Jiangui Liu

TL;DR: This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology.

TL;DR: In this article, a regression model was used to estimate the green LAI of field crops from optical remote sensing data using a few vegetation indices, including the normalized difference vegetation index, the optimized soil adjusted vegetation index (OSAVI), the two band enhanced vegetation index(EVI2), and the modified triangular vegetation indices (MTVI2), with a coefficient of determination above 0.83.

TL;DR: In this article, an approach to integrate crop stressors and crop descriptors derived from optical remote sensing data with the Monteith radiation use efficiency model was developed for estimating crop aboveground dry biomass and yield.

TL;DR: In this paper, the authors explored the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the VIS-based VIs.

TL;DR: In this article, a histogram-based threshold technique was used to separate green vegetation tissues from background soil and residue materials in order to derive the canopy vertical gap fraction from the digital photos.