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

Abstract

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 (LSMs). 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 of 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 observations 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 a large impact 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.

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

“…An alternative way to assimilate satellite observations is to directly ingest level-1 observations (i.e., brightness temperature or radar backscatter), instead of retrievals Reichle, 2016a, 2016b;Lievens et al, 2017aLievens et al, , 2017bModanesi et al 2022). In particular, Modanesi et al (2022) assimilated 1 km Sentinel-1 backscatter (γ 0 ) observations into the Noah MP LSM, equipped with a sprinkler irrigation scheme into the National Aeronautics and Space Administration (NASA) Land Information System (LIS) framework, for the update of both surface soil moisture and vegetation states. The authors found that DA improves the bias of irrigation simulation although limitations mainly due to irrigation model parameterization still need further improvement.…”

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

“…The WCM calibration was done for each site separately, based on Noah‐MP SSM and LAI simulations and real S1 $${\gamma }_{VV}^{0}$$ observations, following Modanesi et al. (2021, 2022). The synthetic observations are assimilated after perturbing them with different levels of Gaussian white noise, with standard deviations ranging from 0 to 0.7 dB (see Section 2.4).…”

“…It should be noted that in contrast to the setup of Modanesi et al. (2022), a perturbation bias correction method was applied in this study. This adjustment was proposed by Ryu et al.…”

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

“…The results were consistent with in situ observations over a large and intensely irrigated area, but failed over small agricultural practices, mainly due to spatial mismatch between coarse‐scale MW products (>25 km) and smallholder agricultural plots (<1 km). Despite the promising results from SM2RAIN obtained from these studies, the SM data itself remains a major challenge to this model, in that a higher resolution SM product is needed to detect the irrigation signal (Dari et al., 2020; Foster et al., 2020; Jalilvand et al., 2019; Massari et al., 2021; Modanesi et al., 2022; Zaussinger et al., 2019), while, increasing the spatial resolution corresponds with narrower sensor swath width and less frequent retrievals (Das et al., 2019). Dari et al.…”

“…(2018) assimilated SMAP SM observations (using a 1‐D Kalman filter) to constrain the target SM in the Community Land Model (CLM) irrigation module and significantly reduced the irrigation water requirement error over an intensely irrigated area in the U.S. Modanesi et al. (2022) used an Ensemble Kalman Filter (EnKF) to directly assimilate Sentinel 1 backscatter in co‐ and cross‐polarization into the Noah‐MP LSM with an irrigation scheme. They showed that assimilating Sentinel 1 backscatter in the VH polarization (that contains LAI information) can slightly improve irrigation simulations.…”