Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments

Davide Cammarano,Glenn Fitzgerald,Raffaele Casa,Bruno Basso

doi:10.3390/rs6042827

Davide Cammarano, Glenn Fitzgerald + Show 2 more

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https://doi.org/10.3390/rs6042827

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Abstract

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g∙N∙m−2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g∙N∙m−2) (r2 = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.

Highlights

Vegetation indices (VIs) based on spectral reflectance have been used in agricultural research for finding functional relationships between canopy characteristics and remote sensing observations for nearly four decades [1,2]
Among all the VIs studied, the Canopy Chlorophyll Content Index (CCCI) was the best index for robustly estimating canopy N content (g∙N∙m−2)
The CCCI, on the other hand, showed good correlation with biomass and Leaf Area Index (LAI), because it is based on a two-dimensional approach that compensates for an increasing of canopy biomass (Table 1)

Summary

Introduction

Vegetation indices (VIs) based on spectral reflectance have been used in agricultural research for finding functional relationships between canopy characteristics and remote sensing observations for nearly four decades [1,2]. Most indices for detecting canopy chemical component (e.g., nitrogen, chlorophyll) have been developed at the leaf scale, because it is the first step for further up-scaling to the canopy level [3]. In this case, indices are developed by using leaves of the same species, leaves from different crops or by comparing several indices with a large simulated database [4,5]. It is a function of the Leaf Area Index (LAI), leaf clumping, leaf angle distribution, vegetation cover, soil background and source-target illumination geometry [10,11,12,13,14]. At the canopy level, the correlation between canopy nitrogen concentration and canopy reflectance decreased as a function of LAI

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