文献类型：期刊文献

英文题名：Comparing the performance of phenocam GCC, MODIS GCC, and MODIS EVI for retrieving vegetation phenology and estimating gross primary production

作者：Zhang, Jingru[1,6] Xiao, Jingfeng[2] Tong, Xiaojuan[1] Zhang, Jinsong[3,4] Li, Jun[5] Liu, Peirong[1,7] Yu, Peiyang[1] Meng, Ping[3,4]

第一作者：Zhang, Jingru

通信作者：Tong, XJ[1]

机构：[1]Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China;[2]Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA;[3]Chinese Acad Forestry, Key Lab Tree Breeding & Cultivat State Forestry Ad, Res Inst Forestry, Beijing 100091, Peoples R China;[4]Henan Xiaolangdi Earth Crit Zone Natl Res Stn Midd, Jiyuan 454650, Peoples R China;[5]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;[6]Zhejiang A&F Univ, Sch Environm & Resources Sci, Hangzhou 311300, Peoples R China;[7]Chinese Acad Forestry, Res Inst Trop Forestry, Hainan Jianfengling Forest Ecosyst Natl Field Sci, Guangzhou 510520, Peoples R China

年份：2024

卷号：166

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20242716566389);Scopus(收录号：2-s2.0-85197348995);WOS:【SCI-EXPANDED(收录号:WOS:001266850700001)】；

基金：This study was sponsored by the National Natural Science Foundation of China (32271875; 31872703) and the National Key R & D Program of China (2020YFA0608101) . J.X. was supported by the University of New Hampshire through bridge support. We thank the PhenoCam Network collaborators, including site PIs and technicians, for publicly sharing the data that were used in this paper. We express our appreciation to the AmeriFlux PIs for their data sharing to the AmeriFlux program (https://ameriflux.lbl.gov) .

语种：英文

外文关键词：Phenology; Phenocam; Gross primary production; Green chromatic coordinate; Deciduous broadleaf forest; Grassland

摘要：Vegetation phenology serves as an important indicator for climate change and plays a crucial role in affecting the terrestrial water, energy, and carbon cycles. The green chromatic coordinate (GCC) obtained from digital repeat photographs has been widely applied in estimating phenology from the perspective of greenness, while the performance of satellite derived GCC is not well understood. We used flux tower GPP from seven deciduous broadleaf forest (DBF) and three grassland (GRA) sites over the Northern Hemisphere. The aim was to compare phenological events with GCC (obtained from digital repeat photographs and satellite remote sensing (GCC(MODIS))) and the enhanced vegetation index (EVI). Meanwhile, we also explored the performance of these three indices in simulating GPP utilizing the light use efficiency (LUE) model at the DBF and GRA sites. Phenology retrieved by GCC, GCC(MODIS), and EVI was all significantly correlated with GPP-estimated values at all sites (P < 0.001). It indicates the comparable performance of GCC, GCC(MODIS), and EVI in estimating phenological events. The RMSE values between the GPP and three indices-estimated phenological events revealed that the three indices excelled in estimating the start of growing season (SOS) compared to the end of growing season (EOS) and the length of growing season (GSL). In terms of GPP estimation performance, the R-2 values of GCC(MODIS) and EVI-estimated GPP increased by 2 % and 1 %, respectively, compared to GCC-simulated GPP. Meanwhile, the RMSE values for GCC(MODIS) and EVI reduced by 0.08, and the bias values were reduced by 0.06 and 0.12, respectively. This study showed that GCC obtained from satellite remote sensing data could be utilized as an effective tool in extracting phenology and has a great potential to estimate GPP, at least across the DBF and GRA regions.