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

被引:4
|
作者
Senanayake, Indishe P. [1 ,2 ]
Arachchilage, Kalani R. L. Pathira [1 ,2 ]
Yeo, In-Young [1 ,2 ]
Khaki, Mehdi [1 ]
Han, Shin-Chan [1 ]
Dahlhaus, Peter G. [2 ,3 ]
机构
[1] Univ Newcastle, Coll Engn Sci & Environm, Sch Engn, Callaghan, NSW 2308, Australia
[2] Cooperat Res Ctr High Performance Soils, Callaghan, NSW 2308, Australia
[3] Federat Univ, Ctr Eres & Digital Innovat, Mt Helen, Vic 3350, Australia
关键词
downscaling; machine learning; microwave remote sensing; soil moisture; spatial resolution; LOGISTIC-REGRESSION; CLIMATE-CHANGE; ATMOSPHERE INTERACTIONS; SURFACE-TEMPERATURE; LINEAR-REGRESSION; NEURAL-NETWORKS; ENERGY FLUXES; RESOLUTION; PATTERNS; VARIABILITY;
D O I
10.3390/rs16122067
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:29
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