Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely
sensed data. Although significant progress has been made in image classification based …

This paper begins with a review of the history of remote sensing algorithms for the
determination of particulate inorganic carbon (PIC; aka calcium carbonate), primarily …

Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is
imperative for high-throughput phenoty** in precision agriculture. Although fusion of data …

This paper evaluates state-of-the-art parametric and nonparametric approaches for the
estimation of leaf chlorophyll content (Chl), leaf area index, and fractional vegetation cover …

Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of
freshwater ecosystems and to humans who use these resources for drinking and recreation …

Precision agriculture requires high-resolution information to enable greater precision in the
management of inputs to production. Actionable information about crop and field status must …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Gaussian process models constitute a class of probabilistic statistical models in which a
Gaussian process (GP) is used to describe the Bayesian a priori uncertainty about a latent …

This letter introduces the epsiv-Huber loss function in the support vector regression (SVR)
formulation for the estimation of biophysical parameters extracted from remotely sensed …