Abstract

Soil moisture is essential for water resources management, yet accurate information of soil moisture has been a challenge. The major goal was to parametrize the Modified Water Cloud Model (MWCM). The Sentinel-1A data of winter wheat crop was collected for two weeks. Concurrently, in-situ soil moisture data was collected using Time Domain Reflectometer (TDR). A parametric scheme was used for the retrieval of the VV polarization of Sentinel-1A. The effect of NDVI as a vegetation descriptors (V₁ and V₂) on total VV backscatter (σ⁰) was analyzed. The calibration showed NDVI has the potential to influence Water Cloud Model (WCM) and vegetation descriptors; hence it is recommended to calibrate the MWCM. The coefficient of determination (R² = 0.83) showed a good agreement between observed and estimated soil moisture. Therefore, this approach help improve soil moisture prediction, and can be applied to determine soil moisture more accurately for winter crops, grasses, and pasture lands.

摘要

土壤水分对于水资源管理至关重要，但准确的土壤水分信息一直是一个挑战。主要目标是对修改后的水云模型 (MWCM) 进行参数化。收集了两周冬小麦作物的 Sentinel-1A 数据。同时，使用时域反射仪（TDR）收集原位土壤水分数据。参数方案用于检索 Sentinel-1A 的 VV 极化。分析了 NDVI 作为植被描述符（V ₁和 V ₂）对总 VV 后向散射（σ ⁰）的影响。校准表明 NDVI 有可能影响水云模型 (WCM) 和植被描述符；因此建议校准 MWCM。决定系数（R ²= 0.83) 表明观察到的和估计的土壤水分之间有很好的一致性。因此，这种方法有助于改进土壤水分预测，并可用于更准确地确定冬季作物、草和牧场的土壤水分。