Remote sensing technology allows to provide information about biochemical and
biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems …

The accurate estimation of Leaf area index (LAI) is of great importance for evaluating crop
growth in precision agriculture. Although previous studies have confirmed great advantages …

Radiative transfer models (RTMs) provide reliable information about crop yield and traits
with high resource efficiency. In this study, we have conducted a systematic literature review …

Real-time and accurate assessment of leaf chlorophyll content (C ab) will be significant for
monitoring plant physiological status. Development of hybrid methods advances the …

Map** crop within-field yield variability provide an essential piece of information for
precision agriculture applications. Leaf Area Index (LAI) is an important parameter that …

Chlorophyll content is a key trait for understanding the functioning of agroforestry
ecosystems and has important implications for leaf and canopy photosynthesis. However …

Canopy water content (CWC) is an important indicator of crop growth. Currently, the hybrid
approach, which combines the physical model and machine learning (ML) method, is …

Wheat stripe rust is the main cause of yield loss in winter wheat. For nondestructive
monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring …

Detailed spatial representations of terrestrial vegetation are essential for precision
agricultural applications and the monitoring of land cover changes in heterogeneous …

Context Leaf area index (LAI) is a vital indicator to identify the crop growth condition and to
infer crop yield and water consumption. The generality of empirical models for estimating LAI …