Informative spectral bands for remote green LAI estimation in C3 and C4 crops

Authors

Oz Kira, Technion-Israel Institute of Technology
Anthony L. Nguy-Robertson, University of Nebraska-LincolnFollow
Timothy J. Arkebauer, University of Nebraska - LincolnFollow
Raphael Linker, Technion Israel Institute of technology
Anatoly A. Gitelson, University of Nebraska - LincolnFollow

Document Type

Article

Date of this Version

2016

Citation

Published in Agricultural and Forest Meteorology 218–219 (2016), pp. 243–249.

doi 10.1016/j.agrformet.2015.12.064

Comments

Copyright © 2015 Elsevier B.V. Used by permission.

Abstract

Green leaf area index (LAI) provides insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast and nondestructive estimation of green LAI. A number of methods have been used for the estimation of green LAI; however, the specific spectral bands employed varied widely among the methods and data used. Our objectives were (i) to find informative spectral bands retained in three types of methods, neural network (NN), partial least squares (PLS) regression and vegetation indices (VI), for estimating green LAI in maize (a C4 species) and soybean (a C3 species); (ii) to assess the accuracy of the algorithms estimating green LAI using a minimal number of bands for each crop and generic algorithms for the two crops combined. Hyperspectral reflectance and green LAI of irrigated and rainfed maize and soybean were taken during eight years of observations (altogether 24 field-years) in very different weather conditions. The bands retained in the best NN, PLS and VI methods were in close agreement. The validity of these bands was further confirmed via the uninformative variable elimination PLS technique. The red edge and the NIR bands were selected in all models and were found the most informative. Identifying informative spectral bands across all four techniques provided insight into spectral features of reflectance specific for each species as well as those that are common to species with different leaf structures, canopy architectures and photosynthetic pathways. The analyses allowed development of algorithms for estimating green LAI in soybean and maize with no re-parameterization. These findings lay a strong foundation for the development of generic algorithms which is crucial for remote sensing of vegetation biophysical parameters.