Estimation of net primary productivity: An introduction to different approaches

Abstract

The net primary productivity (NPP) is defined as the net carbon gain by plants in natural and agricultural ecosystems, which is computed by subtracting the autotrophic respiration from the gross photosynthetic carbon uptake by the ecosystems. It acts as the indicators of carbon sequestration, ecosystem health, and agricultural yield which are important in the context of climate change, its impact and mitigation, and food security. The NPP can be estimated in multiple ways including the direct and indirect measurements and modelling. The various direct NPP measurements are ground-based in situ observations of ecosystem-atmosphere carbon flux such as the micrometeorological flux-gradient method, eddy covariance, flux chamber measurements etc. The indirect measurements of NPP include the satellite-derived NPP estimates which are computed from the directly measured spectral reflectances, using different biophysical relations such as the light use efficiency model etc. However the accuracy of these products varies geospatially and largely depends on the retrieval of input parameters and representativeness of underlying model parameterization. There are two major modelling approaches to estimate the NPP namely bottom-up and top-down estimates. The bottom-up models compute the NPP from the directly recorded variables such as temperature, precipitation, radiation, wind, atmospheric CO2 concentration etc. using the biome-specific functional relations due to which these are also known as the process-based models. The top-down or inverse models use the matrix inversion method to predict the sources and sinks of CO2 emission in a region from the directly measured concentrations by the surface stations and/or satellites and thus the NPP of that region. The NPP estimates from measurements and models are used to calculate the carbon budgets at different scales from ecosystem-level to global scale. However significant uncertainties exist in such estimates due to insufficient surface measurements, under-representation of several regions and ecosystems, imperfect boundary conditions and parameterizations in models. While the direct measurements provide more accurate estimates of NPP, these require to be carried over for long duration using multiple different instruments which are prone to errors and data-loss whereas the models can provide large-scale estimates of NPP but need to be validated against realistic in situ measurements across an wide array of ecosystems. The aforementioned aspects of NPP estimation are discussed in detail in the present chapter.