First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study

• Main Authors: Xiaoning Song, Jianwei Ma, Xiaotao Li, Pei Leng, Fangcheng Zhou, Shuang Li
• Format: Article
• Language: English
• Published: MDPI AG 2014-12-01
• Series: Remote Sensing
• Subjects: advanced integrated equation model (AIEM); ASAR; Hyperion; michigan microwave canopy scattering (MIMICS); surface soil moisture; water-cloud model
• Online Access: http://www.mdpi.com/2072-4292/6/12/12055

This study introduces a new approach to estimate surface soil moisture in vegetated areas using Synthetic Aperture Radar (SAR) and hyperspectral data. To achieve this, the Michigan Microwave Canopy Scattering (MIMICS) model was initially used to simulate backscatter from vegetated surfaces containing various canopy water contents, across three frequency bands (i.e., L, S, and C). Using this simulated dataset, the influence of the canopy water content on the backscattered signals was further analyzed. In addition, we developed a modified Water-Cloud model which adds in the crown-ground interaction term. Finally, a soil moisture retrieval model for an agricultural region was developed. Alternating polarization data with ASAR and Hyperion hyperspectral data were used to retrieve soil moisture and validate the feasibility of the retrieval model. The field measured data from the Heihe river basin was used to confirm the proposed model. Results revealed an average absolute deviation (AAD) and average absolute relative deviation (AARD) of 0.051 cm3∙cm−3 and 19.7%, respectively, between the estimated soil moisture and the field measurements.