A 30-m annual grassland dataset from 1991 to 2020 for Inner Mongolia, China

Abstract

Abstract Grassland is a widely distributed land use type that provides essential resources for livestock production and serves as a crucial ecosystem component. Over the past decades, grassland has significantly degraded and shrunk due to climate change and human activities, with ongoing changes in its area. This study utilized the Google Earth Engine (GEE) and the Res-UNet++ model to analyze the phenological and spectral characteristics of grasslands in Landsat images for long-term annual monitoring. Additionally, the LandTrendr algorithm was utilized to correct long-term time series data for grasslands, yielding a map of grassland distribution in Inner Mongolia from 1991 to 2020. The results indicate that the overall spatial accuracy from 1991 to 2020 exceeded 96%, with a Kappa coefficient over 0.92, demonstrating high monitoring accuracy. In particular, the 2019 grassland monitoring area showed high consistency with data from the Third National Land Survey (TNLS), with a coefficient of determination (R^2) reaching 0.97, reflecting the high accuracy and reliability.