Soil Moisture Retrieval Using SAR Derived Vegetation Descriptors in Water Cloud Model

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

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Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

Remote Sensing ◽

10.3390/rs10091370 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1370 ◽

Cited By ~ 15

Author(s):

Junhua Li ◽

Shusen Wang

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Cloud Model ◽

Data Availability ◽

Agricultural Crop ◽

Sar Images ◽

Area Index ◽

Water Cloud ◽

The Impact

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.

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Estimation of Vegetation Parameters of Water Cloud Model for Global Soil Moisture Retrieval Using Time-Series L-Band Aquarius Observations

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2016.2596541 ◽

2016 ◽

Vol 9 (12) ◽

pp. 5621-5633 ◽

Cited By ~ 9

Author(s):

Chenzhou Liu ◽

Jiancheng Shi

Keyword(s):

Time Series ◽

Soil Moisture ◽

Cloud Model ◽

Global Soil ◽

Water Cloud ◽

Vegetation Parameters ◽

L Band

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Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Water ◽

10.3390/w13020135 ◽

2021 ◽

Vol 13 (2) ◽

pp. 135

Author(s):

Min Zhang ◽

Fengkai Lang ◽

Nanshan Zheng

Keyword(s):

Surface Roughness ◽

Soil Moisture ◽

Satellite Data ◽

Cloud Model ◽

Growth Cycle ◽

Wheat Growth ◽

Vegetation Fraction ◽

Effective Roughness ◽

Water Cloud ◽

Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

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