https://orcid.org/0000-0002-1541-1393
![Sample our Geography journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5937/TOC-Subject-Sample-2021-Geography.jpg"})
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.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All satellite data and products supporting the methodology and findings of this paper (including MODIS and SM Products) have been archived in GEE and are openly available at https://developers.google.com/earth437engine/datasets/catalog/. The field samples of SM over three validation sites, which were used in this study to validate the proposed method, were collected using the ISMN and are available at https://ismn.earth/.
Supplemental data
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20964471.2023.2257905.
Additional information
Funding
Notes on contributors
Farzane Mohseni
Dr. Ing. Farzane Mohseni is a scientific researcher at the Institute of Geodesy and Geoinformation (Working group Geoinformation) at the University of Bonn since December 2022. Her research interests include soil hydrology, groundwater estimation, water resource management, disaggregation of coarse-scale radiometric soil moisture products, and land cover mapping. She received the B.Sc. degrees in geodesy and geomatics in 2015, the M.Sc. degree in remote sensing in 2017, and a doctoral degree in remote sensing in 2022 from the K. N. Toosi University of Technology, Iran. Prior to assuming her current position, she had been a visiting researcher at the University of Lund, Sweden.
Amirhossein Ahrari
M.Sc. Amirhossein Ahrari holds a position as a PhD researcher in Environmental Engineering, at the University of Oulu, Finland. His primary research focused on hydrological data mining utilizing data from earth observation satellites. With a strong emphasis on enhancing accuracy of hydrological applications, he employs advanced machine learning algorithms to extract valuable insights from satellite data. Prior to his current role, he achieved his Bachelor’s (2015) and Master’s (2018) degrees in Remote Sensing & GIS with specialization in soil and water studies from the University of Tehran, Iran.
Jan-Henrik Haunert
Prof. Dr. Ing. Jan-Henrik Haunert is a full professor of geoinformation at the University of Bonn, Germany. His research is concerned with the development of efficient algorithms for geovisualization and spatial data analytics. In particular, he applies methods from combinatorial optimization and computational geometry to tasks of automated cartography, such as the aggregation and selection of objects in multi-scale digital landscape models. Before he took up his current position, he had acquired a diploma and doctoral degree in geodesy and geoinformatics from the University of Hannover, Germany, had been a postdoctoral researcher at the institute of computer science at the University of Würzburg, Germany, and a professor of geoinformatics at the University of Osnabrück, Germany.
Carsten Montzka
Dr. Carsten Montzka received the Ph.D. degree in geography from the University of Bonn, Germany, in 2007, for the integration of multispectral remote sensing data into nitrogen cycle simulations. In 2004, he joined the Institute of Bio- and Geosciences: Agrosphere (IBG-3) of the Forschungszentrum Jülich, Jülich, Germany. Since 2007, he has been working on passive microwave retrieval for soil moisture and related validation activities. His current work has mainly been focused on the development of multiscale soil moisture data assimilation techniques, airborne active/passive microwave campaigns to support new microwave-based soil moisture missions, and multi-sensor combination on Unmanned Aerial Vehicles (thermal, multispectral, LiDAR). He is alumnus of the Arab-German Young Academy for Sciences and Humanities (AGYA), Berlin, Germany, and is soil moisture focus area co-lead of the Committee on Earth Observation Satellites (CEOS) Land Parameter Validation subgroup.