Synthetic Aperture Radar (SAR) Sentinel-1 (S1) surface soil moisture (SSM) retrieval is promising but challenging in canopy areas. Additional data from optical-thermal infrared (TIR) sensors (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) have potential to compensate for SSM monitoring from single S1 product, suggesting their synergistic use for SSM retrieval in vegetated terrains. A physical, straightforward and non-calibration synergy method of SAR S1 and optical-TIR MODIS was proposed in this study to enhance SSM estimation in an agro-pastoral transitional zone. Change detection for S1 backscatter (σ°) and simplified triangle method representing land surface temperature (LST) – normalized difference vegetation index (NDVI) space (T_s-VI) for MODIS, were combined under a synergistic framework regarding vegetation conditions. The synergy method performance was evaluated against in-situ soil moisture sites for different phenological stages. Correlation analysis results indicated that S1 σ° is highly sensitive to SSM in non-growing season, whereas MODIS LST is negatively linked to root-zone soil moisture during summer crop growth as the seasonal vegetation effect that can be explained via the observed trapezoidal T_s-VI shape. For the synergy, S1 is mainly involved in during non-growing period, while major contribution in growing period was from MODIS, which accounted for 90% improvements in correlation (R, 0.29 – 0.55) and 30% for Root-Mean-Square Error (RMSE, 0.093 – 0.065 m³m⁻³) during a four-month crop growth as compared to single S1 product. For entire period, the synergy method outperformed single S1 over all sites, where the average R and RMSE improved 30% (0.46 – 0.60) and 14% (0.070 – 0.060 m³m⁻³), respectively, with highest values (R = 0.73 and RMSE = 0.039 m³m⁻³) observed in a maize site. This synergy suggests a suitability of integrating optical-TIR data to improve SAR SSM estimation in agricultural lands, especially over the crop growth period when water is a major limiting factor.

合成孔径雷达 (SAR) Sentinel-1 (S1) 地表土壤水分 (SSM) 反演很有前景，但在冠层区域具有挑战性。来自光热红外 (TIR) 传感器（例如，中分辨率成像光谱仪，MODIS）的附加数据有可能补偿来自单个 S1 产品的 SSM 监测，表明它们可协同用于植被地形中的 SSM 检索。本研究提出了一种物理、直接和非标定的 SAR S1 和光学-TIR MODIS 协同方法，以增强农牧过渡区的 SSM 估计。S1 后向散射 (σ°) 的变化检测和表示地表温度 (LST) 的简化三角法 - 归一化差异植被指数 (NDVI) 空间 (T _s-VI) 对于 MODIS，在关于植被条件的协同框架下进行了组合。针对不同物候阶段的原位土壤水分站点评估了协同方法的性能。相关分析结果表明，S1 σ° 在非生长季节对 SSM 高度敏感，而 MODIS LST 与夏季作物生长期间根区土壤水分呈负相关，因为季节性植被效应可以通过观察到的梯形 T _s来解释-VI 形状。对于协同作用，S1 主要参与非生长期，而生长期的主要贡献来自 MODIS，其相关性提高了 90% (R, 0.29 – 0.55)，均方根误差提高了 30% (RMSE, 0.093 – 0.065 m ³ m ⁻³) 与单一 S1 产品相比，在四个月的作物生长期间。在整个时期内，协同方法在所有站点上的表现都优于单个 S1，其中平均 R 和 RMSE 分别提高了 30% (0.46 – 0.60) 和 14% (0.070 – 0.060 m ³ m ^-3 )，最高值 ( R  = 0.73 和 RMSE = 0.039 m ³ m ^-3 ) 在玉米地点观察到。这种协同作用表明整合光学-TIR 数据以改进农业用地中的 SAR SSM 估计是合适的，尤其是在水是主要限制因素的作物生长期。