Artificial Neural Networks in Remote Sensing of Hydrologic Processes

Recent progress in remote sensing technologies, coupled with ongoing and planned remote sensing missions, is expected to generate hydrologic data at spatial, temporal, and spectral resolutions never previously available. Artificial neural networks (ANNs), although at early stages of hydrologic applications, are rapidly becoming an attractive tool to characterize, model, and predict complex multisource remotely sensed hydrologic data. We review and examine the utility of ANNs for hydrologic applications, with particular emphasis on remote sensing of precipitation, soil moisture, and multisource land surface data. In addition to more popularly used multilayer feedforward networks, we also review recurrent neural networks for prediction and self-organization neural networks for spatial characterization of heterogeneous land surface processes.