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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 3 of this Preprint.


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Hanzi Mao, Dhruva Kathuria, Nicholas Duffield, Binayak P. Mohanty


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 hold-out 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 higher Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and lower unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean ubRMSE) when compared to the SMAP 33 km soil moisture product. Additional validation against airborne data and in-situ data from soil moisture networks is also satisfactory.



Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Hydrology, Physical Sciences and Mathematics, Water Resource Management


machine learning, soil moisture, Multi-Resolution Gap Filling, Sentinel-1 satellite, SMAP satellite, Spatial/Temporal Transfer Learning


Published: 2019-02-11 21:41

Last Updated: 2019-08-20 03:01

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Academic Free License (AFL) 3.0

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