Pixel-wise parameter assignment in LandTrendr algorithm: Enhancing cropland abandonment monitoring using satellite-based NDVI time-series

•NDVI-based magnitude used to detect cropland abandonment in Inner Mongolia.•LandTrendr’s value parameter assigned for dynamic magnitude analysis.•Accuracy of 82.02 % in detecting cropland disturbances in Inner Mongolia.•Methodology for land use change detection with wide applicability. Effective global agricultural land management is crucial for ensuring food security amidst rapid population growth, especially in Northern China’s semi-arid and desert regions, where uncontrolled fallowing has led to increased cropland abandonment. Traditional remote sensing methods often face accuracy challenges in these harsh climatic conditions. This study introduces a novel approach to enhance the Normalized Difference Vegetation Index (NDVI) by reconstructing the magnitude image using ground-truth samples and regional-scale Vegetation Health Index (VHI) data. This allows for flexible, pixel-level parameter assignment to detect cropland transitions more accurately. Rather than relying on single-value assessments to differentiate between active and inactive croplands, we employ pixel-wise magnitude images, integrated into the LandTrendr algorithm for trajectory-based change detection, to better address the variability of large, diverse agricultural regions. Tested on the Google Earth Engine (GEE) platform in Inner Mongolia—a representative arid and semi-arid region of Northern China—our method identified the year of cropland abandonment with 82.02 % accuracy and showed a correlation (R2) of 0.6065 between observed and actual abandonment durations. This research extends the applicability of the LandTrendr algorithm, offering a robust solution for optimizing land use change detection across regions with significant climatic variation.