Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
<p>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) ND...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-09-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/4181/2023/essd-15-4181-2023.pdf |
| Summary: | <p>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 <span class="inline-formula"><i>R</i><sup>2</sup></span> (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 (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.956, RMSE <span class="inline-formula">=</span> 0.048, MAE <span class="inline-formula">=</span> 0.034, and MAPE <span class="inline-formula">=</span> 6.0 %) and global
vegetation trend (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.9</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="51pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="03810a67bc0120249818a4e12ffc118e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-15-4181-2023-ie00001.svg" width="51pt" height="14pt" src="essd-15-4181-2023-ie00001.png"/></svg:svg></span></span> yr<span class="inline-formula"><sup>−1</sup></span>). 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 <a href="https://doi.org/10.5281/zenodo.8253971">https://doi.org/10.5281/zenodo.8253971</a> (Li et al., 2023).</p> |
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| ISSN: | 1866-3508 1866-3516 |