A dataset of NDVI for the vegetation growing season in Central Asia with a resolution of 250 m(2001-2020)and 30 m(2020)
Central Asia is the largest arid and semi-arid region in the Northern Hemisphere,and its ecological environment is extremely fragile and susceptible to the effects of global climate change.Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its strategic geographic location.Vegetation serves as a significant indicator of the ecological environment,and its spatial and temporal distribution pattern,along with changing trends,are important indicators for assessing the ecological status of the region.The Normalized Difference Vegetation Index(NDVI)is a commonly used remote sensing index to study vegetation,which characterizes the spatio-temporal changes of vegetation.In this dataset,we used MODIS13Q1 to generate a long-term time series of mean NDVI data for the growing season with a spatial resolution of 250m in Central Asia from 2001 to 2020.To obtain the mean NDVI data for the growing season with a higher spatial resolution of 30m in 2020,we applied the Cubist algorithm based on rule segmentation regression,to fuse Landsat data and MODIS data.Meanwhile,this dataset has undergone rigorous quality control through three aspects:data source quality control,model training optimization,and model independent verification,ensuring the accuracy and reliability of the data.The dataset is expected to provide powerful data support for analyzing vegetation dynamics and spatial pattern in Central Asia.
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