Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery.

Physical principles applied to remote sensing data are key to successfully quantifying vegetation physiological condition from the study of the light interaction with the canopy under observation. We used the fluorescence-reflectance-transmittance (FRT) and PROSPECT leaf models to simulate reflectance as a function of leaf biochemical and fluorescence variables. A series of laboratory measurements of spectral reflectance at leaf and canopy levels and a modeling study were conducted, demonstrating that effects of chlorophyll fluorescence (CF) can be detected by remote sensing. The coupled FRT and PROSPECT model enabled CF and chlorophyll a + b (Ca + b) content to be estimated by inversion. Laboratory measurements of leaf reflectance (r) and transmittance (t) from leaves with constant Ca + b allowed the study of CF effects on specific fluorescence-sensitive indices calculated in the Photosystem I (PS-I) and Photosystem II (PS-II) optical region, such as the curvature index [CUR; (R675.R690)/R2(683)]. Dark-adapted and steady-state fluorescence measurements, such as the ratio of variable to maximal fluorescence (Fv/Fm), steady state maximal fluorescence (F'm), steady state fluorescence (Ft), and the effective quantum yield (delta F/F'm) are accurately estimated by inverting the FRT-PROSPECT model. A double peak in the derivative reflectance (DR) was related to increased CF and Ca + b concentration. These results were consistent with imagery collected with a compact airborne spectrographic imager (CASI) sensor from sites of sugar maple (Acer saccharum Marshall) of high and low stress conditions, showing a double peak on canopy derivative reflectance in the red-edge spectral region. We developed a derivative chlorophyll index (DCI; calculated as D705/D722), a function of the combined effects of CF and Ca + b content, and used it to detect vegetation stress.