Remote estimation of leaf area index and green leaf biomass in maize canopies

TLDR

Leaf area index is crucial for climate modeling, yield forecasting, and other studies, yet existing spectral indices perform poorly at moderate‑to‑high LAI, highlighting the need for improved remote sensing methods. The study proposes a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels (green/red edge and NIR). The method uses reflectance measurements in the green or red edge (~550–700 nm) and NIR (>750 nm) to derive LAI and biomass. Applied to maize fields, the technique accurately estimates LAI from 0 to over 6.

Abstract

Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate‐to‐high LAI. In this paper, we propose 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). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6.