Abstract
Cloud contamination has largely limited the application of the Moderate Resolution Imaging Spectroradiometer(MODIS) normalized difference snow index (NDSI). Here, a novel gap-filling method based on spatial-temporal similar pixel interpolation was proposed to remove cloud occlusions in MODIS NDSI products. First, the widely used Terra and Aqua combination and three-day temporal filter methods were applied. The remaining missing NDSI information was estimated by using similar eligible pixels in the remaining cloud-free portion of a target image through a spatial-temporal similar pixel selecting algorithm (SPSA). The MODIS daily NDSI product data from 2003 to 2018 in the Qinghai–Tibetan Plateau (China) was used as a case study. The results demonstrate that the three-step methodology can generate almost completely cloud-free, daily MODIS NDSI images, reducing the cloud-gap fraction from >45% to less than 1.5% on average. The validation results of the SPSA method exhibited a high accuracy, with a high R2 exceeding 0.78, a low mean absolute error of 2.77%, a root mean square error of 3.78%, and a 96.92% overall accuracy. The proposed method can fill cloud gaps without a significant loss of accuracy, especially during snow cover transition periods (autumn and spring), which may provide more accurate cloud-free NDSI data for climate change and energy balance studies.
Highlights
As an integral part of the Earth’s climate system, seasonal snow is one of the most variable land cover types throughout the year and has a strong impact on the radiation and energy balance, hydrological and biogeochemical cycles, and even human activities [1,2]
MODIS normalized difference snow index (NDSI) Products Two versions of MODIS daily snow products have been widely used in most studies, i.e., version
In version 5, binary and fractional snow cover are provided, both of which are calculated based on the NDSI
Summary
As an integral part of the Earth’s climate system, seasonal snow is one of the most variable land cover types throughout the year and has a strong impact on the radiation and energy balance, hydrological and biogeochemical cycles, and even human activities [1,2]. We propose a novel gap-filling method based on non-local spatiotemporal similar pixels and conditional probabilities to eliminate cloud occlusion in daily MODIS NDSI products This algorithm first determines the possible NDSI range of cloudy pixels through spatial similarity, after which, the temporal similarity is used as an index to select similar pixels in the cloud-free areas to fill the cloud-covered area in the MODIS NDSI products. Our study focuses on the following three goals: (1) a comprehensive assessment of cloud contamination severity in MODIS NDSI products (version 6) across the Qinghai–Tibetan Plateau (TP); (2) an evaluation of the potential applicability of existing widely-used binary product-based cloud removal methods to continuous NDSI products; and (3) the development and accuracy assessment of an innovative cloud removal algorithm based on spatiotemporal pixel similarity
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