Attributes of neural networks for extracting continuous vegetation variables from optical and radar

Efficient algorithms that incorporate different types of spectral data and ancillary data are being developed to extract continuous vegetation variables. Inferring continuous variables implies that functional relationships must be found among the predicted variable(s), the remotely sensed data and the ancillary data. Neural networks have attributes which facilitate the extraction of vegetation variables. The advantages and power of neural networks for extracting continuous vegetation variables using optical and/or radar data and ancillary data are discussed and compared to traditional techniques. Studies that have made advances in this research area are reviewed and discussed. 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. Many practical problems occur when inverting physically based models using traditional techniques and neural ne...