Preprints
https://doi.org/10.5194/hess-2022-61
https://doi.org/10.5194/hess-2022-61
 
02 Mar 2022
02 Mar 2022
Status: this preprint is currently under review for the journal HESS.

Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

Sara Modanesi1,2,3, Christian Massari1, Michel Bechtold2, Hans Lievens2, Angelica Tarpanelli1, Luca Brocca1, Luca Zappa4, and Gabriëlle J. M. De Lannoy2 Sara Modanesi et al.
  • 1Research Institute for Geo-hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
  • 2Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
  • 3DICEA Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy
  • 4Department of Geodesy and Geoinformation, Technische Universität Wien (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria

Abstract. In recent years, the amount of water used for agricultural purposes has been rising due to an increase in food demand. However, anthropogenic water usage, such as for irrigation, is still not or poorly parameterized in regional and larger-scale land surface models (LSM). By contrast, satellite observations are directly affected by, and hence potentially able to detect, irrigation as they sense the entire integrated soil-vegetation system. By integrating satellite observations and fine-scale modelling it could thus be possible to improve estimation of irrigation amounts at the desired spatial-temporal scale.

In this study we tested the potential information offered by Sentinel-1 backscatter observations to improve irrigation estimates, in the framework of a data assimilation (DA) system composed by the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a Water Cloud Model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and leaf area index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast backscatter residuals back to updates in soil moisture and LAI through an ensemble Kalman filter (EnKF).

The benefits of Sentinel-1 backscatter observation in two different polarizations (VV and VH) were tested in two separate DA experiments, performed over two irrigated sites, the first one located in the Po Valley (Italy) and the second one located in northern Germany. The results confirm that VV backscatter has a stronger link with soil moisture than VH backscatter, whereas VH backscatter observations introduce larger updates in the vegetation state variables. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had large impacts on the analysis, with more contradicting results obtained for the evaluation at the fine agriculture scale (i.e., field scale). Above all, this study sheds light on the limitations resulting from a poorly-parameterized sprinkler irrigation scheme which prevents improvements in the irrigation simulation due to DA, and points to future developments needed to improve the system.

Sara Modanesi 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-2022-61', Anonymous Referee #1, 10 Mar 2022
  • RC2: 'Comment on hess-2022-61', Anonymous Referee #2, 15 Apr 2022
  • EC1: 'Comment on hess-2022-61', Narendra Das, 19 May 2022

Sara Modanesi et al.

Sara Modanesi et al.

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Short summary
Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for the global water resource management, the comprehension of human impacts on the water cycle and to identify main challenges and outlooks for future research.