Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau

Zhao, Hongfei; Li, Jie; Yuan, Qiangqiang; Lin, Liupeng; Yue, Linwei; Xu, Huang

doi:10.1016/j.jhydrol.2022.127570

Cited by 32 publications

(27 citation statements)

References 64 publications

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“…Our determination procedure for the monthly optimal trend model, which is based on independent validation rather than model fitting, may support relevant studies. When the benefits of the incorporation of VP for spatial downscaling are investigated, related advanced algorithms (such as support vector machine [37], [67], random forest [24], [67], artificial neural network [1], [15], and deep learning [12], [20], [21], [22], etc.) and other spatial features which have the nature of spatial continuousness (such as land surface temperature and surface soil moisture) should also be deeply explored and validated at different spatial scales in future research.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, satellite products with a high temporal resolution are often required to be spatially downscaled (disaggregation of a coarse cell into many finer cells) for various environmental applications. In the past 10 years, numerous spatial downscaling studies have been conducted on many satellite-derived products, such as precipitation [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], soil moisture [15], [16], [17], [18], [19], [20], [21], [22], [23], land surface temperature [2], [24], [25], [26], [27], [28], [29], [30], night-time light [31], solar radiation [32], evapotranspiration [33], [34], [35], chlorophyll [36], and wind speed [37]. The primary goal of spatial downscaling research is to improve the downscaling performance of satellite-derived products which is generally performed from two main aspects [38]: the introduction of new auxiliary variables [5], [8], [39], [40] and the development of new downscaling models [6], [13], [22],…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the ''standard deviation'' operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination(R 2 ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.INDEX TERMS Variation predictor, spatial downscaling, disaggregation, unmixing, scaling, geographically weighted regression (GWR), tropical rainfall measuring mission (TRMM), precipitation, north-south transitional zone of China.The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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“…Deep learning models have limited assumptions on the data and processes that are to be modeled, and can use a large amount of information. However, they cannot be treated as a panacea for solving any task, as they can learn spurious relationships and make bad predictions outside of observed conditions, can demand a lot of computational resources (Menghani, 2023), and can sometimes be less accurate than process‐based or simpler statistical models (Gamage & Samarabandu, 2020; Korbmacher & Todeaux, 2022; Rajula et al., 2020; H. Zhao et al., 2022).…”

Section: Classification Of Lake Temperature Modelsmentioning

confidence: 99%

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Piccolroaz,

Zhu,

Ladwig

et al. 2024

Reviews of Geophysics

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Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

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“…Meanwhile, researchers have discovered that downscaling is very similar to the classical computer vision task of superresolution. Thus, some of them tried to apply the deep learning-based superresolution models to downscaling tasks, and achieved competitive results [37], [38], [39], [40]. However, differing from most superresolution tasks that performs reconstruction in an integer scale, the downscaling for temperature distribution usually reconstructs the meteorological elements in a noninteger and large scale.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Temperature Distributions in Southwest China by Using Terrain-Guided Attention Network

Liu

Zhang

Hang

et al. 2023

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

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Deep learning techniques, especially convolutional neural networks (CNNs), have dramatically boosted the performance of statistical downscaling. In this study, we propose a CNNbased 2 m air temperature downscaling model named Terrain-Guided Attention Network (TGAN), which aims at rebuilding 2 m air temperature distribution from 0.0625 • to 0.01 • over Southwest China. More concretely, TGAN utilizes two upsampling modules to progressively reconstruct the high-resolution temperature data from the low-resolution one. Then, to better recover the spatial detail of the low-resolution temperature data, an attentive-terrain block is proposed to introduce digital terrain model (DEM) information. It aggregates the temperature data and the correspondingscale DEM information via the attention mechanism in a multiscale manner. Ultimately, the reconstruction module is employed to obtain the high-resolution temperature data. We use the 2019 data for training, and utilize the 2018 data to verify the effectiveness of the proposed TGAN. The experimental results showed that TGAN achieved the lowest root-mean-square error (1.12 • C) when incorporating DEM data by attentive-terrain blocks in a multiscale manner, followed by incorporating DEM data in a multiscale manner (TGAN-land, 1.31 • C) and only incorporating DEM data (SRCNN-land, 1.36 • C). Meanwhile, TGAN showed a competitive performance when compared with several advanced deep-learningbased super-resolution algorithms and reconstructed the texture details of 2 m air temperature fields more clearly. In general, among various deep learning approaches, TGAN achieves better downscaling results for 2 m air temperature reconstruction and provides a practical method and guidance for the back-calculation of high-resolution historical meteorological grid data.

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Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau

Cited by 32 publications

References 64 publications

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Statistical Downscaling of Temperature Distributions in Southwest China by Using Terrain-Guided Attention Network

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