Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations

Abstract

Previous studies have shown the potential of remote-sensing tools to monitor coastal wetlands at a landscape scale. Several biophysical parameters are typically used to evaluate ecosystem conditions such as the chlorophyll content, an indicator of photosynthesis activity. In natural environments characterized by a large fraction of standing litter–such as the marshes on the Atlantic coast of the Buenos Aires Province–spectral indices designed for green vegetation change their typical response and, hence, their biophysical interpretation requires more attention. In this work, a theoretical study was performed to determine if it is possible to detect and eventually quantify the abrupt reductions of leaf chlorophyll content ((Formula presented.)) in Sporobolus densiflorus–the dominant vegetation in these marshes–using hyperspectral data. To achieve this, in situ radiometric measurements in the VIS-NIR-SWIR (Visible, Near-Infrared and Short Wave Infrared) spectral region and biological data, acquired over S. densiflorus specimens in several campaigns, were used to set up an inversion procedure based on the radiative transfer model PROSAIL. By applying this model, simulated reflectances that fit the measured reflectances were obtained and by means of this inversion a theoretical canopy reflectance data set for S. densiflorus was modelled using the PROSAIL parameters. The performance of several vegetation indices typically used to estimate chlorophyll content was studied using the simulated and modelled reflectances, among which MTCI (MERIS Terrestrial Chlorophyll Index), Macc and MCARI/OSAVI (Modified Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Index) indices showed significant correlation with (Formula presented.). By means of addressing the performance of these indices together with BLRs (Baseline Residuals), a two-step VI-LUT (Vegetation Index–Look-Up Table) inversion model was proposed to retrieve (Formula presented.), which first corrects the effect of variable leaf area index (LAI) using a BLR index (using bands at 800, 1100 and 1300 nm), and then retrieves (Formula presented.) by either using MCARI/OSAVI or another BLR (using bands at 657, 672 and 700 nm). Even though both procedures perform similarly well in the estimation of (Formula presented.) (coefficient of determination (Formula presented.) about 0.6) the two steps BLR-based approach is preferred, given that these indices are a priori less sensitive to undesired atmospheric effects.