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This is a Preprint and has not been peer reviewed.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: https://doi.org/10.1029/2019WR024902.

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Authors

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

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 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.

DOI

https://doi.org/10.31223/osf.io/ce865

Subjects

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

Keywords

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

Dates

Published: 2019-02-11 17:11

Last Updated: 2019-08-19 23:31

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License

Academic Free License (AFL) 3.0

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