Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
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Published:2023-09-25
Issue:9
Volume:15
Page:4181-4203
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Li Muyi, Cao SenORCID, Zhu ZaichunORCID, Wang Zhe, Myneni Ranga B., Piao Shilong
Abstract
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 Modeling 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 global Landsat NDVI samples and a data consolidation method to
generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI
(1982–2022), based on AVHRR and Moderate-Resolution Imaging
Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples
that were 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 (0.97 over 0.94), root mean
squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over
0.07), 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 consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global
vegetation trend (0.9×10-3 yr−1). 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. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).
Funder
National Natural Science Foundation of China Shenzhen Fundamental Research Program Shenzhen Science and Technology Innovation Program
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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