@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2026RemS...18.2702X,
       author = {{Xu}, Keke and {Zhu}, Yongzhen and {Liu}, Xianglei and {Zheng}, Wei and {Li}, Huanxu and {Zhao}, Jiaqi and {Chen}, Mengchao},
        title = "{Temporal Gap Filling and Model-Based Spatial Downscaling of GRACE-Based Groundwater-Storage Anomalies Using Gaussian Process and Random Forest Models}",
      journal = {Remote Sensing},
     keywords = {groundwater-storage anomalies, GRACE, groundwater depletion, machine learning, North China},
         year = 2026,
        month = aug,
       volume = {18},
       number = {16},
          eid = {2702},
        pages = {2702},
     abstract = "{What are the main findings? A sequential Gaussian Process--Random Forest
        workflow was used for temporal gap filling and model-based
        spatial downscaling of GRACE-based groundwater-storage
        anomalies. The resulting 1 km gridded estimates showed temporal
        agreement with groundwater-level anomalies (R = 0.88) and
        identified persistent groundwater depletion in northern Henan. A
        sequential Gaussian Process--Random Forest workflow was used for
        temporal gap filling and model-based spatial downscaling of
        GRACE-based groundwater-storage anomalies. The resulting 1 km
        gridded estimates showed temporal agreement with groundwater-
        level anomalies (R = 0.88) and identified persistent groundwater
        depletion in northern Henan. What are the implications of the
        main findings? The workflow improves the temporal continuity and
        spatial representation of GRACE-based groundwater-storage
        estimates without implying independently observed groundwater
        information at the 1 km scale. The approach is potentially
        applicable to other regions, subject to local calibration,
        hydrogeological conditions, and validation with independent
        observations. The workflow improves the temporal continuity and
        spatial representation of GRACE-based groundwater-storage
        estimates without implying independently observed groundwater
        information at the 1 km scale. The approach is potentially
        applicable to other regions, subject to local calibration,
        hydrogeological conditions, and validation with independent
        observations. Groundwater-storage anomalies (GWSA) derived from
        the Gravity Recovery and Climate Experiment (GRACE) mission
        provide valuable information for regional groundwater
        monitoring. Improving the spatial representation and temporal
        continuity of GRACE-derived GWSA is important for supporting
        groundwater assessment at subregional scales. A sequential
        framework combining Gaussian Process (GP) temporal gap filling
        and Random Forest (RF) spatial downscaling was developed for
        GWSA reconstruction in Henan Province, China, during 2002--2022.
        The GP model was used to reconstruct missing observations and
        characterize temporal variations, while the RF model
        statistically redistributed the GRACE-based GWSA signal using
        multi-source hydroclimatic predictors. The resulting dataset
        comprises model-derived GWSA estimates on a 1 km output grid
        constrained by the coarse spatial support of GRACE observations
        and the relationships learned from the auxiliary variables.
        Therefore, the 1 km grid spacing should not be interpreted as an
        independent 1 km resolving capability for groundwater-storage
        variations. Agreement with the parent GRACE-based GWSA product
        was used to assess coarse-scale reconstruction consistency
        rather than independent fine-scale accuracy. Comparison with
        groundwater-level anomalies from 63 monitoring wells yielded a
        correlation coefficient of 0.88, indicating temporal agreement
        at the sampled locations. Because the groundwater-level
        observations were not converted into storage anomalies using
        specific yield, this comparison does not establish absolute GWSA
        accuracy or independently validate the model-derived fine-scale
        spatial patterns. The reconstructed estimates revealed
        pronounced spatial heterogeneity in groundwater-storage changes,
        with persistent depletion concentrated in northern Henan, where
        groundwater decline rates exceeded 20 mm yr$^{-1}$. Overall, the
        framework improved the temporal continuity and spatial
        representation of GRACE-based groundwater-storage estimates
        while retaining the fundamental spatial constraints of satellite
        gravimetry. The results demonstrate the potential of integrating
        GRACE observations, machine learning, and multi-source Earth
        observation data to support regional groundwater assessment.}",
          doi = {10.3390/rs18162702},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2702X},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}