A Computationally Efficient Spatio-Temporal Fusion Model for Reflectance Data (original) (raw)

Zou, Zhaoyuan, O'Donnell, Ruth ORCID: https://orcid.org/0000-0002-3538-7511, Miller, Claire ORCID: https://orcid.org/0000-0002-1857-4454, Lee, Duncan ORCID: https://orcid.org/0000-0002-6175-6800 and Wilkie, Craig(2024) A Computationally Efficient Spatio-Temporal Fusion Model for Reflectance Data. In: Developments in Statistical Modelling, 38th International Workshop on Statistical Modelling (IWSM 2024), Durham, UK, 14-19 Jul 2024, pp. 81-87. ISBN 9783031657221(doi: 10.1007/978-3-031-65723-8_13)

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Abstract

Fusing remotely-sensed reflectance data from different sources at different spatial and temporal scales is useful to monitor lake water quality. The nonparametric statistical downscaling model (NSD) [5] can account for a change of spatial and temporal support between two remote sensors, but it is computationally demanding for large datasets. This work proposes a method to improve the computational efficiency of the NSD model by endowing it with a Gaussian predictive process. The predictive performance and computational efficiency of both models are compared through simulation and using satellite reflectance data from Lake Garda.

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