A REVISED MODIS-GPP ALGORITHM BY INCORPORATING SEASONAL FLUCTUATION OF MAXIMUM LIGHT USE EFFICIENCY FOR MAIZE AND SOYBEAN

文献类型： 外文期刊

第一作者： Huang, Lingxiao

作者： Huang, Lingxiao;Jiang, Yazhen;Tang, Ronglin;Huang, Lingxiao;Jiang, Yazhen;Tang, Ronglin;Liu, Meng

作者机构：

关键词： Gross Primary Production (GPP); Vegetative Stage; Senescence Stage; Seasonal Fluctuation; Maximum Light Use Efficiency; NIRv

期刊名称：2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)

ISSN： 2153-6996

年卷期： 2022 年

页码：

收录情况： SCI

摘要： Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand global carbon budget but also ensures basic human survival supplements. Here, we improved the MODIS-GPP algorithm by two main perspectives: (1) taking the seasonal variations of maximum light use efficiency (LUE) into modeling consideration; (2) separately parameterizing maximum LUE with a recently proposed vegetation index (VI) NIRv during vegetative stage and senescence stage. Performances of the revised and traditional MODIS-GPP algorithms were tested at three FLUXNET crop sites planted with maize and soybean. The revised model was well validated, indicated by the root mean square error (RMSE), coefficient of determination (R-2) and Bias being 2.33 gC m(-2) day(-1), 0.91 and 0.48 gC m(-2) da(y)-(1) for maize, respectively, and being 1.51 gC m(-2) day(-1), 0.91 and 0.43 gC m(-2) day(-1) for soybean, respectively. Overall, compared to the traditional MODIS-GPP algorithm, the proposed algorithm reduced RMSE by 29.6% and 27.4%, increased R-2 by 10.9% and 10.9%, and reduced Bias by 41.5% and 36.8% for maize and soybean, respectively. This paper demonstrates that incorporating seasonal fluctuations of maximum LUE into MODIS-GPP algorithm and distinguishing the different photosynthesis rates among vegetative and senescence stages significantly benefit the retrieval accuracy of daily model-estimated GPP.

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