Paper Info
Title
Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods.
Abstract
Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m(2)/m(2)) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.
Year
DOI
Venue
2020
10.3390/s20092460
SENSORS
Keywords
DocType
Volume
leaf area index (LAI),look-up table (LUT),Gaussian process regression (GPR),GF-1,PROSAIL
Journal
20
Issue
ISSN
Citations 
9
1424-8220
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yangyang Zhang100.68
Jian Yang213.09
Xiuguo Liu31010.88
Lin Du4247.43
Shuo Shi5156.21
Jia Sun601.01
Biwu Chen701.01