Development of long-term spatiotemporal continuous NDVI products for alpine grassland from 1982 to 2020 in the Qinghai–Tibet Plateau, China

Abstract

Background: The time-series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.

Methods: In this study, a long time-series monthly NDVI data set with a spatial resolution of 250m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High-Resolution Radiometer) time-series NDVI products using the Random Forest (RF) downscaling model.

Results: Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R² values mostly above 0.7.

Conclusions: The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.

Keywords

alpine grassland / long term / machine learning / NDVI

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