A snow-free vegetation index for improved monitoring of vegetation   spring green-up date in deciduous ecosystems

doi:10.1016/j.rse.2017.04.031

CORC > 北京大学 > 城市与环境学院

	A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems
	Wang, Cong ; Chen, Jin ; Wu, Jin ; Tang, Yanhong ; Shi, Peijun ; Black, T. Andrew ; Zhu, Kai
刊名	REMOTE SENSING OF ENVIRONMENT
	2017
关键词	Vegetation phenology Green-up date Remote sensing Snowmelt NDPI Climate change DIGITAL REPEAT PHOTOGRAPHY CARBON-DIOXIDE EXCHANGE DIFFERENCE WATER INDEX LAND-SURFACE PHENOLOGY TIBETAN PLATEAU GROWING-SEASON BOREAL REGIONS HIGH-LATITUDES SATELLITE DATA CLIMATE-CHANGE
DOI	10.1016/j.rse.2017.04.031
英文摘要	Vegetative spring green-up date (GUD), an indicator of plants' sensitivity to climate change, exerts an important influence on biogeochemical cycles. Conventionally, large-scale monitoring of spring phenology is primarily detected by satellite-based vegetation indices (VIs), e.g. the Normalized Difference Vegetation Index (NDVI). However, these indices have long been criticized, as the derived GUD can be biased by snowmelt. To minimize the snowmelt effect in monitoring spring phenology, we developed a new index, Normalized Difference Phenology Index (NDPI), which is a 3-band VI, designed to best contrast vegetation from the background (i.e. soil and snow in this study) as well as to minimize the difference among the backgrounds. We examined the rigorousness of NDPI in three ways. First, we conducted mathematical simulations to show that NDPI is mathematically robust and performs superior to NDVI for differentiating vegetation from the background, theoretically justifying NDPI for spring phenology monitoring. Second, we applied NDPI using MODIS land surface reflectance products to real vegetative ecosystems of three in-situ PhenoCam sites. Our results show that, despite large snow cover in the winter and snowmelt process in the spring, the temporal trajectories of NDPI closely track the vegetation green-up events. Finally, we applied NDPI to 11 eddy-covariance tower sites, spanning large gradients in latitude and vegetation types in deciduous ecosystems, using the same MODIS products. Our results suggest that the GUD derived by using NDPI is consistent with daily gross primary production (GPP) derived GUD, with R (Spearman's correlation) = 0.93, Bias = 2.90 days, and RMSE (the root mean square error) = 7.75 days, which outcompetes the snow removed NDVI approach, with R = 0.90, Bias = 7.34 days, and RMSE = 10.91 days. We concluded that our newly-developed NDPI is robust to snowmelt effect and is a reliable approach for monitoring spring green-up in deciduous ecosystems. (C) 2017 Elsevier Inc. All rights reserved.; Fund for Creative Research Groups of National Natural Science Foundation of China [41321001]; Northeastern States Research Cooperative, NSF's Macrosystems Biology Program [EF-1065029]; DOE's Regional and Global Climate Modeling Program [DE-SC0016011]; US National Park Service Inventory and Monitoring Program; USA National Phenology Network (United States Geological Survey) [G10AP00129]; project of "Early detection and prediction of climate warming based on the long-term monitoring of fragile ecosystems in the East Asia" - Ministry of Environment, Japan [MOJ-Kan-1351]; U.S. Department of Energy's Office of Science; SCI(E); ARTICLE; 1-12; 196
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/472537]
专题	城市与环境学院
推荐引用方式 GB/T 7714	Wang, Cong,Chen, Jin,Wu, Jin,et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems[J]. REMOTE SENSING OF ENVIRONMENT,2017.
APA	Wang, Cong.,Chen, Jin.,Wu, Jin.,Tang, Yanhong.,Shi, Peijun.,...&Zhu, Kai.(2017).A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems.REMOTE SENSING OF ENVIRONMENT.
MLA	Wang, Cong,et al."A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems".REMOTE SENSING OF ENVIRONMENT (2017).