Publications related to the GRACE Missions (no abstracts)

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Uncertainty Quantification of Satellite–Based Essential Climate Variables Derived from Deep Learning

Gou, Junyang, Salberg, Arnt–Børre, Kiani Shahvandi, Mostafa, Tourian, Mohammad J., Meyer, Ulrich, Boergens, Eva, Waldeland, Anders U., Velicogna, Isabella, Dahl, Fredrik, Jäggi, Adrian, Schindler, Konrad, and Soja, Benedikt, 2026. Uncertainty Quantification of Satellite–Based Essential Climate Variables Derived from Deep Learning. Surveys in Geophysics, .

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@ARTICLE{2026SGeo..tmp...12G,
       author = {{Gou}, Junyang and {Salberg}, Arnt-B{\o}rre and {Kiani Shahvandi}, Mostafa and {Tourian}, Mohammad J. and {Meyer}, Ulrich and {Boergens}, Eva and {Waldeland}, Anders U. and {Velicogna}, Isabella and {Dahl}, Fredrik and {J{\"a}ggi}, Adrian and {Schindler}, Konrad and {Soja}, Benedikt},
        title = "{Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning}",
      journal = {Surveys in Geophysics},
     keywords = {Deep learning, Uncertainty quantification, Essential climate variables, Satellite observations, Snow cover, Terrestrial water storage},
         year = 2026,
        month = jan,
     abstract = "{Accurate uncertainty information associated with essential climate
        variables (ECVs) is crucial for reliable climate modeling and
        understanding the spatiotemporal evolution of the Earth system.
        Recent developments in deep learning have remarkably advanced
        the estimation of ECVs with improved accuracy. However, the
        quantification of uncertainties associated with outputs of such
        deep learning models has yet to be widely adopted. This survey
        explores the types of uncertainties associated with ECVs derived
        from deep learning methods, including aleatoric (data) and
        epistemic (model) uncertainty, and the techniques to quantify
        them. The focus is on highlighting the importance of considering
        uncertainty associated with inputs in the deep learning models
        to account for the dynamic and multifaceted nature of satellite
        observations. The survey starts by clarifying the definitions of
        aleatoric and epistemic uncertainties and their roles in a
        typical satellite observation processing workflow, followed by
        bridging the gap between conventional statistical and deep
        learning views on uncertainties. Then, we comprehensively review
        the existing uncertainty quantification methods for deep
        learning algorithms and discuss their strengths and limitations.
        A comprehensive literature review about quantifying
        uncertainties in the deep learning estimates of ECVs follows the
        theoretical survey, covering a wide range of ECVs. The specific
        need for modification to fit the requirements from both the
        Earth observation side and the deep learning side in such
        interdisciplinary tasks is highlighted. We further demonstrate
        our findings with two selected ECV examples, snow cover and
        terrestrial water storage, to provide clear insights into
        different methods by promoting quantitative comparison. In the
        end, we summarize our findings and provide perspectives for
        future research.}",
          doi = {10.1007/s10712-025-09919-2},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026SGeo..tmp...12G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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