Details

Autor(en) / Beteiligte

• Xing, Minfeng
• Chen, Lin
• Wang, Jinfei
• Shang, Jiali
• Huang, Xiaodong

Titel

Soil Moisture Retrieval Using SAR Backscattering Ratio Method during the Crop Growing Season

Ist Teil von

• Remote sensing (Basel, Switzerland), 2022-07, Vol.14 (13), p.3210

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season.