Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data

REMOTE SENSING OF ENVIRONMENT

卷:210

Article

Zhang, Yongguang^1,2  Guanter, Luis³  Joiner, Joanna⁴  Song, Lian¹  Guan, Kaiyu⁵ 

[1] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China

[2] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China

[3] Helmholtz Ctr Potsdam, GFZ German Res Ctr Geosci, Remote Sensing Sect, Telegrafenberg A17, D-14473 Potsdam, Germany

[4] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA

[5] Univ Illinois, Dept Nat Resources & Environm Sci, Natl Ctr Supercomp Applicat, Champaign, IL USA

关键词: Sun-induced;    Chlorophyll fluorescence;    Regional GPP;    SCOPE;    Leaf maximum carboxylation rate (V-cmax);    Cropland;   

DOI  :  10.1016/j.rse.2018.03.031

来源: Elsevier

【 摘 要 】

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V-cmax at a reference temperature of 25 degrees C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V-cmax at the seasonal scale (R-2 = 0.70 for C4 and R-2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V-cmax with SIF that was used to estimate spatially and temporally varying crop V-cmax for the US Corn Belt region. The resulting V-cmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V-cmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

【 授权许可】

Free