Retrievals of Chlorophyll Fapar for Improved Crop Gpp Modeling

Qingyuan Zhang, Universities Space Research Association Greenbelt, Greenbelt, MD, United States, Yen-Ben Cheng, Sigma Space Corporation, Lanham, MD, United States, Yujie Wang, University of Maryland Baltimore County, Baltimore, MD, United States, Alexei Lyapustin, NASA Goddard Space Flight Center, Greenbelt, United States and Tian Yao, Science Systems and Applications, Inc., Lanham, United States
Abstract:
Accurate estimation of crop gross primary productivity (GPP) is important. We have recently developed an algorithm to derive fAPAR of chlorophyll (fAPARchl), fAPAR of foliage (fAPARfoliage) and chlorophyll LAI (LAIchl) with PROSAIL2. The MODIS surface reflectance produced with MAIAC were utilized to retrieve fAPARchl, fAPARfoliage and LAIchl for three AmeriFlux sites of maize and soybean. MOD15A2 FPAR and the retrieved fAPARchl were compared with field fAPARcanopy and the fraction of PAR absorbed by green leaves of the vegetation (fAPARgreen). MOD15A2 FPAR overestimated field fAPARcanopy in spring and in fall, and underestimated field fAPARcanopy in midsummer whereas fAPARchl correctly captured the seasonal phenology. The retrieved fAPARchl agreed well with field fAPARgreen at early crop growth stage in June, and was less than field fAPARgreen in late July, August and September, which is consistent with crop physiology theory. GPP estimates with fAPARchl and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPARchl was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPARchl which also reduced uncertainties of crop GPP estimates by 1.12 – 2.37 g C m-2 d-1.

NDVI, EVI, WDRVIgreen, and CIgreen have also been employed to estimate GPP by other scientific teams. We investigated the scaling factors and offsets (i.e., regression slopes and offsets) between fAPARchl and the VIs. The results showed that the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions, compared to the original un-scaled VIs. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields.

MOD15A2 LAI and the retrieved LAIchl were implemented into CLM to simulate GPP, respectively. Simulated GPP series with both MOD15A2 LAI and LAIchl were compared to tower flux GPP. GPP simulation with LAIchl was significantly improved, compared to GPP simulation with MOD15A2 LAI. Our results show that the fAPARchl/LAIchl products significantly improve the accuracy and reduce the uncertainty of GPP estimates, compared to MOD15A2 FPAR/LAI.

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