Evapotranspiration (AETI), Land Cover Classification (LCC), Net Primary Productivity (NPP), Precipitation (PCP), Phenology (PHE), Quality layers (QUAL), Reference Evapotranspiration (RET), Soil moisture (RSM), Total Biomass Production (TBP) and Water Productivity (WP)

Net Primary Production (NPP) data component

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. NPP is part of a family of definitions describing the carbon fluxes between the ecosystem and the atmosphere. Gross Primary Production (GPP) represents the carbon uptake by the standing biomass due to photosynthesis. NPP is the GPP minus autotrophic respiration, the losses caused by the conversion of basic products (glucose) to higher-level photosynthates (starch, cellulose, fats, proteins) and the respiration needed for the maintenance of the standing biomass. NEP or Net Ecosystem Production also accounts for the contribution of soil respiration, i.e. the re-conversion to CO2 of leaf and other litter by soil micro-flora. Finally, subtracting the losses due to disturbance and anthropogenic removals gives the Net Biome Production (NBP). Below figure shows a schematic overview of carbon fluxes.

Figure x: The component fluxes and processes in ecosystem productivity. GPP: Gross Primary Production, NPP: Net Primary Production, NEP: Net Ecosystem Production, NBP: Net Biome Production (Valentini, 2003)

WaPOR data component

The Net Primary Production component (NPP) is produced using the same processing chain at all resolution levels. The data are delivered on a dekadal basis at all levels, where pixel values represent the average daily net primary production for that specific dekad in gC/m²/day. In some cases, such as for agricultural purposes, it is more appropriate to measure Dry Matter Production (DMP, in kgDM/ha/day). NPP can be converted to DMP using a constant scaling factor of 0.45 gC/gDM (Ajtay et al., 1979). Therefore 1 gC/m²/day (NPP) = 22.222 kgDM/ha/day (DMP). Typical values for NPP within the region vary between 0 and 5.4 gC/m²/day (NPP), or 0 to 120 kgDM/ha/day (DMP), although higher values can occur (theoretically up to 320 kgDM/ha/day).

***Table x: Overview of NPP data component

Calculating NPP requires daily input from Weather data (Tmin/Tmax) and Solar radiation, as well as dekadal inputs from fAPAR and Soil moisture stress. Land Cover is an indirect input as Light Use Efficiency (LUE) is land cover specific. NPP is in turn used as input to compute total aboveground biomass production.

Methodology

NPP is derived from satellite imagery, land cover information and meteorological data. The core of the methodology has been detailed in Veroustraete et al. (2002), whilst the practical implementation is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light use efficiencies (LUEs). WaPOR applies this updated methodology. Specifically for WaPOR, two additional changes were made: (i) a reduction factor for soil moisture stress (SM) that accounts for short-term water deficiency was added, and (ii) the application of light use efficiencies (LUEs) specific to the type of natural vegetation and the type of crops classified within WaPOR. More information on the computation of this soil moisture stress factor and more details on LUEs can be found in the relevant documentation.

The method to compute Net Primary Production is based on Monteith (1972), which describes ecosystem productivity in response to solar radiation. The full equation as adopted in WaPOR is expressed in below equation. In the following sections, several factors of this equation are further elaborated upon.

Where:
NPP = Net Primary Production [gC/m²/day]
Sc = Scaling factor from DMP to NPP (=0.045) [-]
R_s = Total shortwave incoming radiation [GJT/ha/day]
ε_p = Climate efficiency (0.48) [JP/JT]
fAPAR = Fraction of photosynthetically active radiation absorbed by green vegetation [JPA/JP]
SM = Soil moisture stress reduction factor
ε_lue = Light use efficiency (DM=Dry Matter) at optimum conditions [kgDM/GJPA]
ε_T = Normalized temperature effect [-]
ε_CO2 = Normalized CO2 fertilization effect [-]
ε_AR = Fraction kept after autotrophic respiration, equals to 0.5 [-]
ε_RES = Fraction kept after residual effects (including soil moisture stress)[-]

The climate efficiency ε_p is the fraction of PAR (Photosynthetically Active Radiation, 400-700 nm) within the total shortwave domain (200-4000 nm) and varies slightly around the mean of ε_p=0.48, denoting that 48% of all incoming solar radiation is situated in the 400-700nm region (McCree, 1972). Although small variations occur, this value is kept constant.

