A New Soil Moisture Retrieval Algorithm from the L-Band Passive Microwave Brightness Temperature Based on the Change Detection Principle

Remote Sensing

卷:12

Zhuangzhuang Feng¹  Lei Li¹  Xingming Zheng²  Tao Jiang²  Xiaojie Li²  Xiaofeng Li²  Bingze Li³  Hongxin Xu⁴  Yanlong Sun⁴ 

[1] College of Resources and Environment, University of Chinses Academy of Sciences, Beijing 100049, China;

[2] Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

[3] School of Geomatics and Prospecing Engineering, Jilin Jianzhu University, Changhcun 130118, China;

[4] Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China;

关键词: soil moisture;    passive microwave remote sensing;    change detection;    farmland;   

DOI  :  10.3390/rs12081303

来源: DOAJ

【 摘 要 】

The launch of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites has led to the development of a series of L-band soil moisture retrieval algorithms. In these algorithms, many input parameters (such as leaf area index and soil texture) and empirical coefficients (such as roughness coefficient (h_P, N_RP) and crop structure parameter (b_P, tt_P)) are needed to calculate surface soil moisture (SSM) from microwave brightness temperature. Many previous studies have focused on how to determine the value of these coefficients and input parameters. Nevertheless, it can be difficult to obtain their ‘real’ values with low uncertainty across large spatial scales. To avoid this problem, a passive microwave remote sensing SSM inversion algorithm based on the principle of change detection was proposed and tested using theoretical simulation and a field SSM dataset for an agricultural area in northeastern China. This algorithm was initially used to estimate SSM for radar remote sensing. First, theoretical simulation results were used to confirm the linear relationship between the change rates for SSM and surface emissivity, for both H and V polarization. This demonstrated the reliability of the change detection algorithm. Second, minimum emissivity (or the difference between maximum emissivity and minimum emissivity) was modeled with a linear relationship between vegetation water content, derived from a three-year (2016–2018) SMAP L3 SSM dataset. Third, SSM values estimated by the change detection algorithm were in good agreement with SMAP L3 SSM and field SSM, with RMSE values ranging from 0.015~0.031 cm³/cm³ and 0.038~0.051 cm³/cm³, respectively. The V polarization SSM accuracy was higher than H polarization and combined H and V polarization accuracy. The retrieved SSM error from the change detection algorithm was similar to SMAP SSM due to errors inherited from the training dataset. The SSM algorithm proposed here is simple in form, has fewer input parameters, and avoids the uncertainty of input parameters. It is very suitable for global applications and will provide a new algorithm option for SSM estimation from microwave brightness temperature.

【 授权许可】

Unknown