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Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2023, Vol.16, p.4261-4275

2008-2012 IEEE ;DOI: 10.1109/JSTARS.2023.3272916

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  • Title:
    Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model
  • Author: Chen, Zhiwei ; Zheng, Wei ; Yin, Wenjie ; Li, Xiaoping ; Ma, Meihong
  • Subjects: Geographically weighted regression downscaled model (GWRDM) ; Gravity Recovery and Climate Experiment (GRACE) ; groundwater storage (GWS) ; Indexes ; Land surface ; Land surface temperature ; Northwest China ; Satellites ; spatial downscaling ; Spatial resolution ; terrestrial water storage ; Water resources ; Water storage
  • Is Part Of: Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 2023, Vol.16, p.4261-4275
  • Description: Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.
  • Publisher: IEEE
  • Language: English
  • Identifier: DOI: 10.1109/JSTARS.2023.3272916
  • Source: IEEE Open Access Journals
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