Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 583-586, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 583-586, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

Z. Hu, L. Xu, and B. Yu Z. Hu et al.
  • CUGB, School Of land Science And Technology , Beijing, China

Keywords: Soil Moisture Retrieval, Microwave Remote Sensing, Deep Learning, Convolutional Neural networks, GPU

Abstract. A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.