This dataset utilizes a spatiotemporal adaptive fusion method (STAR) that comprehensively considers spatiotemporal background information to generate a cloud free Terra Aqua MODIS NDSI dataset from China over the past 20 years. STAR NDSI data generally has the following advantages: (1) a continuous 20-year dataset, which is the shortest period of long-term hydrological and climatic datasets. (2) The cloud free dataset can accurately estimate snow dynamics, which is consistent with the depth of snow in situ and the height of NDSI satellite maps. In addition, STAR NDSI collection eliminates cloud pollution, greatly improving the overall performance of the TAC NDSI dataset. Therefore, this dataset can serve as a foundational dataset for hydrological and climate modeling, used to explore various key environmental issues.
The MOD10A1 and MYD10A1 datasets can be accessed through the NASA website(NASA: ttps://search.earthdata.nasa.gov/ ）Obtain
This data is based on an advanced STAR method that comprehensively utilizes spatiotemporal background information and can completely remove cloud layers. This method consists of two steps: Spatiotemporal Adaptive Fusion (STAF) and Error Correction (EC). Generate new NDSI maps, including spatial partitioning, adaptive spatiotemporal block determination, and fusion based on Gaussian kernel function (GKF). Considering the spatial heterogeneity of snow cover patterns, the study area will first be divided into ten zones. In this way, subsequent processing can be carried out on the basis of partitioning. In addition, the optimal query partition (Q) for each target partition (T) is determined by comprehensively considering the time distance (t), regional correlation (r), and cloud free score (f) in terms of the temporal complexity of snow changes.
For areas with rapidly changing and fluctuating snow cover, time background reference is likely to introduce erroneous information and amplify errors during the iteration process. This dataset adopts post-processing methods to reduce the "disorder" phenomenon in quality assurance maps. Firstly, manually determine the NDSI map with the most consistent snow cover pattern between adjacent times as a reference. Subsequently, the above-mentioned EC technology is applied to improve the spatial consistency between the post-processing area and the original area. Finally, update the quality assurance map.
Optical remote sensing images are severely polluted by clouds, and the MODIS NDSI dataset cannot accurately reflect daily snow accumulation and melting. Based on this, a two-stage spatiotemporal fusion method called STAR is proposed to generate spatiotemporal continuous snow collection. The generation process includes preprocessing TAC and key processing STAR. Provide a quality assessment (QA) method to provide users with data reliability files. On this basis, post-processing is used to further improve the data quality of individual abnormal areas.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB4016.2023
10.5281/zenodo.5644386
