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 …

Surface soil moisture is a key variable used to describe water and energy exchanges at the
land surface/atmosphere interface. Passive microwave remotely sensed data have great …

Many advances in spaceborne instrumentation, remote sensing, and data analysis have
occurred in recent years, but until now there has been no book that reflects these advances …

Snow on the ground is a complex multiphase photochemical reactor that dramatically
modifies the chemical composition of the overlying atmosphere. A quantitative description of …

Understanding fire behavior characteristics and planning for fire management require maps
showing the distribution of wildfire fuel loads at medium to fine spatial resolution across …

Satellite‐based remotely sensed data have the potential to provide hydrologically relevant
information about spatially and temporally varying physical variables. A methodology for …

Two neural network algorithms trained by a physical vegetation model are used to retrieve
soil moisture and vegetation variables of wheat canopies during the whole crop cycle. The …

Seasonal alpine snow contributes significantly to the water resource. It plays a crucial role in
regulating the environmental feedback and from the perspective of socio-economic …

A broad class of neural network (NN) applications dealing with the remote measurements of
geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and …

Standard regression methods applied to canopies within a single homogeneous soil type
yield good results for estimating leaf area index (LAI) but perform unacceptably when …