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

Abstract

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.