Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment.

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content ( ) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review.

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture.

Understanding deep learning in land use classification based on Sentinel-2 time series.

The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions.

Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options.

For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring.

Ensemble approach for potential habitat mapping of invasive spp in Turkana, Kenya.

Aim: spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote-sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas.