Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects
The saturation of spectral reflectance within densely vegetated regions is a renowned
challenge that has precluded the optimal use of broad-band remotely sensed data and its …
challenge that has precluded the optimal use of broad-band remotely sensed data and its …
Remote sensing of grassland production and management—A review
Grasslands cover one third of the earth's terrestrial surface and are mainly used for livestock
production. The usage type, use intensity and condition of grasslands are often unclear …
production. The usage type, use intensity and condition of grasslands are often unclear …
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
Random Forest (RF) is a machine learning technique that has been proved to be highly
accurate in several agricultural applications. However, to yield prediction, how much this …
accurate in several agricultural applications. However, to yield prediction, how much this …
Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery
Having the ability to make accurate assessments of above ground biomass (AGB) at high
spatial resolution is invaluable for the management of dryland forest resources in areas at …
spatial resolution is invaluable for the management of dryland forest resources in areas at …
Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images
Grassland degradation has accelerated in recent decades in response to increased climate
variability and human activity. Rangeland and grassland conditions directly affect forage …
variability and human activity. Rangeland and grassland conditions directly affect forage …
[HTML][HTML] The use of machine learning methods to estimate aboveground biomass of grasslands: A review
The study of grasslands using machine learning (ML) methods combined with
proximal/remote sensing data (RS) has been steadily increasing in the last decades …
proximal/remote sensing data (RS) has been steadily increasing in the last decades …
[HTML][HTML] Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield
F Wang, Q Yi, J Hu, L **e, X Yao, T Xu… - International Journal of …, 2021 - Elsevier
The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data
choices for crop yield estimation. In most cases, spectral information derived from …
choices for crop yield estimation. In most cases, spectral information derived from …
A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning
With an increasing volume and dimensionality of Earth observation data, enhanced
integration of machine-learning methodologies is needed to effectively analyze and utilize …
integration of machine-learning methodologies is needed to effectively analyze and utilize …
Estimating aboveground biomass using Sentinel-2 MSI data and ensemble algorithms for grassland in the Sheng** Lake Wetland, China
C Li, L Zhou, W Xu - Remote Sensing, 2021 - mdpi.com
Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem
health and is critical for water purification, carbon cycle, and biodiversity conservation …
health and is critical for water purification, carbon cycle, and biodiversity conservation …
Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques
Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential,
reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis …
reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis …