DOI: 10.5061/dryad.v9s4mw74h
title: eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI

description: Gross Primary Productivity (GPP) represents the cumulative amount of
 carbon dioxide (CO2) assimilated by green plants through photosynthesis at
 specific time intervals and spatial scales. It is the main component of
 the carbon exchange between the terrestrial biosphere and the atmosphere,
 and has a major influence on global climate and terrestrial ecosystem
 functioning. Over the last two decades, the continuous and reliable
 collection of global land surface variables by EOS-MODIS, and the parallel
 development of the eddy-covariance flux tower network (FLUXNET) have
 enabled the integration of MODIS observations with tower measurements for
 the calibration and validation of remote sensing models to obtain global
 GPP estimates. Despite the significant progress and success to date,
 current remote sensing GPP models based on the light use efficiency (LUE)
 concept share several limitations, including the difficulty in accurately
 predicting LUE variability and the associated use of land cover maps and
 look-up tables for biome specific maximum LUE, further down-regulated by
 coarse resolution interpolated meteorological data, which introduce
 significant uncertainties in the predicted GPP. To address the above
 limitations, here we applied a simple yet ecologically sound remote
 sensing GPP model based on the ecosystem light use efficiency (eLUE)
 concept, using the more than two decades of global MODIS Enhanced
 Vegetation Index (EVI) product and the publicly available FLUXDATA2015
 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from
 February 2000 to December 2024. Cross-validation with 120 flux tower sites
 (952.66 site/year) showed favorable accuracy of eLUE-GPP (hereafter
 GPPeLUE) (R2 = 0.74, RMSE = 2.05 g C m-2 d-1). The uncertainty associated
 with GPPeLUE is comparatively lower than that of the other global GPP
 datasets (MOD17,  VPM, among others). We have also calculated the
 uncertainty analytically for each GPP estimate based on the law of error
 propagation, which allows quantification of the error budget in
 applications such as Earth system model benchmarking and atmospheric
 inversion. Our estimate of global total annual GPP, averaged over the
 period 2001-2024, was 135.53±11.03 Pg C yr-1. Furthermore, we found a
 significant increasing trend in global total annual GPP at a rate of
 0.26±0.06 Pg C yr-1 (p &lt; 0.001) from 2001 to 2024, particularly in
 eastern Asia, northern India, Europe, eastern North America, and central
 South America. We expect that the eLUE-GPP product will enable a more
 accurate diagnostic analysis of the global carbon budget and thus
 contribute to climate change research.
description: The eLUE Gross Primary Productivity (eLUE-GPP) is based on a
 simple yet ecologically sound ecosystem light use efficiency (eLUE) GPP
 model, using the more than two decades of global MODIS Enhanced Vegetation
 Index (EVI) product and the publicly available FLUXDATA2015 dataset, to
 generate a global GPP product (eLUE-GPP) from February 2000 to December
 2024. The eLUE-GPP is available in both global and site scales and at
 various temporal and spatial resolutions. 
description: # eLUE-GPP (MODIS): A Global Gross Primary Productivity Product based on
 ecosystem light-use-efficiency model and MODIS EVI
 [https://doi.org/10.5061/dryad.v9s4mw74h](https://doi.org/10.5061/dryad.v9s4mw74h) The eLUE Gross Primary Productivity (eLUE-GPP) is based on a simple yet ecologically sound ecosystem light use efficiency (eLUE) GPP model, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global GPP product (eLUE-GPP) from February 2000 to December 2024. The eLUE-GPP is available in both global and site scales and at various temporal and spatial resolutions. ## Description of the data and file structure The eLUE-GPP product has two Science Datasets, comprising GPP and GPP uncertainty. To reduce data storage size, the original values have been adjusted to integers. Users are advised to refer to the scaling factors for value restoration when using the data. The eLUE-GPP dataset provide three subsets at different spatio-temporal resolutions suitable for global and local applications. First, for global applications, we have produced the CMG 0.05° resolution GPP and uncertainty datasets at two temporal resolutions (16-day and monthly) from 18 February 2000 to 31 December 2024. For local applications, we produced 250 m GPP and uncertainty data for 750×750 m footprint centered at each site at 16-day intervals from 18 February 2000 to 31 December 2024 for all 212 global FLUXNET sites. ## Files: 1. eLUE-GPP User Guide v1: For a detailed description of the data product. 2. Global_5km_16d_YYYY.zip: Global CMG 0.05° resolution GPP and uncertainty datasets at 16-day resolution for YYYY (year). 3. Global_5km_monthly_YYYY.zip: Global CMG 0.05° resolution GPP and uncertainty datasets at monthly resolution for YYYY (year). 4. FLUXNET_250m_16d.zip: 250 m GPP and uncertainty data for 750×750 m footprint centered at each flux tower site at 16-day intervals from 18 February 2000 to 31 March 2024 for all 212 global FLUXNET sites (those included in the FLUXDATA2015 dataset), with one file per site. The site name is denoted by xx-xxx (country code - site abbreviation), the data file is xx-xxx.csv, and MaLab_MCD13Q1_eLUE_GPP_YYYYDDD_YYYYDDD_001_FLUXNET_xx-xxx.jpeg is the time series plot for YYYYDDD_YYYYDDD(start year+DOY to end year+DOY). 5. DataReader.R: Code files for reading data in R. 6. DataReader.py: Code files for reading data in Python. 7. DataReader.m: Code files for reading data in Matlab. 8. 16d.GIF: A GIF animation of the spatial distribution of CMG 0.05° resolution GPP with a 16-day temporal resolution from February 18, 2000, to March 31, 2024. 9. monthly.GIF: A GIF animation of the spatial distribution of CMG 0.05° resolution GPP with a monthly temporal resolution from February 18, 2000, to March 31, 2024. ## Change Log: * **15-Aug-2024:** The eLUE GPP dataset has been updated to reflect a different treatment of negative EVI values in the pre-processing stage. This revision resulted in changes to the calculated FLUXNET 250m 16d, global 5km 16d and monthly GPP datasets. * **12-Jan-2025:** The eLUE GPP dataset has been updated with enhanced quality control measures applied to the FLUXNET2015 dataset during the calculation process. This revision resulted in changes to the calculated FLUXNET 250m 16d, global 5km 16d and monthly GPP datasets. Furthermore, we updated the dataset to the latest version, covering data up to December 2024.