Journal Article

A Global 250-m Downscaled NDVI Product from 1982 to 2018

Ma, Zhimin, Dong, Chunyu, Yan, Yu, Chen, Xiaohong, Luo, Jianfeng, Lin, Kairong, Jiang, Dingshen

2022

Overview

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

• Publisher: MDPI AG
• Subject: Algorithms / AVHRR / Changing environments / Climate change / Coefficient of variation / Correlation coefficient / Correlation coefficients / Datasets / Deep learning / downscaling / Ecosystems / Environmental changes / GEE / Global climate / Heterogeneity / High resolution / Internet / Machine learning / Methods / MODIS / Monitoring / NDVI / Neural networks / normalized difference vegetation index / Normalized difference vegetative index / Precipitation / Remote sensing / Root-mean-square errors / Satellites / Sensors / Spatial data / Spatial discrimination / Spatial resolution / Spectroradiometers / Terrestrial ecosystems / Time series / time series analysis / Vegetation