Natali et al. (2026) Field-scale irrigation estimation using Sentinel-1 and Sentinel-2 data within a soil water balance model coupled to the Water Cloud Model

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-09-01
• Authors: Martina Natali, Sara Modanesi, Domenico De Santis, Gabriëlle J. M. De Lannoy, Susan C. Steele‐Dunne, Alessia Flammini, Alessio Domeneghetti, Christian Massari
• DOI: 10.1016/j.rse.2026.115639

Research Groups

• Department of Civil and Environmental Engineering, University of Perugia
• Institute of Geoscience, National Research Council of Italy (CNR-GEO)
• Department of Earth and Environmental Sciences, KU Leuven
• Department of Geoscience and Remote Sensing, TU Delft
• Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna

Short Summary

This study presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves jointly calibrating the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components.

Objective

• Estimate irrigation at the field scale without relying on ground measurements.
• Jointly calibrate the WCM and SWB models using Sentinel-1 data.

Study Configuration

• Spatial Scale: Field-scale (10 m resolution) within an irrigated district in the Po Valley, Italy.
• Temporal Scale: Daily to bi-weekly irrigation estimates, with a temporal resolution of about 1.7 days.

Methodology and Data

• Models used: Water Cloud Model (WCM) and two-layer Soil Water Balance (SWB) model.
• Data sources: Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data, along with reanalysis meteorological data from MERIDA.

Main Results

• Average time series correlations of 0.43 to 0.95 are obtained for daily to bi-weekly irrigation estimates, respectively.
• Associated bias amounts to 0.15 mm/day and 2.25 mm/15 days, and around 25 mm for annual estimates.

Contributions

• The proposed framework introduces a novel approach to estimate irrigation at the field scale without relying on ground measurements.
• The joint calibration of WCM and SWB models using Sentinel-1 data enables the direct estimation of irrigation volumes while maintaining physical consistency among vegetation dynamics, soil moisture variations, and SAR backscatter.

Funding

• This research was funded by the European Space Agency (ESA) through the project "Sentinel-1 for Irrigation Monitoring".
• Additional funding was provided by the Italian National Research Council (CNR) through the project "Irrigation Management in Agricultural Regions".

Citation

@article{Natali2026Fieldscale,
  author = {Natali, Martina and Modanesi, Sara and Santis, Domenico De and Lannoy, Gabriëlle J. M. De and Steele‐Dunne, Susan C. and Flammini, Alessia and Domeneghetti, Alessio and Massari, Christian},
  title = {Field-scale irrigation estimation using Sentinel-1 and Sentinel-2 data within a soil water balance model coupled to the Water Cloud Model},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115639},
  url = {https://doi.org/10.1016/j.rse.2026.115639}
}