Relationship of local incidence angle with satellite radar backscatter for different surface conditions

O’Grady, Damien; Leblanc, Marc; Gillieson, David

doi:10.1016/j.jag.2013.02.005

Cited by 38 publications

(25 citation statements)

References 30 publications

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“…In our experiments, using a single threshold resulted in missing water bodies in the far range, because there were no seeds captured: the threshold value excluded more pixels on open water in the far range, which usually has lower signal to noise ratios and higher calibration uncertainty. Work by O'Grady et al [21,46] also concluded that there are variations in the backscatter based on the incidence angle, given the varying noise levels of the scene. Another drawback of threshold-based methods for water mapping from SAR is that the threshold used for water mapping is severely affected by the noise (and noise floor) of the scene.…”

Section: Discussionmentioning

confidence: 99%

Operational Surface Water Detection and Monitoring Using Radarsat 2

et al. 2016

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Traditional on-site methods for mapping and monitoring surface water extent are prohibitively expensive at a national scale within Canada. Despite successful cost-sharing programs between the provinces and the federal government, an extensive number of water features within the country remain unmonitored. Particularly difficult to monitor are the potholes in the Canadian Prairie region, most of which are ephemeral in nature and represent a discontinuous flow that influences water pathways, runoff response, flooding and local weather. Radarsat-2 and the Radarsat Constellation Mission (RCM) offer unique capabilities to map the extent of water bodies at a national scale, including unmonitored sites, and leverage the current infrastructure of the Meteorological Service of Canada to monitor water information in remote regions. An analysis of the technical requirements of the Radarsat-2 beam mode, polarization and resolution is presented. A threshold-based procedure to map locations of non-vegetated water bodies after the ice break-up is used and complemented with a texture-based indicator to capture the most homogeneous water areas and automatically delineate their extents. Some strategies to cope with the radiometric artifacts of noise inherent to Synthetic Aperture Radar (SAR) images are also discussed. Our results show that Radarsat-2 Fine mode can capture 88% of the total water area in a fully automated way. This will greatly improve current operational procedures for surface water monitoring information and impact a number of applications including weather forecasting, hydrological modeling, and drought/flood predictions.

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Section: Discussionmentioning

confidence: 99%

Operational Surface Water Detection and Monitoring Using Radarsat 2

et al. 2016

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“…For example, it is believed that the rate of backscatter change with local incidence angle may provide a better means to map the water extents than a single threshold on backscatter alone [41]. Therefore, our future work will investigate robust water mapping methodologies that involve more variables, such as local incidence angle, terrain indices, or any other useful information.…”

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confidence: 99%

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

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Abstract:The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.

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“…The t-test was conducted using equation 1. SAR data is known to be sensitive not just to cover type but also structure of the objects which contribute to the volume of the scattering process (Cloude, 2010;O'Grady et al, 2013). This concept was linked to the backscatter values recorded for some of the permanent classes such as settlement, waterbody and even the crop fields on the 2016 classified composite image.…”

Section: Discussionmentioning

confidence: 99%

Monitoring the Impacts of El Niño on the Extent of Cultivated Fields Using Sar Data Around the Agricultural Region of the Free State, South Africa

Ngie

Tesfamichael

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:There is continuous challenge in crop monitoring from erratic climatic phenomena such as irregular rainfall episodes during required farming seasons or cloud cover. Remote sensing has offered vital support in the monitoring of such scenarios and informs relevant authorities for better decision making. While optical sensors measure the greenness of vegetation to enable monitoring of its status, their usage is constrained by the continuous cloud cover during crop growth seasons in sub Saharan Africa. Synthetic aperture radar data (SAR) are on the other hand capable of penetrating clouds and are sensitive to the structure and moisture content of target features, thereby providing complementary information for monitoring crop cultivated fields. This study sought to evaluate the sensitivity of Sentinel -1 SAR data to the status of cultivated crop fields that experienced varying rainfall amounts between 2015/2016 and 2016/2017 growing seasons as a result of El Niño induced drought in 2015. Dual polarization composites per season were classified and through sample farms delineated from Google Earth image, backscatter values were extracted for statistical comparisons. The two sample t-test was applied to test significance of the differences between the two seasons at the level of farm status. Results showed an overall significant difference (p-value of 0.003< 0.005) in SAR backscatter sensitivity to cultivated crop fields during and after the El Niño phenomenon. While these results are encouraging for areas that experience clouds during growing seasons, further improvements can be expected by factoring in other variables such as topographic and moisture conditions of farms.

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Relationship of local incidence angle with satellite radar backscatter for different surface conditions

Cited by 38 publications

References 30 publications

Operational Surface Water Detection and Monitoring Using Radarsat 2

Operational Surface Water Detection and Monitoring Using Radarsat 2

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Monitoring the Impacts of El Niño on the Extent of Cultivated Fields Using Sar Data Around the Agricultural Region of the Free State, South Africa

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