Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

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Abstract

Abstract As the most recent 3‐km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel‐1 L2_SM_SP product has a unique capability to provide global‐scale 3‐km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high‐resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel‐1 radars. To address this issue, this paper presents a novel two‐layer machine learning‐based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap‐fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30‐day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two‐layer framework is validated against regional holdout SMAP/Sentinel‐1 3‐km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3‐km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R , at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33‐km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory.

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