Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades
Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Wei Li, Xinxin Liu, Liangpei Zhang
Abstract
Satellite normalized difference vegetation index (NDVI) time series, essential for ecological and environmental applications, is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the longest continuous time series since 1982, but with the drawback of coarse spatial resolution and poor data quality. To address this issue, a spatiotemporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals, by fusing with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. A multi-step processing fusion framework, containing temporal filtering, normalization, spatiotemporal fusion, and residual error correction, was employed to combine the superior characteristics of the two products, respectively. Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation, providing stable long-term results similar to MODIS data. We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.