Preprints
https://doi.org/10.5194/hess-2022-63
https://doi.org/10.5194/hess-2022-63
 
17 Feb 2022
17 Feb 2022
Status: this preprint is currently under review for the journal HESS.

Integrating process-based information into an ANN for root-zone soil moisture prediction

Roiya Souissi1, Mehrez Zribi1, Chiara Corbari2, Marco Mancini2, Sekhar Muddu3, Sat Kumar Tomer4, Deepti B. Upadhyaya3,4, and Ahmad Al Bitar1 Roiya Souissi et al.
  • 1CESBIO—Centre d’Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
  • 2Department of Civil and Environmental Engineering (DICA), Polytechnic University of Milan, 20133 Milano, Italy
  • 3Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India
  • 4Satyukt analytics Pvt Ltd, Sanjay Nagar Main Rd, MET Layout, Bengaluru, Karnataka 560094, India

Abstract. Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change, and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-based inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporative efficiency computed based on a MODIS remote sensing dataset and simplified analytical model, while vegetation growth was expressed through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data hosting facility. The results indicate that the integration of process-based features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semi-arid areas, for instance, performance enhancement was observed when the evaporative efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied on observation sites in Tunisia, Italy and South-India that are not part of ISMN. The results reveal that joint use of surface soil moisture, evaporative efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-based features were integrated into the ANN models in addition to SSM. However, process-based features have no to little added value in temperate to tropical conditions.

Roiya Souissi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-63', Anonymous Referee #1, 21 Mar 2022
    • AC1: 'Reply on RC1', Roiya Souissi, 02 May 2022
  • RC2: 'Comment on hess-2022-63', Anonymous Referee #2, 07 Apr 2022
    • AC2: 'Reply on RC2', Roiya Souissi, 02 May 2022

Roiya Souissi et al.

Roiya Souissi et al.

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Short summary
In this study, we investigate the combination of surface soil moisture information with process-based features namely evaporative efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-based features yielded more accurate predictions in the case of arid and semi-arid conditions. However, they have no to little added value in temperate to tropical conditions.