Light Use Efficiency (ε_LUE) is a coefficient for the efficiency by which vegetation converts energy into biomass. As mentioned earlier, in WaPOR it is tightly linked to the produced land cover information. More information on this factor is provided in the relevant section of the WaPOR documentation.

The effect of temperature (ε_T), atmospheric CO₂ concentration (ε_CO2) and autotrophic respiration (ε_AR) is simulated via rather complex biochemical equations (see Veroustraete et al., 2002). However, the influencing factors driving these biochemical processes are temperature (T) and CO2 concentration. The CO₂ concentration is assumed to be constant over the globe, as well as within a year. The overall increasing trend in CO₂ concentrations, resulting in the greening effect of CO₂, is included by adjusting the CO₂ concentration with a linear function over time. This function was derived from the annual 'spatial' average of globally-averaged marine surface (CO₂) data from the NOAA-ESRL cooperative air sampling network of the last 15 years.

The factor ε_RES (residual) is added in the equation x to emphasize the fact that some potentially important factors, such as the effect of droughts, nutrient deficiencies, pests and plant diseases, influence NPP. However, it might be argued that the adverse effects of diseases and shortages of nutrients are manifested (sooner or later) via the remote sensing-derived fAPAR. Hence, in practice, ε_RES is assumed to have a constant value of 1.

Given the simple elaboration of the epsilons, equation x can be rewritten as follows:

Where:

This formulation better highlights the fact that, within the limits of the described model, NPP is only determined by six basic factors: fAPAR, soil moisture stress, radiation, temperature, land cover specific light use efficiency and CO₂. Note that NPP_max (Equation x4) represents the maximum obtainable NPP, for the (virtual) cases where fAPAR would be equal to one. At the same time, the above equations provide a practical method to bypass the differences in temporal (and spatial) resolution between the inputs. The meteorological inputs (R_s, T_min, T_max) are provided on a daily basis, fAPAR and SM are derived from the dekadal data components and the final NPP product has a dekadal frequency. More details on the radiation data component, fAPAR and soil moisture stress can be found in the relevant sections of the WaPOR documentation.

Processing approach

NPP is computed using a combination of daily weather data (Tmin/Tmax) and [Solar radiation](../Intermediate_Data_Components/SolarRadiati, dekadal fAPAR and [soil moisture](../WaPOR_data_components_and_methodology/ stress data and land cover information. The procedure according to Eerens et al. (2004) has been adopted, which is also visually illustrated in Figure x. First, using Equation * and based on the daily meteo data R_s, T_min, T_max, and the yearly fixed value of the CO₂ level, NPP_max is calculated on a daily basis (NPP_max,1. At the end of every dekad, a new data layer (NPP_max,10 is computed with the mean of the daily NPP_max,1 scenes. Next, NPP_max,10, fAPAR, the land use efficiency and SM are simply multiplied to retrieve the final dekadal NPP output (Equation *).

Figure x: Detailed process flow of NPP. Daily NPPmax is estimated based on meteorological data. At the end of each dekad, a mean value composite of these NPPmax images is calculated. The final NPP10 product is retrieved by the simple multiplication of the mean value composite NPPmax with the fAPAR, soil moisture stress and the land cover dependent light use efficiency.

Challenges

Effects of several potentially important factors, such as nutrient deficiencies, pests and plant diseases are omitted in the calculation of the NPP product. However, it might be argued that the adverse effects of diseases and shortages of nutrients are manifested (sooner or later) via the remote sensing-derived fAPAR.

The collection of optical satellite data can be hampered by the presence of clouds, reducing the information on temporal variability. The quality of the NPP data component is a combination of the accuracy of the algorithms and the quality of the external data. The NDVI quality layer is provided to indicate the quality of the external optical satellite data.

Functions and flowcharts

NPP