Preprints
https://doi.org/10.5194/essd-2023-1
https://doi.org/10.5194/essd-2023-1
21 Feb 2023
 | 21 Feb 2023
Status: this preprint is currently under review for the journal ESSD.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020

Muyi Li, Sen Cao, and Zaichun Zhu

Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality and global-wide Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982−2020), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.975 over 0.942), mean absolute error (MAE: 0.033 over 0.074), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high temporal consistency with MODIS NDVI in describing vegetation trends (R2 = 0.962, MAE = 0.032, and MAPE = 6.5 %). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters.

Muyi Li et al.

Status: open (until 24 Apr 2023)

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Muyi Li et al.

Data sets

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020 Muyi Li, Sen Cao, and Zaichun Zhu https://zenodo.org/record/7441559#.Y7J7y3ZByCo

Muyi Li et al.

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Short summary
The long-term global NDVI products are basis to understand vegetation changes under dramatic global environmental changes. This study generated a spatiotemporally consistent NDVI product (PKU GIMMS NDVI) (1982−2020) via massive Landsat samples. Our product effectively eliminates the evident orbital drift and sensor degradation effects in AVHRR data. It has a higher accuracy than its predecessor (GIMMS NDVI3g) and has a high temporal consistency with MODIS NDVI in describing vegetation trends.