Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

Abstract

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

Keywords: Earth observation; carbon fluxes; carbon modeling; crop productivity; spaceborne spectroscopy.

Fig. 1.

Global map of maximum monthly sun-induced chlorophyll fluorescence (SIF) per 0.5° grid box for 2009. SIF retrievals are performed in a spectral window centered at 740 nm (see Materials and Methods and SI Appendix, SIF Retrievals). This maps illustrates the outstanding SIF signal detected at the US CB, which shows the highest SIF return of all terrestrial ecosystems. The maximum SIF over the largest part of the US CB region is detected in July.

Fig. 2.

Spatial patterns of maximum monthly gross primary production (GPP) per 0.5° grid box for 2009 from data-driven (A) and process-based (B) models together with maximum monthly SIF at 740 nm (C). The fraction of C4 crop area (mostly corn in this region) depicts the approximate area of the US Corn Belt (D). The data-driven GPP data correspond to the MPI-BGC model (27), the process-based GPP corresponds to the median of an ensemble of 10 global dynamic vegetation models from the Trendy (“Trends in net land-atmosphere carbon exchange over the period 1980−2010”) project (28, 29), and SIF was retrieved from GOME-2 satellite measurements (26). The fraction of C4 crop data are described in Ramankutty et al. (6).

Fig. 3.

Comparison of monthly mean GPP estimates at cropland flux tower sites in the US Corn Belt and grassland sites in Western Europe. Flux tower GPP estimates are compared with sun-induced fluorescence (SIF) observations at 740 nm (A) and with GPP estimates from the MPI-BGC data-driven model (27) (B) and from process-based models [median of an ensemble of 10 dynamic global vegetation models (28, 29)] (C). Each symbol depicts a monthly average for a 0.5° grid box and those months in the 2007–2011 period for which flux tower data were available (see SI Appendix, Table S1). The P value is <0.01 in all of the comparisons. The dashed line in B and C represents the 1:1 line. Similar comparisons but including also Western Europe cropland sites are provided in SI Appendix, Fig. S4.

Fig. 4.

Time series of flux tower-based GPP compared with SIF retrievals (A and B) and the MODIS MOD13C2 EVI (C and D) for the same cropland and grassland sites and spatiotemporal averages as in Fig. 3 (monthly averages in 0.5° grid boxes and the 2007–2011 period). SIF and EVI are plotted with the same vertical scale for cropland and grassland sites.

Fig. 5.

Comparison of net primary production (NPP) estimates over the US Corn Belt (35°N–50°N, 80°W–105°W) from the USDA agricultural inventory (8) with crop GPP estimates from SIF retrievals (A) and data-driven and process-based model ensembles (B and C). Points correspond to 1° grid boxes with fraction of cropland area higher than 20%. GPP and NPP values are given in per-total-area units (see SI Appendix, NPP Data from Agricultural Inventories). The squared Pearson’s correlation coefficient and the P value of the comparisons are shown. An analogous comparison with the inventory-based NPP from Monfreda et al. (7), which also includes Western Europe, can be found in SI Appendix, NPP Data from Agricultural Inventories.

Fig. 6.

Spatial details of the annual SIF-based crop GPP estimates over cropland areas (A), fraction of cropland area per grid box (B), and absolute and relative differences between annual SIF-based crop GPP estimates and the output of data-driven models (C and E) and process-based models (D and F). Spatially explicit GPP is derived through the scaling of SIF retrievals with the relationship GPP(SIF) = −0.10 + 3.72 × SIF (see SI Appendix, Derivation of Spatially-Explicit Crop GPP Estimates). Cropland GPP is given in per-total-area units. The absolute difference ΔGPP is calculated as GPP(SIF) − GPP(model), and the relative difference is calculated as ΔGPP over GPP(model).

Fig. 7.

(A–F) Time series of monthly crop GPP derived from SIF retrievals, process-based models, and data-driven models over different cropland regions in 2009. GPP area averages are weighted by the fraction of cropland area per grid box. Data-driven GPP corresponds to the MPI-BGC data-driven model (27). Process-based GPP estimates are calculated as the median of the monthly GPP estimates from the Trendy process-based model ensemble (28, 29) (see also SI Appendix, Table S2).