Reconstruction of time-series soil moisture from AMSR2 and SMOS data by using recurrent nonlinear autoregressive neural networks

@article{Lu2015ReconstructionOT,
  title={Reconstruction of time-series soil moisture from AMSR2 and SMOS data by using recurrent nonlinear autoregressive neural networks},
  author={Zheng Lu and Linna Chai and Qinyu Ye and Tao Zhang},
  journal={2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2015},
  pages={980-983}
}
Soil moisture (SM) is a key variable in describing land surface characteristics. However, most passive microwave sensed soil moisture products are spatially and temporally discontinuous. In this study, a recurrent autoregressive neural network was investigated for its capability to reconstruct time-series soil moisture. The train dataset was collected from the observations of AMSR2 and SMOS, along with the daily NDVI, land surface temperature (LST), precipitation (PRC) and DEM information. Then… CONTINUE READING