Recent advances in data-driven modeling of remote sensing applications in hydrology

Abstract

Artificial neural networks (ANNs) are very effective statistical models for (1) extracting significant features or characteristics from complex data structures and/or for (2) learning nonlinear relationships involved in any input–output mapping. Another interesting aspect of ANN modeling is the fact that overall performance of these models is not greatly hampered by the presence of error-corrupted values in some input nodes. ANNs have gained interest in remote sensing applications as valuable inverse models that can retrieve physical characteristics of interest, such as precipitation, from remote sensing measurements collected from radars or satellites. The spatial coverage and high resolution of remote sensing measurements relative to ground-based measurements can improve the hydrological modeling of the water cycle at both local and global scales. This review paper intends to present recent advances in artificial neural network modeling of remote sensing applications in hydrology. This paper focuses on precipitation and snow water equivalent (SWE) retrievals from remote sensing data.