14 Jun 2021

14 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Sara Modanesi1,2,4, Christian Massari1, Alexander Gruber2, Hans Lievens2, Angelica Tarpanelli1, Renato Morbidelli3, and Gabrielle 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
  • 3Dept. of Civil and Environmental Engineering of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
  • 4DICEA Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy

Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation.

The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

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-2021-273', Anonymous Referee #1, 21 Jul 2021
    • AC1: 'Reply on RC1', Sara Modanesi, 23 Aug 2021
  • RC2: 'Comment on hess-2021-273', Anonymous Referee #2, 09 Aug 2021
    • AC2: 'Reply on RC2', Sara Modanesi, 23 Aug 2021

Sara Modanesi et al.

Sara Modanesi et al.


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Short summary
Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.