气候变化领域集成服务门户(CCPortal): A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems

CCPortal

DOI	10.1016/j.rse.2019.03.028
	A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems
	Yang, Wei; Kobayashi, Hideki; Wang, Cong; Shen, Miaogen; Chen, Jin; Matsushit, Bunkei; Tang, Yanhong; Kim, Yongwon; Bret-Harte, M. Syndonia; Zona, Donatella; Oechel, Walter; Kondoh, Akihiko
通讯作者	Yang, W (通讯作者)
发表日期	2019
ISSN	0034-4257
EISSN	1879-0704
起始页码	31
结束页码	44
卷号	228
英文摘要	Satellite monitoring of plant phonology in tundra and grassland ecosystems using conventional vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), can be biased by effects of snow. Snow free VIs that take advantage of the shortwave infrared (SWIR) band have been proposed to overcome this problem, viz., the phonology index (PI) and the normalized difference phonology index (NDPI). However, the PI cannot properly capture the presence of sparse vegetation, and the NDPI does not account for the influence of dry vegetation. Here, we propose a novel snow-free VI, designated the normalized difference greenness index (NDGI), that uses reflectance in the green, red, and near-infrared (NIR) bands. The NDGI is a semi-analytical index based on a linear spectral mixture model and the spectral characteristics of vegetation, snow, soil, and dry grass. Its performance at estimating the start and end of the growing season (SOS and EOS) was evaluated using simulation datasets, time-lapse camera data at tundra sites, and flux tower gross primary production (GPP) data at grassland sites. Simulation results demonstrated that the NDGI can exclude the influence of snow on estimates of SOS and EOS. At the tundra sites, the NDGI markedly outperformed the NDVI, PI, NDPI, NIRv (near-infrared reflectance of vegetation), EVI2 (two-band enhanced vegetation index), PPI (plant phenology index), and DVI (difference vegetation index plus) for SOS estimation, with a root mean square error (RMSE) of 6.5 days and a Bias of 1.3 days, and for EOS estimation, with an RMSE of 8.3 days and a Bias of 0.11 days. At the grassland sites, the NDGI also outperformed the other VIs at SOS estimation, with an RMSE of 10.3 days and a Bias of 4.9 days. Although its performance was poorer at monitoring EOS than SOS at grassland (GPP) sites, its performance was comparable to that of the PI and superior to that of the other VIs at estimating EOS. These results indicate the potential of the NDGI for operational monitoring of plant phenology in tundra and grassland ecosystems based on satellite observations.
关键词	LAND-SURFACE PHENOLOGY TIME-SERIES GREEN-UP SPRING PHENOLOGY GROWING-SEASON BOREAL REGIONS CARBON BALANCE SOIL SATELLITE MODIS
英文关键词	Vegetation phenology; Remote sensing; Snow; NDGI; MODIS; Linear spectral mixture model
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000470050500003
来源期刊	REMOTE SENSING OF ENVIRONMENT
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259655
推荐引用方式 GB/T 7714	Yang, Wei,Kobayashi, Hideki,Wang, Cong,et al. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems[J]. 中国科学院青藏高原研究所,2019,228.
APA	Yang, Wei.,Kobayashi, Hideki.,Wang, Cong.,Shen, Miaogen.,Chen, Jin.,...&Kondoh, Akihiko.(2019).A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems.REMOTE SENSING OF ENVIRONMENT,228.
MLA	Yang, Wei,et al."A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems".REMOTE SENSING OF ENVIRONMENT 228(2019).