Remote Sensing | |
Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance | |
Junjie Wang2  Tiejun Wang3  Andrew K. Skidmore3  Tiezhu Shi2  Guofeng Wu4  Yoshio Inoue1  | |
[1] School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China;;School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China; E-Mails:;Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500, The Netherlands; E-Mails:;Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information & Shenzhen Key Laboratory of Spatial-Temporal Smart Sensing and Services & College of Life Sciences, Shenzhen University, Shenzhen 518060, China | |
关键词: canopy level; grass nutrients; hyperspectral reflectance; statistical modeling; | |
DOI : 10.3390/rs70505901 | |
来源: mdpi | |
【 摘 要 】
The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190012914ZK.pdf | 1109KB | download |