underlying water surface [99]. The biomass of totora reeds and bofedal in water-saturated Andean grasslands was mapped with ERS-1 data in [100]. The goal was to protect this ecosystem from overgrazing. They found that the backscatter signal of ERS-1 was sensitive to the humid and dry biomass of reeds and grasslands and their biomass maps were useful for the livestock management in the study region. [101] developed regression and analytical models for estimating mangrove wetland biomass in South China using RADARSAT images. [102, 103] also found that L-band ALOS PALSAR can be used to estimate the aboveground biomass because of the correlation between HH and HV backscatter signals. C-band backscatter characteristics from RADARSAT-2 data were used by [104] to estimate the biomass of the Poyang Lake wetlands in China. Also, [105] used ENVISAT ASAR data to estimate wetland vegetation biomass in Poyang Lake. These studies have shown that it is possible to estimate above water

obstacles.

by different satellites.

3.5. Wetland biomass estimation

biomass in wetlands with SAR data.

Flood mapping is operational with SAR data in many countries using data from a variety of SAR systems from X- to L-band (see for example [71–74]). Intensity thresholding techniques have traditionally been used for open water mapping [75]. Texture, cross-polarization data, and other techniques are being developed to solve the problem when the water is brighter due to wind or current induced roughness, as well as to automate the process [76–79].

As described in the section on wetland mapping the double bounce effect and enhanced scattering from flooded vegetation makes SAR a good sensor for mapping flooded vegetation from non-flooded vegetation [40, 59, 80]. This allows the delineation of wetland extent and with multi-temporal data can be very useful for monitoring seasonal and/or annual changes in the wetland size and extent. [81] showed that flooded vegetation tends to remain coherent using InSAR techniques and this can then be used to map wetland type and extent. Thus, SAR is an ideal sensor for monitoring the spatially and temporally dynamic flooded vegetation components of wetlands.

The development of standard coverages, like that used for the Sentinel program, results in stacks of data with the same geometry and facilitates the use of temporal filters for speckle noise reduction. The use of a multi-temporal filter rather than the conventional spatial filtering approach can be an effective way to reduce the speckle while maintaining the spatial resolution and the ability to detect small objects and edges [82, 83]. This also allows the use of intensity metrics rather than thresholds to separate water from land, which can also help solving the wind roughness problem [84]. The multi-temporal coverage provided by SAR systems enables generating hydro-period and dynamic surface water as well as flooded vegetation masks [85, 86]. This enables better mapping of temporary, seasonal and ephemeral water bodies as well as the permanent water bodies, which are static and much easier to map. A recent review of SAR flood mapping and flood studies with SAR is provided in [87], while [88] provides a review of flooded vegetation mapping with SAR.

#### 3.4. Water level monitoring

Wetland interferometric synthetic aperture radar (InSAR) is a relatively new application of the InSAR technology that detects water level changes over wide areas with 5–100 m pixel resolution and several centimeters vertical accuracy [89–91]. The wetland InSAR technique works where vegetation emerges above the water surface due to the "double bounce" effect, in which the radar pulse is backscattered twice from the water surface and vegetation [53]. InSAR observations were successfully used to study wetland hydrology in the Everglades [90–93], Louisiana [94–96] and the Sian Ka'an in Yucatan [97].

One of the key issues in using the InSAR observations for assessing wetland hydrology is the calibration of the InSAR observations, which are relative in both space and time. In time, the measurements provide the change in water level (not the actual water level) that occurred between the two data acquisitions. In space, the measurements describe the relative change of water levels in the entire interferogram with respect to a zero change at an arbitrary reference point, because the actual range between the satellite and the surface cannot be determined accurately. However, the relative changes between pixels can be determined at the cm-level. In many other InSAR applications, such as earthquake or volcanic induced deformation, the reference zero change point is chosen to be in the farfield, where changes are known to be negligible [98]. However, in wetland InSAR, the assumption of zero surface change in the far-field does not hold, because flow and water levels can be discontinuous across the various water control structures or other flow obstacles.

The calibration stage requires additional information on water level changes, which can be derived from various sources. In areas monitored by stage (water level) stations, as in the Everglades, the stage data can be used for the InSAR calibration, as conducted by [90]. Another calibration technique relies on spaceborne radar altimetry, which detects absolute water level changes over a few km wide footprints with accuracy of 5–10 cm [94]. However, the altimetry observations are limited in space and time, as the radar altimeter data can only be acquired along the satellite tracks, which are spaces roughly 100 km apart. Also, the altimetry data is not always synchronized with the InSAR observations, which are acquired by different satellites.

