The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m/m and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications.

中文翻译：

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来自主动和被动微波联合观测的表层土壤湿度：基于机器学习方法集成 ASCAT 和 SMAP 观测


与使用单个传感器相比，主动和被动微波测量的融合预计将在各种环境条件下提供更强大的表面土壤湿度（SSM）测绘。因此，最新的 L 波段被动观测（即土壤湿度主动被动，SMAP）和主动观测（即高级散射仪，ASCAT）的集成为提高 SSM 测绘精度提供了机会。然而，这种整合在很大程度上仍未得到充分探索。在此背景下，通过充分考虑传统辐射传输模型（RTM）中的潜在误差源以及其他 SSM 相关因素，研究了 SMAP 亮度温度（TB）和 ASCAT 后向散射系数的集成用于全球尺度 SSM 估计。基于具有减轻失配问题的全球分布密集网络的地面测量和时空独立评估策略，本研究：（i）综合评估了四种经典机器学习方法，包括随机森林（RF）、长短期记忆（LSTM） 、支持向量机（SVM）和级联神经网络（CNN），并选择性能最好的RF方法来实现SSM的最终集成； (ii) 将集成检索与使用来自具有相同机器学习框架的单个传感器（SMAP 或 ASCAT）的数据以及 SMAP 被动、ASCAT 主动和 ESA CCI 主动-被动组合 SSM 产品进行的集成检索进行比较。结果表明，积分检索获得了 0.042 m/m 的平均无偏均方根误差 (ubRMSE) 和 0 的时间相关性，从而实现了令人满意的性能。756，优于从单个有源或无源传感器估计的基于机器学习的 SSM，并且也优于 SMAP、ASCAT 和 ESA CCI 产品。此外，与SMAP和ASCAT SSM产品相比，时间分辨率明显提高，全球大部分地区的时间分辨率超过60%。因此，混合主动和被动测量可以通过增加全球范围内的采样来提供更可靠的 SSM 绘图，并有助于改善水文生态应用。