Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (Vls) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these Vls may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.

Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various Vls computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected Vls included some classical Vls from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red edge and near-infrared domains. Overall, mND(blue)[728,850] defined as (R-440-R-728)/(R-440+R-850) and computed over the brightest green pixels obtains the best correlations with C-ab for spatial resolutions finer than 8.8 cm with a root mean square error of prediction better than 2.6 mu g/cm(2). Conversely, mND(blue)[728,8501 poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between C-ab and these canopy structure variables. As mND(blue)[728,8501 can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve C-ab from millimeter- to centimeter-scale reflectance imagery. (C) 2017 Elsevier Inc. All rights reserved.