A dataset of NDVI with medium to high spatio-temporal resolutions in Tajikistan using spatio-temporal data fusion method(2010-2020)
The Normalized Difference Vegetation Index(NDVI)is one of the most commonly used remote sensing indices to study the vegetation.Long-term NDVI data are of great significance to the study of vegetation changes.However,due to sensor limitations,remote sensing data cannot obtain both high temporal resolution and high spatial resolution simultaneously.Therefore,among the widely used NDVI data products,the data with spatio-temporal resolution remains scarce.To address this,we used the Cubist model to integrate MODIS data with Landsat and Sentinel remote sensing data,and obtained a dataset of NDVI with medium to high spatio-temporal resolutions in Tajikistan using spatio-temporal data fusion method from 2010 to 2020.In order to ensure the accuracy and reliability of the data,we carried out quality control of data products through three aspects,namely data sources,model training optimization,and model independent verification,and have achieved good verification results.This dataset can reflect the spatio-temporal changes of NDVI in Tajikistan from 2010 to 2020,offering long-term data support for vegetation change analysis and ecological environment monitoring in Tajikistan.
Normalized Difference Vegetation IndexTajikistanspatio-temporal data fusion methodRemote sensing product
万方数据
维普
