A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method

Shangguan, Yulin; Min, Xiaoxiao; Wang, Nan; Tong, Cheng; Shi, Zhou

doi:10.1080/15481603.2023.2290337

Cited by 6 publications

(2 citation statements)

References 84 publications

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“…However, it was evident that the TC-based merging was more efficient than the mathematical averaging, especially when there were large differences between the parent products. Shangguan et al [132] used an ML-based downscaling and merging process to generate a long-term high-accuracy SM dataset with a good accuracy, with r = 0.52 and ubRMSE = 0.047 m 3 /m 3 , by using CNN, ANN, XGB, LSTM, and ResNet. This study further highlights the importance of using short-wave-infrared-band-derived SM indices due to their sensitivity to SM.…”

Section: Improvements Made To Ml-based Sm Downscalingmentioning

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: Improvements Made To Ml-based Sm Downscalingmentioning

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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“…This makes them less suitable for agricultural, hydrological, and environmental applications requiring daily and high spatial detail information (Vergopolan et al, 2021). Several methods have been proposed to enhance the spatial resolution of remote soil moisture estimates through a process called "downscaling" (Abbaszadeh et al, 2019;Bai et al, 2019;Cui et al, 2019;Fang et al, 2019;Guevara & Vargas, 2019;Hernandez-Sanchez et al, 2020;Liu et al, 2020;Mao et al, 2019;Montzka et al, 2020;Peng et al, 2017;Shangguan et al, 2024;Sishah et al, 2023;Xu et al, 2024;Zhu et al, 2023). Recently, machine learning techniques such as random forest (Hengl et al, 2018) have achieved advancements in the downscaling of remote soil moisture estimates, either spatially (Bai et al, 2019;Chen et al, 2019;Zappa et al, 2019;Zhao et al, 2018) or temporally (Lu et al, 2015;Mao et al, 2019;Xing et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Bueno Dueñas,

Baca García,

Montoya

et al. 2024

Sci. agropecu.

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Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

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High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

Fu,

Zhang,

Guo

et al. 2024

Agricultural and Forest Meteorology

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A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method

Cited by 6 publications

References 84 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

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

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