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Article

A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine

1
Institute for Earth Observation, Eurac Research, 39100 Bolzano, Italy
2
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Lefei Zhang
Remote Sens. 2021, 13(11), 2099; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112099
Received: 7 May 2021 / Revised: 20 May 2021 / Accepted: 22 May 2021 / Published: 27 May 2021
(This article belongs to the Section AI Remote Sensing)
Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m3·m−3 and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets. View Full-Text
Keywords: soil moisture; Sentinel-1 SAR; Landsat-8 optical/thermal data; machine learning; cloud-based approach; Google Earth Engine soil moisture; Sentinel-1 SAR; Landsat-8 optical/thermal data; machine learning; cloud-based approach; Google Earth Engine
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MDPI and ACS Style

Greifeneder, F.; Notarnicola, C.; Wagner, W. A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine. Remote Sens. 2021, 13, 2099. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112099

AMA Style

Greifeneder F, Notarnicola C, Wagner W. A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine. Remote Sensing. 2021; 13(11):2099. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112099

Chicago/Turabian Style

Greifeneder, Felix, Claudia Notarnicola, and Wolfgang Wagner. 2021. "A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine" Remote Sensing 13, no. 11: 2099. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112099

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