干旱区生态环境知识资源中心(Arid): Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland

Arid

DOI	10.1016/j.agrformet.2020.107983
	Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland
	Wang Hesong; Jia Gensuo; Epstein, Howard E.; Zhao Huichen; Zhang Anzhi
通讯作者	Wang, HS
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
ISSN	0168-1923
EISSN	1873-2240
出版年	2020
卷号	288
英文摘要	The accurate estimation of temporally-continuous gross primary production (GPP) is important for a mechanistic understanding of the global carbon budget, as well as the carbon exchange between land and atmosphere. Ground-based PhenoCams can provide near-surface observations of plant phenology with high temporal resolution and possess great potential for use in modeling the seasonal dynamics of GPP. However, due to the site-level empirical approaches for estimating the fraction of absorbed photosynthetically active radiation (fAPAR), a broad application of PhenoCams in GPP modeling has been restricted. In this study, the stage of vegetation phenology (P-scalar) is proposed, which is calculated from the excess green index (ExGI) derived from PhenoCam data. We integrate Pscalar with the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to generate a daily time-series of the fAPAR (fAPAR(CAM)), and then to estimate daily GPP (GPP(CAM)) with a light use efficiency model in a semi-arid grassland area from 2012 to 2014. Over the three continuous years, the daily fAPAR(CAM) exhibited similar temporal behavior to the eddy covariance-measured GPP (GPP(EC)), and the overall determination coefficients (R-2) were all > 0.81. GPP(CAM) agreed well with GPPEC, and these agreements were highly statistically significant (p < 0.01); R-2 varied from 0.80 to 0.87, the relative error (RE) varied from -2.9% to 2.81%, and the root mean square error (RMSE) ranged from 0.83 to 0.98 gC/m(2)/d. GPP(CAM) was then resampled to 8-day temporal resolution (GPP(CAM8d)), and further evaluated by comparisons with MODIS GPP products (GPP(MOD17)) and vegetation photosynthesis model (VPM)-derived GPP (GPP(VPM)). Validation revealed that the variance explained by GPP(CAM8d) was still the greatest among these three GPP products. The RMSE and RE of GPPCAM8d were also lower than those of the other two GPP products. The explanatory power of predictors in GPP modeling was also explored; the fAPAR was found to be the most influential predictor, followed by photosynthetically active radiation (PAR). The contributions of the environmental stress indices of temperature and water (T-scalar and W-scalar, respectively) were less than that of PAR. These results highlight the potential for PhenoCam images in high temporal resolution GPP modeling. Our GPP modeling method will help reduce uncertainties by using PhenoCam images for monitoring the seasonal development of vegetation production.
英文关键词	PhenoCam Eddy covariance Light use efficiency model Fraction of absorbed photosynthetically active radiation (fAPAR) Gross primary production (GPP)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000538107200003
WOS关键词	INDUCED CHLOROPHYLL FLUORESCENCE ; EVERGREEN CONIFEROUS FOREST ; DIGITAL REPEAT PHOTOGRAPHY ; SUN-INDUCED FLUORESCENCE ; EDDY COVARIANCE DATA ; CAMERA-BASED INDEXES ; CANOPY PHOTOSYNTHESIS ; DEGRADED GRASSLAND ; FLUX MEASUREMENTS ; DECIDUOUS FOREST
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
来源机构	中国科学院大气物理研究所 ; 北京林业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324648
作者单位	[Wang Hesong] Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China; [Jia Gensuo; Zhao Huichen; Zhang Anzhi] Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm Temperate East Asia, Beijing 100029, Peoples R China; [Epstein, Howard E.] Univ Virginia, Dept Environm Sci, Charlottesville, VA 22904 USA
推荐引用方式 GB/T 7714	Wang Hesong,Jia Gensuo,Epstein, Howard E.,et al. Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland[J]. 中国科学院大气物理研究所, 北京林业大学,2020,288.
APA	Wang Hesong,Jia Gensuo,Epstein, Howard E.,Zhao Huichen,&Zhang Anzhi.(2020).Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland.AGRICULTURAL AND FOREST METEOROLOGY,288.
MLA	Wang Hesong,et al."Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland".AGRICULTURAL AND FOREST METEOROLOGY 288(2020).