These are very topical and truly difficult problems that are being encountered in the remote sensing community. Neural networks can provide solutions to many of these problems. The intent of this paper is to raise the awareness of the remote sensing community to the advantages of advancements in the use of neural networks. Neural networks have significant advantages as compared to traditional techniques when applied to both measurement and modeling studies. Neural networks can provide accurate initial models for extracting vegetation variables when an adequate amount of data is available.
Networks provide a performance standard for evaluating existing physically-based models.
Networks can be used as a tool to find a set of variables that are relevant to the desired variables to be inferred for measurement and modeling studies. Neural networks adapt to incorporate new data sources that would be difficult or impossible to use with conventional techniques. Neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. This power and flexibility is gained by repeating nonlinear activation functions in a network structure. This unique structure allows the neural network to learn complex functional relationships between the input and output data that can not be envisioned by a researcher.