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 (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.

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

“…By shifting from parameterized to physically resolved modeling (e.g., static parameterized to prognostic dynamic vegetation) and by coupling more processes, the DA impact of a single observation can reach more unobserved, but model-resolved, compartments. For example, snow depth DA can improve discharge and low-level atmospheric estimates (Griessinger et al, 2019;Rudisill et al, 2021;Lahmers et al, 2022), and backscatter DA can update dynamic vegetation and soil moisture, to eventually update irrigation (Modanesi et al, 2022). Efforts are ongoing to advance land DA in coupled land-atmosphere models (de Rosnay et al, 2014;Boussetta et al, 2015;Carrera et al, 2019;Reichle et al, 2021b) to make good on the promise to improve NWP and subseasonal to seasonal predictions (Kumar et al, 2022).…”

Modeling actual irrigation water use regimes at district scale: application to the FAO-56 dual crop coefficient method

“…However, the same irrigation module is also used in other regions where irrigation practices are different. For example, Modanesi et al (2022) recently implemented this module in Germany and Italy, with SM threshold empirically set to 45% in Italy to avoid large overestimations in irrigation amounts using the default threshold value of 50%. The latter calibration strategy was evaluated by comparing the ~ 1 km resolution simulations with the observations available in several small (0.4 ha) elds and two (290 and 760 ha) irrigation districts.…”

Is it Possible to Quantify Irrigation Water-Use by Assimilating a High-Resolution 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 (<1km). 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 (Jalilvand et al, 2019;Zaussinger et al, 2019;Dari et al, 2020;Foster et al, 2020;Massari et al, 2021;Modanesi et al, 2022), while, increasing the spatial resolution corresponds with narrower sensor swath width and less frequent retrievals (Das et al, 2019). Dari et al (2020 and resolved the low temporal resolution issue by downscaling the coarse spatial but high temporal resolution data (e.g., SMAP Enhanced 9km product) using methods such as DiSPATCh.…”

“…For example, Felfelani 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 US. Modanesi et al, (2022) used an Ensemble Kalman Filter (EnKF) DA 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.…”