Abstract
The application of detailed process-oriented simulation models for gross primary production (GPP) estimation is constrained by the scarcity of the data needed for their parametrization. In this manuscript, we present the development and test of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Normalized Difference Vegetation Index (NDVI) observations into a simple process-based model driven by basic meteorological variables (i.e., global radiation, temperature, precipitation and reference evapotranspiration, all from global circulation models of the European Centre for Medium-Range Weather Forecasts). The model is run at daily time-step using meteorological forcing and provides estimates of GPP and LAI, the latter used to simulate MODIS NDVI though the coupling with the radiative transfer model PROSAIL5B. Modelled GPP is compared with the remote sensing-driven MODIS GPP product (MOD17) and the quality of both estimates are assessed against GPP from European eddy covariance flux sites over crops and grasslands. Model performances in GPP estimation (R2 = 0.67, RMSE = 2.45 gC m−2 d−1, MBE = −0.16 gC m−2 d−1) were shown to outperform those of MOD17 for the investigated sites (R2 = 0.53, RMSE = 3.15 gC m−2 d−1, MBE = −1.08 gC m−2 d−1).
Highlights
Gross primary production (GPP) of crops and rangelands is important for understanding CO2 uptake of these ecosystems and for monitoring the provision of services [1]
E1x0aomf p21les of simulated GPP and Normalized Difference Vegetation Index (NDVI) compared to the observed time series are reported in Figures 2 and 3 for crop rdsei−st1)ep.secAatnilvtdheolFyui)ggthuherseSosimm4e–m6uofnoddreelgrGr-aPasPsnldeasntoidmvesairtt-eeessst.airmFeoanrtiobotnoatfhfoeccccrtueordpssbiyannsydssotgmermeasaystilecaanursdnds(eeirt.gee.ss,tiwm20ea0ts9ieolnaencadtseid2n0et1hx2ea,mple sites cwasitehocf othnetrMasOtiDn1g7pperrofdourcmt.ances of the model
This study demonstrated that the assimilation of NDVI observations into simple growth model is effective for estimating crop and grassland GPP
Summary
Gross primary production (GPP) of crops and rangelands is important for understanding CO2 uptake of these ecosystems and for monitoring the provision of services (food, feed, and fibers) [1]. Detailed crop growth models (CGMs) (e.g., WOFOST, [2]; CROPSYST, [3]), describe plant eco-physiological processes such as light interception and absorption, carbon assimilation, as determined by environmental conditions (weather, soil properties, agro-management, etc.) and vegetation characteristics. Complex CGMs require a large number of input parameters whose value is often not available and/or highly uncertain. The unrealistic spatial and temporal representation of key parameters representing vegetation characteristics and plant functional traits controlling CO2 uptake was shown to hamper the simulation of GPP at various scales [4,5]. Uncertainties in model inputs are transferred into large errors in model estimates [6]
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