文献类型：期刊文献

英文题名：A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

作者：Luo, Xin[1] Jin, Lili[2] Tian, Xin[1] Chen, Shuxin[1] Wang, Haiyi[1]

通信作者：Tian, X[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Shijiazhuang Hutuo River State Owned Forest Farm, Shijiazhuang Hutuo River Urban Forest Pk, Shijiazhuang 050051, Peoples R China

年份：2023

卷号：15

期号：11

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20232414233694);Scopus(收录号：2-s2.0-85161481191);WOS:【SCI-EXPANDED(收录号:WOS:001005815600001)】；

基金：This research was funded by the Fundamental Research Funds of CAF (CAFYBB2021SY006), and The National Science and Technology Major Project of China's High Resolution Earth Observation System (21-Y20B01-9001-19/22).

语种：英文

外文关键词：LAI; Landsat8 OLI; GF-1 WFV; STARFM; SA-BPNN

摘要：The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R-2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R-2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R-2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.