Soil Moisture Prediction Model from ERA5-Land Parameters using a Deep Neural Networks

Daouda Diouf, Carlos Mejia, Djibril Seck


In a global context of scarcity of water resources, accurate prediction of soil moisture is important for its rational use and management. Soil moisture is included in the list of Essential Climate Variables. Because of the complex soil structure, meteorological parameters and the diversity of vegetation cover, it is not easy to establish a predictive relationship of soil moisture. In this paper, using the large amounts of data obtained in West Africa, we set up a deep neural network to establish an estimation of soil moisture for the two first layers and its prediction temporally and spatially. We construct deep neural network model which predicts soil moisture layer 1 and layer 2 multiple days in the future. Results obtained for accuracy training and test are greater than 93 %. The mean absolute errors are very low and vary between 0,01 to 0,03 m3/m3.


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