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Remote Sens. 2015, 7(5), 5901-5917;

Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance

School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500, The Netherlands
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
Author to whom correspondence should be addressed.
Academic Editors: Yoshio Inoue and Prasad S. Thenkabail
Received: 13 January 2015 / Revised: 15 April 2015 / Accepted: 5 May 2015 / Published: 11 May 2015
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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 (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index ( , is derivative reflectance) model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and 1319 nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (−0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation. View Full-Text
Keywords: canopy level; grass nutrients; hyperspectral reflectance; statistical modeling canopy level; grass nutrients; hyperspectral reflectance; statistical modeling

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.; Wang, T.; Skidmore, A.K.; Shi, T.; Wu, G. Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance. Remote Sens. 2015, 7, 5901-5917.

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