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

Abstract

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.

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

“…Additionally, modeled irrigation schemes generally ignore the source of applied water (i.e., surface water or groundwater), thus not allowing an integrated water resource analysis. (9) Considering that there has been increasing interest in understanding both the role of the irrigation on land-atmosphere interactions [148] and the impact of irrigation on water resources [149], coupling remote sensing information with land surface models for an improved representation of anthropogenic activities seems to be a key challenge to be addressed in the near future (e.g., [119,150]).…”

Irrigation quantification through backscatter data assimilation with a buddy check approach

“…In the last years, several methods have been developed to map and quantify irrigation by making use of satellite remote sensing data (Massari et al, 2021). These observations (optical, microwave and gravimetric measurements) are used alone, combined with each other, or with models.…”

“…Based on these limitations, Modanesi et al (2022) decided to directly assimilate the S1 backscatter signal into the Noah-MP LSM (Niu et al, 2011) equipped with a sprinkler irrigation scheme (Ozdogan et al, 2010), where irrigation is dynamically modeled and triggered based on a soil moisture deficit approach. The DA updated SSM and LAI using an Ensemble Kalman Filter (EnKF) and a calibrated Water Cloud Model (WCM; Attema & Ulaby, 1978;Modanesi et al, 2021) as observation operator to map between SSM, LAI and backscatter. The idea is to provide the model with a better initial state, in terms of soil moisture and vegetation, to improve the triggering and estimation of irrigation.…”

“…We set up synthetic experiments based on the system of Modanesi et al (2022) with the goal to investigate the exact benefits and shortcomings of S1 backscatter DA and to improve the DA system with irrigation modeling (Section 2). In this context, synthetic backscatter observations are generated from a nature run (also called 'truth') with a calibrated WCM as observation operator (Modanesi et al, 2021). These observations are then assimilated into model simulations, for which the forcings are altered compared to the reference nature run.…”

Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data

“…Remote sensing is a unique and valuable tool, capable of addressing the lack of large-scale precise information over irrigation practices, and overcoming the limitations of analyses based on in-situ observations, which are often prone to inconsistencies and gaps in the information collected. Current results in the field of remote sensing for irrigation practices featured the creation of global or regional scale maps of irrigated areas [14], [15], [16], [17], [18], [19], irrigation timings [20], [21], [22] and quantification of irrigation amounts at variable resolutions [23], [24], [25], [26], [27], [28], [29], [30]. In particular, for studies oriented on the mapping of irrigated areas, remote sensing data are often coupled with Machine Learning (ML) models, proving to be successful with both supervised [14], [15] and unsupervised approaches [16].…”