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

被引:126
作者
Wang, Cong [1 ,2 ]
Chen, Jin [1 ]
Wu, Jin [3 ]
Tang, Yanhong [4 ]
Shi, Peijun [1 ]
Black, T. Andrew [5 ]
Zhu, Kai [2 ,6 ]
机构
[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[2] Univ Texas Arlington, Dept Biol, 701 S Nedderman Dr, Arlington, TX 76019 USA
[3] Brookhaven Natl Lab, Environm & Climate Sci Dept, Upton, NY 11973 USA
[4] Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
[5] Univ British Columbia, Fac Land & Food Syst, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada
[6] Rice Univ, Dept BioSci, 6100 Main St, Houston, TX 77005 USA
基金
中国国家自然科学基金;
关键词
Vegetation phenology; Green-up date; Remote sensing; Snowmelt; NDPI; Climate change; DIGITAL REPEAT PHOTOGRAPHY; CARBON-DIOXIDE EXCHANGE; LAND-SURFACE PHENOLOGY; TIBETAN PLATEAU; GROWING-SEASON; BOREAL REGIONS; HIGH-LATITUDES; FOREST; MODIS; WATER;
D O I
10.1016/j.rse.2017.04.031
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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.
引用
收藏
页码:1 / 12
页数:12
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