Remote sensing of plant phenology as an indicator of climate change and for mapping land
cover has received significant scientific interest in the past two decades. The advancing of …

Most crop models were developed and tested in homogeneous field conditions. However,
these crop models are increasingly applied beyond the field scale for larger regions …

In cotton, an optimal balance between vegetative and reproductive growth will lead to high
yields and water-use efficiency. Remote sensing estimations of vegetation variables such as …

Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has
widened the scope of day and night vegetation monitoring, even when cloud cover limits …

Recent advances in remote sensing and machine learning show potential for improving
irrigation use efficiency. In this study, two independent methods to determine the irrigation …

Crop monitoring throughout the growing season is key for optimized agricultural production.
Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high …

Spatial information embedded in a crop model can improve yield prediction. Leaf area index
(LAI) is a well-known crop variable often estimated from remote-sensing data and used as …

The objective of the Ground to Space CALibration Experiment (G-SCALE) is to demonstrate
the use of convex mirrors as a radiometric and spatial calibration and validation technology …

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area
Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study …

Smartphone application usage has increased exponentially over the past decade. The
potentiality of smartphone applications as a tool for various decision-making processes is …