A novel and simple downscaling approach for generating a 250m resolution NDVI database since 1982 at global scale

Keywords: NDVI time series, MODIS, AVHRR, Google Earth Engine, data fusion
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 05:20 PM (Eastern Time (US & Canada)) - 2/26/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 1

Abstract

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to monitor vegetation health dynamics and ecological environment changes globally. However, the inconsistent spatial resolution and quality of the different satellite sensors limit the joint application of the remotely sensed NDVI datasets. The Advanced Very High-Resolution Radiometer (AVHRR) instruments on board the NOAA polar-orbiting satellites provide the only continuous NDVI time series back to the early 1980s, while they only have a coarse spatial resolution of >1km. Here we developed a novel spatio-temporal fusion method to downscale the AVHRR NDVI products to the Moderate-resolution Imaging Spectroradiometer (MODIS) resolution. The algorithm effectively combines the high spatial variability of the MODIS NDVI data and the long-term temporal information of the AVHRR NDVI data. Finally, we successfully generated a monthly global long-term (since 1982) and high-resolution (250m) NDVI database. The entire downscaling and validation processes were conducted using the Google Earth Engine platform and thus it is easy to immediately generate the data for any part of the world using this algorithm. We applied the metrics of Pearson’s correlation (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) to validate the accuracy and error of the downscaling algorithm. The evaluation suggests that the proposed fusion products can obtain high-quality NDVI observations similar as MODIS data. This long-term and high-resolution NDVI database is particularly useful for ecological applications in areas that have heterogeneous vegetation distribution.