Indices-based approach for crop chlorophyll content retrieval from hyperspectral data

This study aims at using forward model simulations and ground-measurements (biophysical and spectral) to estimate chlorophyll concentration from hyperspectral data and imagery. Its specific objectives were: (i) to evaluate various combinations of indices as estimators of chlorophyll content from simulated spectra (PROSPECT and SAILH); (ii) to establish chlorophyll predictive equations using spectral indices determined from field spectra and corresponding chlorophyll concentrations; (iii) to assess the effect of crop type (corn and wheat) on these relationships; and (iv) to validate and compare the indices’ prediction capability using hyperspectral images and ground truth measurements. Hence, intensive field campaigns were organized during the growing seasons of 2000, 2004, and 2005 in order to collect ground spectra and corresponding leaf chlorophyll content values as well as crop growth measures. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT- SAILH simulated data and ground measured datasets, indicating that both spectral measurements and radiative transfer models hold comparable potential for quantitative retrieval of crop foliar pigments. The dataset used showed that crop type had a clear influence on the establishment of predictive equations as well as on their validation. Moreover, corn and wheat data have led to contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Indices TCARI/TRDVI and TCI/TRDVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.