Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venμs sensors

This study developed a set of algorithms for satellite mapping of green leaf area index (LAI) in C3 and C4 crops. In situ hyperspectral reflectance and green LAI data, collected across eight years (2001-2008) at three AmeriFlux sites in Nebraska USA over irrigated and rain-fed maize and soybean, were used for algorithm development. The hyperspectral reflectance was resampled to simulate the spectral bands of sensors aboard operational satellites (Aqua and Terra: MODIS, Landsat: TM/ETM+), a legacy satellite (Envisat: MERIS), and future satellites (Sentinel-2, Sentinel-3, and Venμs). Among 15 vegetation indices (VIs) examined, five VIs - wide dynamic range vegetation index (WDRVI), green WDRVI, red edge WDRVI, and green and red edge chlorophyll indices - had a minimal noise equivalent for estimating maize and soybean green LAI ranging from 0 to 6.5 m² m^-2. The algorithms were validated using MODIS, TM/ETM+, and MERIS satellite data. The root mean square error of green LAI prediction in both crops from all sensors examined in this study ranged from 0.73 to 0.95 m² m^-2 and coefficient of variation ranged between 17.0 and 29.3%. The algorithms using the red edge bands of MERIS and future space systems Sentinel-2, Sentinel-3, and Venμs allowed accurate green LAI estimation over areas containing maize and soybean with no re-parameterization.