贾坤等:Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data
被阅读 373 次
2018-01-30
Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data
作者:Jia, K (Jia, Kun)[ 1,2 ] ; Li, YW (Li, Yuwei)[ 1,2 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3 ] ; Wei, XQ (Wei, Xiangqin)[ 4 ] ; Yao, YJ (Yao, Yunjun)[ 1,2 ]
REMOTE SENSING
卷: 9  期: 11
文献号: 1121
DOI: 10.3390/rs9111121
出版年: NOV 2017
 
摘要
Fractional vegetation cover (FVC), or green vegetation fraction, is an important parameter for characterizing conditions of the land surface vegetation, and also a key variable of models for simulating cycles of water, carbon and energy on the land surface. There are several types of FVC estimation models using remote sensing data, and evaluating their performance over a specific region is of great significance. Therefore, this study firstly evaluated three types of FVC estimation models using Landsat 7 ETM+ data in an agriculture region of Heihe River Basin, China, and then proposed a combination strategy from different individual models to improve the FVC estimation accuracy, which employed the multiple linear regression (MLR) and Bayesian model average (BMA) methods. The validation results indicated that the spectral mixture analysis model with three endmembers (SMA3) achieved the best FVC estimation accuracy (determination coefficient (R-2) = 0.902, root mean square error (RMSE) = 0.076) among the seven individual models using Landsat 7 ETM+ data. In addition, the MLR and BMA combination methods could both improve FVC estimation accuracy (R-2 = 0.913, RMSE = 0.063 and R-2 = 0.904, RMSE = 0.069 for MLR and BMA, respectively). Therefore, it could be concluded that both MLR and BMA combination methods integrating FVC estimates from different models using Landsat 7 ETM+ data could effectively weaken the estimation errors of individual models and improve the final FVC estimation accuracy.
 
通讯作者地址: Jia, K (通讯作者)
Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
通讯作者地址: Jia, K (通讯作者)
Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China
[ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China