#### 3.5. Wetland biomass estimation

3.3. Dynamic surface water and flooded vegetation mapping

74 Wetlands Management - Assessing Risk and Sustainable Solutions

provides a review of flooded vegetation mapping with SAR.

Louisiana [94–96] and the Sian Ka'an in Yucatan [97].

vegetation [40].

components of wetlands.

3.4. Water level monitoring

The specular backscatter from calm water surfaces allows for easy discrimination of open water from upland and flooded vegetation using SAR data. At the same time, the double bounce scattering from flooded vegetation allows discrimination from upland and open water as described earlier. This, combined with the all-weather data collection capabilities, makes SAR an ideal sensor for mapping flood as well as dynamic surface water and flooded

Flood mapping is operational with SAR data in many countries using data from a variety of SAR systems from X- to L-band (see for example [71–74]). Intensity thresholding techniques have traditionally been used for open water mapping [75]. Texture, cross-polarization data, and other techniques are being developed to solve the problem when the water is brighter due

As described in the section on wetland mapping the double bounce effect and enhanced scattering from flooded vegetation makes SAR a good sensor for mapping flooded vegetation from non-flooded vegetation [40, 59, 80]. This allows the delineation of wetland extent and with multi-temporal data can be very useful for monitoring seasonal and/or annual changes in the wetland size and extent. [81] showed that flooded vegetation tends to remain coherent using InSAR techniques and this can then be used to map wetland type and extent. Thus, SAR is an ideal sensor for monitoring the spatially and temporally dynamic flooded vegetation

The development of standard coverages, like that used for the Sentinel program, results in stacks of data with the same geometry and facilitates the use of temporal filters for speckle noise reduction. The use of a multi-temporal filter rather than the conventional spatial filtering approach can be an effective way to reduce the speckle while maintaining the spatial resolution and the ability to detect small objects and edges [82, 83]. This also allows the use of intensity metrics rather than thresholds to separate water from land, which can also help solving the wind roughness problem [84]. The multi-temporal coverage provided by SAR systems enables generating hydro-period and dynamic surface water as well as flooded vegetation masks [85, 86]. This enables better mapping of temporary, seasonal and ephemeral water bodies as well as the permanent water bodies, which are static and much easier to map. A recent review of SAR flood mapping and flood studies with SAR is provided in [87], while [88]

Wetland interferometric synthetic aperture radar (InSAR) is a relatively new application of the InSAR technology that detects water level changes over wide areas with 5–100 m pixel resolution and several centimeters vertical accuracy [89–91]. The wetland InSAR technique works where vegetation emerges above the water surface due to the "double bounce" effect, in which the radar pulse is backscattered twice from the water surface and vegetation [53]. InSAR observations were successfully used to study wetland hydrology in the Everglades [90–93],

to wind or current induced roughness, as well as to automate the process [76–79].

Wetland biomass is of increasing interest due to methane emission contributions to climate change from degraded and thawing wetlands. Wetland change can also be used as an indicator of climate change impacts. Wetland vegetation biomass can therefore be an important indicator of carbon sequestration in wetlands and is essential for understanding the carbon cycle of these ecosystems. SAR data has the potential to estimate vegetation biomass in wetlands because radar is particularly sensitive to the vegetation canopy over an underlying water surface [99]. The biomass of totora reeds and bofedal in water-saturated Andean grasslands was mapped with ERS-1 data in [100]. The goal was to protect this ecosystem from overgrazing. They found that the backscatter signal of ERS-1 was sensitive to the humid and dry biomass of reeds and grasslands and their biomass maps were useful for the livestock management in the study region. [101] developed regression and analytical models for estimating mangrove wetland biomass in South China using RADARSAT images. [102, 103] also found that L-band ALOS PALSAR can be used to estimate the aboveground biomass because of the correlation between HH and HV backscatter signals. C-band backscatter characteristics from RADARSAT-2 data were used by [104] to estimate the biomass of the Poyang Lake wetlands in China. Also, [105] used ENVISAT ASAR data to estimate wetland vegetation biomass in Poyang Lake. These studies have shown that it is possible to estimate above water biomass in wetlands with SAR data.
