The MODIS Gross Primary Production (GPP) CONUS dataset estimates GPP using MODIS Surface Reflectance for CONUS. GPP is the amount of carbon captured by plants in an ecosystem and is an essential component in the calculations of Net Primary Production (NPP). GPP is calculated using the MOD17 algorithm (see MOD17 User Guide) with MODIS Surface Reflectance, gridMET, and the National Land Cover Database.

The MOD17A2H V6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite with a 500m pixel size. The product is based on the radiation-use efficiency concept and can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

The MOD17A3HGF V6.1 product provides information about annual Gross and Net Primary Productivity (GPP and NPP) at 500m pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis(PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) …

The MYD17A3HGF V6.1 product provides information about annual Gross and Net Primary Productivity (GPP and NPP) at 500m pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis(PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) …

Data from article Stocker et al. (in review) *Nature Geosci.*

The datasets provided here include:

• Site-level GPP model results from the P-model (Wang et al., 2017)
• Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)

This data may be used to partly reproduce results presented in Stocker et al. (2019) Nature Geosci. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.

For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from Github or Zenodo, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on RPubs.

The present datasets are prepared by script `prepare_data_openaccess.R ` on Github or Zenodo.

Data description

Site-level data

Data is provided as CSV files:

• `gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)
• `gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)
• `gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))
• `gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.

Each column is a variable with the following name and units (not all variables are available in all files):

• `site_id`: FLUXNET site ID
• `date`: Date of measurement, units: YYYY-MM-DD
• `gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)
• `aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1
• `pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1
• `soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.
• `flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.
• `beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.

Global P-model simulation outputs

GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.

• `s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.
• `s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)

Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com.

References

Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).
Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).
Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).
Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).

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منتج MYD17A2H V6.1 Gross Primary Productivity (GPP) هو منتج مركّب تراكمي لمدة 8 أيام بدقة 500 متر. يستند المنتج إلى مفهوم كفاءة استخدام الإشعاع ويمكن استخدامه كمدخلات في نماذج البيانات لاحتساب الطاقة الأرضية وعمليات دورة الكربون والمياه والكيمياء الحيوية الجغرافية للنباتات. المستندات: …

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