Combined Use of Airborne LiDAR and Satellite GF-1 Data to Estimate Leaf Area Index, Height, and Aboveground Biomass of Maize During Peak Growing Season

作者:Li, W (Li, Wang)[ 1 ] ; Niu, Z (Niu, Zheng)[ 1 ] ; Wang, C (Wang, Cheng)[ 2 ] ; Huang, WJ (Huang, Wenjiang)[ 2 ] ; Chen, HY (Chen, Hanyue)[ 3 ] ; Gao, S (Gao, Shuai)[ 1 ] ; Li, D (Li, Dong)[ 2 ] ; Muhammad, S (Muhammad, Shakir)[ 1 ]

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

卷: 8  期: 9  页: 4489-4501  特刊: SI

DOI: 10.1109/JSTARS.2015.2496358

出版年: SEP 2015

摘要

A fast and efficient estimation of crop biophysical parameters is significantly important inmany agricultural, ecological, and meteorological applications. This study investigated the potential of airborne LiDAR and satellite GF-1 data for estimating three biophysical parameters of maize: 1) leaf area index (LAI); 2) average canopy height (H-canopy); and 3) aboveground biomass (AGB) during the peak growing season. First, classification data of maize was produced using normalized surface height, GF-1 NDVI, and terrain slope through decision-making. Second, four representative remotely sensed (RS) metrics which have been widely used in forest studies were tested to develop multiplicative models with similar shapes for estimating each biophysical parameter of maize, respectively. Third, the estimation results were obtained and validated through leave-one-out cross-validation method yielding a root-mean-square error (rmse) of 0.37 for LAI, 0.17 m for H-canopy, and 0.49 kg/m(2) for AGB. Finally, contributions to the estimation models from each RS metric were analyzed, and spatial patterns of the biophysical parameters across the entire study area were mapped. Based on these results, the following conclusions were drawn. 1) The four selected metrics from airborne LiDAR and satellite GF-1 data are also applicable and promising in estimating biophysical parameters of maize during the peak growing season. 2) Multiplicative model was proved to be a fast, simple but effective alternative by combining LiDAR-derived structure information and spectral content from GF-1 NDVI. These conclusions provide valuable information for estimation of biophysical parameters of maize during the peak growing season.

通讯作者地址: Li, W (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Lab Digital Earth Sci, Beijing 100094, Peoples R China

[ 3 ] Fujian Agr & Forestry Univ, Coll Resource & Environm Sci, Fuzhou 350002, Peoples R China