Abstract
The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.
Highlights
Soil moisture plays a key role in the terrestrial water cycle
This study investigated the capability of using Synthetic Aperture Radar (SAR)-derived vegetation descriptors in a Water Cloud Model (WCM) for improving soil moisture retrieval over a vegetated area
The results can be explained from the relationships of the vegetation descriptors with Leaf Area Index (LAI), which shows σ◦HV saturates at relatively higher LAI values for some crop types and is generally more sensitive to vegetation than radar vegetation index (RVI) and Normalized Difference Vegetation Index (NDVI)
Summary
The retrieval of soil moisture over a large area is important in the modeling and assessing drought impact [1], evapotranspiration [2], and water budget [3,4]. Radar has a high backscattering sensitivity to soil moisture due to the high contrast of the microwave dielectric constant (ε) between dry soil (ε = 2~3) and water (ε = 80) [5]. It has the advantage of observing the earth’s surface day and night in all weather conditions. Over the past 30 years, considerable effort has been spent on using Synthetic Aperture Radar (SAR) imagery to retrieve soil moisture
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