Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product

Karami, Ayoob; Moradi, Hamid Reza; Mousivand, Alijafar; Dijk, Albert I. J. M. van; Renzullo, Luigi J.

doi:10.1080/01431161.2021.2022239

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Data gaps due to cloud cover are one of the limitations of using visible-and infraredimage-based ancillary variables. Karami et al [137] overcame this issue by combining Sentinel-1 radar backscatter with geophysical auxiliary variables in an RF model. Even though there was still a gap of 6 to 12 days to address due to the temporal resolution of the Sentinel-1 data, spatially, the results showed a satisfactory performance.…”

Section: Ensemble-method-based Downscaling Approachesmentioning

confidence: 99%

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

Senanayake,

Pathira Arachchilage,

Yeo

et al. 2024

Remote Sensing

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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.

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Section: Ensemble-method-based Downscaling Approachesmentioning

confidence: 99%

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

Senanayake,

Pathira Arachchilage,

Yeo

et al. 2024

Remote Sensing

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High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC)

Moosavi

Karami

Aliramaee

2023

Science of The Total Environment

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A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications

2022

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Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest future directions for using ensemble algorithms in practical applications.

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Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product

Cited by 3 publications

References 49 publications

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

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

High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC)

A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications

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