The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine
Mohseni, Farzane; Ahrari, Amirhossein; Haunert, Jan-Henrik; Montzka, Carsten (2023-09-14)
Taylor & Francis
14.09.2023
Farzane Mohseni, Amirhossein Ahrari, Jan-Henrik Haunert & Carsten Montzka (2024) The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine, Big Earth Data, 8:1, 33-57, DOI: 10.1080/20964471.2023.2257905
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© 2023 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the International Research Center of Big Data for Sustainable Development Goals, and CASEarth Strategic Priority Research Programme. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
https://creativecommons.org/licenses/by/4.0/
© 2023 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the International Research Center of Big Data for Sustainable Development Goals, and CASEarth Strategic Priority Research Programme. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403282485
https://urn.fi/URN:NBN:fi:oulu-202403282485
Tiivistelmä
Abstract
Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.
Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.
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