Bryan Leavitt

TL;DR: In this article, the authors proposed a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm).

TL;DR: In this article, the authors used an independent data set for irrigated and rainfed maize showed robustness of the technique; RMSE of GPP prediction was less than 0.27 mg CO2/m 2 s.

TL;DR: In this paper, the authors evaluated the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters.

TL;DR: The objective of this paper is to remotely evaluate the phenological development of maize in terms of both biomass accumulation and reproductive organ appearance by means of the recently developed visible atmospherically resistant indices, derived from spectral reflectance data.

TL;DR: In this article, the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition was investigated.