Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau

Shangguan, Yulin; Min, Xiaoxiao; Shi, Zhou

doi:10.1016/j.jhydrol.2022.129014

Cited by 21 publications

(13 citation statements)

References 105 publications

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“…Our study also shows the feasibility of using ML model varies with the environmental context. Nevertheless, the BTCH method effectively integrate these models to achieve more robust estimations (Shangguan et al., 2023). Additionally, we compared BTCH with an equal weighting approach (Table S2 in Supporting Information ), affirming that BTCH delivers superior performance.…”

Section: Resultsmentioning

confidence: 99%

“…Among the ML methods we investigated, the BTCH integration method emerges as particularly promising, demonstrating its potential within model ensembles. In addition to its integration capabilities, the BTCH method excels in harmonizing data from multiple remote sensing products, thereby reducing uncertainty (J. Liu et al., 2021; Shangguan et al., 2023). Given the uncertainty associated with multisource precipitation and ET products in our study, employing the BTCH method to synthesize these data sources could further enhance the accuracy of our model.…”

Section: Discussionmentioning

confidence: 99%

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Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

Bo,

Li,

Liu

et al. 2024

Water Resources Research

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Current irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. This study proposes a hybrid framework that integrates multiple machine learning (ML) methods with theory‐guided strategies to calculate IWU for three principal cereal crops within the Chinese agricultural landscape. We generated high resolution time series data sets of evapotranspiration and surface soil moisture (SM) using remote sensing resources. ML techniques, along with the Bayesian three‐cornered hat ensemble, were employed to drive multiple remote sensing‐derived data sets in IWU calculation. We applied two theory‐guided mechanisms to quantify irrigation signals: first, converting original SM values into logarithmic terms, and second, extracting process‐based SM residuals. Proposed framework has been validated at 12 field stations across China, yielding coefficient of determination (R2) ranging from 0.54 to 0.70, and root mean square error (RMSE) spanning 278–335 mm/yr. Our framework demonstrates considerable strength in IWU estimation when compared to reported IWU values form 341 cities across China. Specifically, for rice, wheat, and maize, the R2 values range from 0.78 to 0.83, 0.68 to 0.76, and 0.53 to 0.64, respectively, with corresponding RMSE measuring 0.22–0.25, 0.10–0.12, and 0.11–0.13 km3/yr, respectively. These findings highlight the effectiveness of theory‐guided strategies in discerning irrigation‐related information, thereby improving overall model performance. Attention should be directed toward the uncertainties in evapotranspiration and precipitation products on model performance, which remained modest, with a relative change of less than 5%.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

Bo,

Li,

Liu

et al. 2024

Water Resources Research

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“…Subsequently, the chosen driving forces are input into the model at a high spatial resolution to output high-spatialresolution SM. The driving forces of SM, such as precipitation, significantly influence both the spatial and temporal variability of SM [61,98,103]. High surface temperatures can lead to increased evaporation rates, leading SM to evaporate from the near-surface layers.…”

Section: Methodological Framework Of Ml-based Downscaling Approachmentioning

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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“…Besides, the proposed model also presented certain advantages to the baseline methods (e.g., the ANN, CNN, XGBoost, LSTM and DCT-PLS model) with higher correlation and more reasonable SM patterns. The simple ANN model failed to well reconstructed SM gaps since it was sensitive to uninformative features [44], which might count for the obvious spatial noise. Even though the LSTM has been demonstrated to well predict the SM time series [45], the gap-filled SM also presented certain degrees of spatial noise.…”

Section: B Advantages and Limitations Of Proposed Modelmentioning

confidence: 99%

Gap Filling of the ESA CCI Soil Moisture Data Using a Spatiotemporal Attention-Based Residual Deep Network

Shangguan

Min

Shi

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau

Cited by 21 publications

References 105 publications

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

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

Gap Filling of the ESA CCI Soil Moisture Data Using a Spatiotemporal Attention-Based Residual Deep Network

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