Abstract
Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.
Highlights
Wetlands perform various ecological, economic and cultural functions with provision of water for agricultural activities being fundamental
Field soil moisture measurements were made in March and August 2013
TerraSAR-X data were obtained for March and September 2013
Summary
Economic and cultural functions with provision of water for agricultural activities being fundamental. In densely vegetated areas X-Band waves bounce off vegetation canopy and backscatter signals do not include contribution from soil surface. It has been utilized to determine the vegetation contribution by using Leaf Area Index (LAI), vegetation water content and biomass as the canopy descriptors [9] [23]-[25]. [26] showed that X band is sensitive to density and size of canopy elements while [27] opted to derive soil moisture in areas with the Normalized Difference Vegetation Index (NDVI) less than 0.25, indicative of bare soils. In another study by [28], X-band was found to better discriminate bare and vegetated fields This emphasizes the importance of incorporating vegetation contribution when determining the soil moisture from X-band imagery.
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