Abstract
The Tibetan Plateau (TP) is an important component of the global environmental system, on which the snow cover greatly affects the regional climate and ecology. Moderate resolution imaging spectroradiometer (MODIS) snow cover products have been demonstrated to be appropriate for investigating the snow cover over the TP. However, they are subject to cloud obscuration, and the TP’s extremely complex terrain makes the snow monitoring difficult. Therefore, in this paper, we propose a two-stage spatio–temporal fusion framework for the cloud removal of MODIS C6 snow products, including an adjusted Terra and Aqua combination (TAC) and a spatio–temporal fusion based on Gaussian kernel function and error correction (STF-GKF-EC). To the best of our knowledge, this is the first time that a spatio–temporally continuous daily 500-m MODIS normalized difference snow index (NDSI) product has been generated for the TP, which greatly improves the spatial and temporal resolutions of the current snow cover products. The main stage, STF-GKF-EC, adaptively weights the spatial and temporal correlations by the Gaussian kernel function, and further takes the rapid changes of snow cover into consideration through the error correction. The experiments indicated that STF-GKF-EC removes clouds completely, achieving an overall accuracy (OA) and mean absolute error (MAE) of 91.48% and 3.88, respectively. Based on the cloud-removed results, during 2001–2017, as far as the intra-annual variation is concerned, a large proportion of the snow cover appears between October and May, with a peak in February/March, and the variation is mainly controlled by temperature. For the inter-annual variation, an obvious increasing trend of 0.68/year for NDSI is observed before 2005, followed by a slight decreasing trend of 0.16/year, in which precipitation is a better explanation factor than temperature.
Highlights
As the “third pole of the planet”, the Tibetan Plateau (TP) has a considerable impact on regional climate and ecology
When the snow melts early, the peak of river runoff will shift from summer and autumn, when water demand is highest, to winter and spring, when water demand is lower [5], which will seriously affect the development of agriculture and the supply of municipal water in the downstream areas
Optical snow products are widely applied in hydrological research, among which the moderate resolution imaging spectroradiometer (MODIS) snow products are popular [14]
Summary
As the “third pole of the planet”, the Tibetan Plateau (TP) has a considerable impact on regional climate and ecology. From a methodological perspective, the algorithms for removing the clouds of MODIS snow products can be mainly classified as temporal filtering methods, spatial filtering methods, multi-source fusion methods, and spatio–temporal union methods. It has been demonstrated that the multi-source fusion methods can completely remove the clouds, but the accuracy relies on the complementarity and accuracy of the input data [14] These methods perform well in binary snow cover mapping, but are likely to lose details due to the great differences in characteristics and spatial resolution between the different types of products. A better practice for producing cloud-free snow cover products is to integrate the spatial and temporal information of optical-based products Their union can remove clouds completely, with a higher accuracy than temporal filtering and spatial filtering [31,32].
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