Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3263-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3263-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Roiya Souissi
CORRESPONDING AUTHOR
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
Mehrez Zribi
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
Chiara Corbari
Department of Civil and Environmental Engineering (DICA), Polytechnic University of Milan, 20133 Milan, Italy
Marco Mancini
Department of Civil and Environmental Engineering (DICA), Polytechnic University of Milan, 20133 Milan, Italy
Sekhar Muddu
Department of Civil Engineering, Indian Institute of Science,
Bangalore 560012, India
Sat Kumar Tomer
Satyukt analytics Pvt Ltd, Sanjay Nagar Main Rd, MET Layout,
Bengaluru, Karnataka 560094, India
Deepti B. Upadhyaya
Department of Civil Engineering, Indian Institute of Science,
Bangalore 560012, India
Satyukt analytics Pvt Ltd, Sanjay Nagar Main Rd, MET Layout,
Bengaluru, Karnataka 560094, India
Ahmad Al Bitar
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
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We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
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We developed an EO-based agricultural drought index (ADMOS) for irrigation management. ADMOS identifies drought levels using rainfall, soil moisture, surface temperature and vegetation anomalies from multiple satellite data. ADMOS was tested in two Italian areas, diverse in climate, crop and irrigation. In one, ADMOS and irrigation volumes were negatively correlated; while in the other, no correlation was found, because the same irrigation is applied every year.
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In the context of major changes, the southern Mediterranean area faces serious challenges with low and continuously decreasing water resources mainly attributed to agricultural use. A method for projecting irrigation water demand under both anthropogenic and climatic changes is proposed. Time series of satellite imagery are used to determine a set of semiempirical equations that can be easily adapted to different future scenarios.
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Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.
In this study, we investigate the combination of surface soil moisture information with...