Large-scale modeling of environmental variables is an increasingly complex but necessary
task. In this paper, we review the literature on using machine learning to cope with …

Abstract Machine learning algorithms find frequent application in spatial prediction of biotic
and abiotic environmental variables. However, the characteristics of spatial data, especially …

Recent substantial advances in high-throughput field phenoty** have provided plant
breeders with affordable and efficient tools for evaluating a large number of genotypes for …

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote
sensing are becoming standard analytical tools in the geosciences. A series of studies has …

Several spatial and non‐spatial Cross‐Validation (CV) methods have been used to perform
map validation when additional sampling for validation purposes is not possible, yet it is …

Leaf chlorophyll content (LCC) is an important indicator for evaluating crop nutritional status
and environmental stress. For the purpose of achieving rapid, non-destructive, and real-time …

Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes
from space. Augmenting field measurement of leaf traits with remote sensing provides a …

Improving genetic yield potential in major food grade crops such as soybean (Glycine max
L.) is the most sustainable way to address the growing global food demand and its security …

Species distribution models are generic empirical techniques that have a number of
applications. One of these applications is to determine which environmental conditions are …

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC),
and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched …