Surface soil moisture is estimated by inverting physical scattering models for low- crops using L-band airborne SAR data over the incidence angle range from 30 to 50°. The forward simulation is accurate with 1.7, 1.8, and 2.3 dB rmse for grass, wheat (including oat and forage), and soybean respectively assessed over 38 fields and during the full growth period for soybean. Dominant scattering mechanisms revealed by the model are found consistent across the incidence angle range: surface (VV) and double bounce (HH) for grass, double bounce for both polarization of wheat, and surface (young plant) and volume (mature plant) for both polarization of soybean. The model fidelity is robust across incidence angles between 30 and 50°, benefiting from rigorously simulating the three scattering mechanisms and successfully simulating the effect of vegetation and roughness.

The soil moisture estimates are accurate to unbiased rmse of 0.041, 0.059 and 0.060 m³/m³ and correlation of 0.71, 0.83, and 0.69 for grass, wheat, and soybean fields, respectively over the soil moisture dynamic range up to 0.5 m³/m³, and soybean's full growth cycle. Vegetation water content and surface roughness are retrieved simultaneously to the soil moisture solutions, thus constraining the inversion of soil moisture. The retrieval performance is fairly uniform across the incidence angle, partly because of the reliable forward modeling across the incidence angle and also thanks to the reliable retrieval strategy. The robustness against the incidence angle supports frequent global mapping.

The use of both HH and VV offers superior soil moisture retrievals than single-channel VV or HH input perfoms, by 0.034 m³/m³ unbiased rmse for grass and by 0.01 m³/m³ for wheat and soybean. The VV-based retrieval performed better than HH for grass and soybean, where surface scattering was important, but either choice resulted in similar retrieval accuracy for double-bounce dominanted wheat. No need for quad-pol and multi-angular observations would allow the retrieval approach to be applicable for spaceborne global mapping.

表层土壤水分是通过使用 L 波段机载 SAR 数据在 30 到 50° 的入射角范围内反演小作物的物理散射模型来估计的。前向模拟准确，草、小麦（包括燕麦和草料）和大豆的 rmse 分别为 1.7、1.8 和 2.3 dB rmse，分别评估了 38 个田地和大豆的整个生育期。模型揭示的主要散射机制在入射角范围内一致：草的表面 (VV) 和双反射 (HH)，小麦的两极化双反射，以及表面（幼苗）和体积（成熟植物）大豆两极分化。模型保真度在 30 到 50° 之间的入射角范围内是稳健的，

土壤水分估计准确到 0.041、0.059 和 0.060 m ³ /m ^{3 的}无偏均方根误差以及草、小麦和大豆田的相关系数分别为 0.71、0.83和 0.69，土壤水分动态范围高达 0.5 m ³ /m ³，以及大豆的完整生长周期。植被含水量和地表粗糙度同时被反演到土壤水分溶液中，从而限制了土壤水分的反演。整个入射角的检索性能相当均匀，部分原因是整个入射角的可靠正向建模，也归功于可靠的检索策略。对入射角的鲁棒性支持频繁的全局映射。

与单通道 VV 或 HH 输入性能相比，HH 和 VV 的使用提供了更好的土壤水分反演，草为0.034 m ³ /m ³无偏 rmse ，小麦和大豆为0.01 m ³ /m ³。对于表面散射很重要的草和大豆，基于 VV 的检索比 HH 表现得更好，但两种选择都导致双弹优势小麦的相似检索精度。不需要四极点和多角度观测将使反演方法适用于星载全球测绘。