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

Remote Sensing

Xiaoning Song²  Jianwei Ma¹  Xiaotao Li¹  Pei Leng²  Fangcheng Zhou²  Shuang Li²  Zhao-Liang Li³  Nicolas Baghdadi³ 

[1] Research Center of Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; E-Mails:;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; E-Mails:College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;

关键词: advanced integrated equation model (AIEM);    ASAR;    Hyperion;    michigan microwave canopy scattering (MIMICS);    surface soil moisture;    water-cloud model;   

DOI  :  10.3390/rs61212055

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.