Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

Long, Di; Bai, Lanqiang; Yan, Liping; Zhang, Caijin; Yang, Wenting; Lei, Huimin; Quan, Jinling; Meng, Xianyong; Shi, Chunxiang

doi:10.1016/j.rse.2019.111364

Cited by 152 publications

(88 citation statements)

References 83 publications

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“…In spatial dimension, the invalid land areas and adjacent valid land areas show spatial consistency and spatial correlation for daily soil moisture products (Long et al, 2020). In temporal dimensions, the daily time-series changing curve of the same point natively appears with continuous and smooth peculiarities (Chan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Zhang

Yuan

et al. 2021

Earth Syst. Sci. Data

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Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 %–80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).

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Section: Introductionmentioning

confidence: 99%

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Zhang

Yuan

et al. 2021

Earth Syst. Sci. Data

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“…The variable importance (VI) value ranking makes the RF model easily interpretable, and because of its good and robust performance, the RF algorithm are increasingly used in geoscientific models and feature selections. Successful stories in evaluating important variables for modeling surface temperature (Hutengs & Vohland, ), soil moisture (Long et al, ), and groundwater (Rahmati et al, ) have been reported. The basic learning process of RF can be briefly depicted as follows (Breiman, ): For a basin, the total training set is a matrix of N × M .…”

Section: Methodsmentioning

confidence: 99%

Understanding Terrestrial Water Storage Declining Trends in the Yellow River Basin

et al. 2019

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Under dual impacts from climate change and human activities, the Yellow River Basin (YRB) of China suffers from droughts and water scarcity. Understanding variations of terrestrial water storage (TWS) over the YRB is significant and beneficial to regional water resources management and sustainable development. This study investigates TWS variations in the YRB using data sets from two solutions (RL05 and RL06) of the Gravity Recovery and Climate Experiment (GRACE) satellites, as well as from global land surface models (the NOAH model and Catchment Land Surface Model [CLSM]) and hydrology and water resources model (PCR‐GLOBWB). Annual terrestrial water storage anomalies (TWSA) variation patterns were tracked by introducing a weighed centroid analysis concept. Human water use was analyzed, and its importance to TWS changes was evaluated by using the random forest algorithm. Conclusions can be briefly summarized as follows: (1) The declining trend of TWS in the YRB from the RL06 solution is more negative than that from RL05; the TWS trend from the PCR‐GLOBWB model is more consistent with GRACE than with the NOAH model and CLSM at the basin level, and the three models all underestimate the declining trend of TWS in the midstream relative to the GRACE solutions. (2) Spatial weighed centroids of annual GRACE TWSA moved toward the headwater of the YRB during the 2003–2015 period, suggesting a wide gap of TWS between the upstream and downstream YRB; the TWSA time series well correlate with the long‐term accumulation of climatic water balance, but the connections became weak in the 2010–2015 period. (3) Groundwater withdrawals have been controlled according to the Water Resources Bulletin, but the stress may be partly shifted to surface water; the increasing trend of ecological and industrial surface water use is significant, following the agriculture water use. (4) For the entire YRB, groundwater use accounts for the majority of feature importance in modeling TWSA time series, and the contribution of climate factors is the least. Agricultural water use ranks first relative to other sectors for the YRB, followed by the ecological and industrial use. This study provides a first comparison of TWSA between the latest Center for Space Research (CSR) GRACE RL06 solution and the previous RL05 solution in the YRB. The results are expected to present a comprehensive picture of TWS variations in the YRB and the impacts from climate and human factors.

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“…The correlation between precipitation and soil moisture spatial and temporal patterns has been observed by many studies [2,8]. Since precipitation datasets are of higher spatial resolution, it has been used in the process of downscaling coarse resolution soil moisture [32,39].…”

Section: Nws Precipitation Datamentioning

confidence: 99%

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

et al. 2020

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ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R 2 and MAE of 0.0518 m 3 /m 3 , 0.7312 and 0.0374 m 3 /m 3 , respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

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Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

Cited by 152 publications

References 83 publications

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Understanding Terrestrial Water Storage Declining Trends in the Yellow River Basin

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

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