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Review

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

by
Indishe P. Senanayake
1,2,
Kalani R. L. Pathira Arachchilage
1,2,
In-Young Yeo
1,2,*,
Mehdi Khaki
1,
Shin-Chan Han
1 and
Peter G. Dahlhaus
2,3
1
School of Engineering, College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia
2
Cooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, Australia
3
Centre for eResearch and Digital Innovation, Federation University, Mount Helen, VIC 3350, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(12), 2067; https://doi.org/10.3390/rs16122067
Submission received: 25 March 2024 / Revised: 30 May 2024 / Accepted: 5 June 2024 / Published: 7 June 2024
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Moisture)

Abstract

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms.
Keywords: downscaling; machine learning; microwave remote sensing; soil moisture; spatial resolution downscaling; machine learning; microwave remote sensing; soil moisture; spatial resolution

Share and Cite

MDPI and ACS Style

Senanayake, I.P.; Pathira Arachchilage, K.R.L.; Yeo, I.-Y.; Khaki, M.; Han, S.-C.; Dahlhaus, P.G. Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review. Remote Sens. 2024, 16, 2067. https://doi.org/10.3390/rs16122067

AMA Style

Senanayake IP, Pathira Arachchilage KRL, Yeo I-Y, Khaki M, Han S-C, Dahlhaus PG. Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review. Remote Sensing. 2024; 16(12):2067. https://doi.org/10.3390/rs16122067

Chicago/Turabian Style

Senanayake, Indishe P., Kalani R. L. Pathira Arachchilage, In-Young Yeo, Mehdi Khaki, Shin-Chan Han, and Peter G. Dahlhaus. 2024. "Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review" Remote Sensing 16, no. 12: 2067. https://doi.org/10.3390/rs16122067

APA Style

Senanayake, I. P., Pathira Arachchilage, K. R. L., Yeo, I.-Y., Khaki, M., Han, S.-C., & Dahlhaus, P. G. (2024). Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review. Remote Sensing, 16(12), 2067. https://doi.org/10.3390/rs16122067

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