The feasibility of soil moisture retrieval from C-band Sentinel-1 data has been widely acknowledged, with pre-operational 1-km products currently available at regional and/or continental scale using the long-term (LTCD) or short-term change detection (STCD) methods. Both algorithms share the same assumptions of time-invariant roughness and vegetation, which can be questionable even for a short period of 4 Sentinel-1 acquisitions (18–36 days). An advanced change detection (ACD) method is proposed in this study for an improved soil moisture retrieval from Sentinel-1 data, including two main modifications with respect to the existing STCD methods: i) approximating the effect of temporal varying vegetation on the Sentinel-1 backscatter as a variation in the two-way attenuation, and ii) a temporal soil moisture constraint based on the coarse Soil Moisture Active Passive (SMAP) soil moisture product to partly remove the uncertainty caused by vegetation and/or roughness changes. The evaluation, based on time-series observations from 34 OzNet stations and ground samples collected during the Fifth Soil Moisture Active and Passive Experiment (SMAPEx-5) showed that the ACD improved the correlation coefficient (R), root mean square error (RMSE) and un biased RMSE (ubRMSE), achieving 0.66, 0.071 m³/m³ and 0.071 m³/m³ at the point scale, 0.77, 0.063 m³/m³ and 0.051 m³/m³ at 1-km scale, 0.80, 0.055 m³/m³ and 0.050 m³/m³ at 3-km scale. The contribution of the two modifications was further investigated using 559 stations from 22 networks across the world, showing that: i) the two modifications can increase R by 0.08–0.13 and reduce the retrieval RMSE by 0.009–0.013 m³/m³ (10% - 15% relative), and ii) the retrieval over densely vegetated areas or areas with large temporal vegetation variation can benefit more from the proposed modifications. The ACD achieved stable performance for various Sentinel-1 orbits/passes and maintained a stable performance for retrieval windows up to 30 Sentinel-1 acquisitions, providing a promising alternative for achieving consistent soil moisture retrievals from Sentinel-1.

从 C 波段 Sentinel-1 数据反演土壤水分的可行性已得到广泛认可，目前使用长期 (LTCD) 或短期变化检测可在区域和/或大陆尺度上获得运行前 1 公里产品(STCD) 方法。两种算法共享相同的时不变粗糙度和植被假设，即使在 4 次 Sentinel-1 采集的短时间内（18-36 天）也可能存在问题。本研究提出了一种高级变化检测 (ACD) 方法，用于从 Sentinel-1 数据中改进土壤水分反演，包括对现有 STCD 方法的两个主要修改：i) 近似时间变化植被对 Sentinel-1 的影响1 反向散射作为双向衰减的变化，ii) 基于粗土壤水分主动被动 (SMAP) 土壤水分产品的时间土壤水分约束，以部分消除由植被和/或粗糙度变化引起的不确定性。评估基于来自 34 个 OzNet 站的时间序列观测和第五次土壤水分主动和被动实验 (SMAPEx-5) 期间收集的地面样本，表明 ACD 提高了相关系数 (R)、均方根误差 (RMSE)和无偏 RMSE (ubRMSE)，达到 0.66、0.071 m³ /m ³和0.071 m ³ /m ³在点尺度，0.77、0.063 m ³ /m ³和0.051 m ³ /m ³在1公里尺度上，0.80、0.055 m ³ /m ³和0.050 m ³ /米³在 3 公里规模。使用来自全球 22 个网络的 559 个站点进一步研究了两次修改的贡献，表明：i) 两次修改可以将 R 增加 0.08-0.13 并将反演 RMSE 降低 0.009-0.013 m ³ /m ³（10% - 15% 相对），以及 ii) 在植被茂密的区域或具有较大时间植被变化的区域的检索可以从提议的修改中受益更多。ACD 在各种 Sentinel-1 轨道/通过中实现了稳定的性能，并在多达 30 个 Sentinel-1 采集的反演窗口中保持了稳定的性能，为从 Sentinel-1 实现一致的土壤水分反演提供了有希望的替代方案。