Global Monitoring of Agricultural Productivity with Space-Borne Measurements of Sun-Induced Chlorophyll Fluorescence

Abstract

Global food and biofuel production and their vulnerability in a changing climate are of paramount societal importance. However, model based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. In this study we investigate the potential of space-based retrievals of sun-induced chlorophyll fluorescence (SIF) to provide a direct, global and time-resolved measure of the GPP of cropland and grassland ecosystems. The following data streams have been used in this analysis: (1) SIF retrievals have been derived from measurements of the MetOp-A / GOME-2 instrument in the 2007-2011 time period (see contributions by Joiner et al and Köhler et al); (2) ensembles of process-based and data-driven biogeochemistry models have been analyzed in order to assess the capability of global models to represent crop GPP; (3) flux tower-based GPP estimates covering the 2007-2011 time period have been extracted for 17 cropland and grassland sites in the Midwest US and Western Europe from the Ameriflux and the European Fluxes Database networks; (4) large-scale net primary production estimates have been derived by the agricultural inventory data sets developed by USDA-NASS and Monfreda et al. The strong linear correlation between the SIF space retrievals and the flux tower-based GPP, found to be substantially higher than for reflectance-based vegetation indices (EVI, NDVI and MTCI), has enabled the direct upscaling of SIF to cropland GPP maps at the synoptic scale. Our SIF-based annual crop GPP estimates are 50 to 75 % higher than results from state- of-the-art carbon cycle models over the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. This finding is supported by an independent validation against agricultural inventories derived from yield statistics. In addition, we show that generic process-based models fail to capture the seasonality of agricultural areas when the lifetime of the crops differs from that of the natural vegetation in the surroundings. In In this work we demonstrate that spaceborne SIF retrievals can provide realistic estimates of photosynthetic uptake rates over the largest crop belts world-wide without need of any additional information. This finding indicates that SIF data can be an essential complement to existing models and remotely-sensed observations for the evaluation of global agricultural yields, and that SIF-based estimates of crop photosynthesis may become a unique data set for both an unbiased monitoring of agricultural productivity and the benchmarking of carbon cycle models.