Remote Sensing Ecological Index (RSEI) raster data for the ecological restoration belt in the southeastern karst region of Southwest China

This dataset comprises annual Remote Sensing Ecological Index (RSEI) raster data for the ecological restoration belt in the southeastern karst region of Southwest China, spanning the period from 2000 to 2022. The dataset has a spatial resolution of 500 meters and is designed to provide a comprehensive assessment of the spatiotemporal dynamics of the Ecological Environment Quality (EEQ) in the study area.
The RSEI is a composite index that integrates four key remote sensing indicators through Principal Component Analysis (PCA):
Greenness Indicator: Normalized Difference Vegetation Index (NDVI), reflecting vegetation cover.
Wetness Indicator: The wetness component (WET) from the Tasseled Cap Transformation, representing the moisture content of soil and vegetation.
Dryness Indicator: Normalized Difference Built-up and Soil Index (NDBSI), indicating the degree of surface dryness influenced by built-up areas and bare soil.
Heat Indicator: Land Surface Temperature (LST), reflecting the surface thermal environment.
All original remote sensing imagery (MOD09A1, MOD13A1, MOD11A2, MOD17A3HGF) was sourced from the National Aeronautics and Space Administration's (NASA) Earth Observing System Data and Information System (EOSDIS). This study utilized the Google Earth Engine (GEE) platform for the systematic processing of these multi-source remote sensing data. The process included image mosaicking, cloud removal, gap-filling, atmospheric correction, and topographic correction to ultimately compute the 23-year annual RSEI time-series data covering the entire study area.
This dataset is a core output of the paper titled "Diagnostic Adaptive Assessment Framework (DAAF-Karst): Revealing the Heterogeneous Mechanisms of Ecological Evolution in the Karst Region of Southwest China." It provides a critical data foundation for an in-depth analysis of the ecological restoration and degradation processes, decoupling their complex driving mechanisms, and formulating precise adaptive management strategies for this ecologically fragile region.