Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

Irrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel-1 backscatter observations into the Noah-MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model-only 14-day irrigation estimates by about 20% in terms of root-mean-squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real-world cases. Irrigation has an important impact on the terrestrial water cycle. However, it remains poorly simulated by models and it is hard to quantify through satellite observations alone. The combination of models and satellite observations to detect and quantify irrigation has been explored in the last few years. Recently, Sentinel-1 radar (microwave) observations have been assimilated into the Noah-MP land surface model in order to quantify irrigation, and better estimate the related land surface variables, such as soil moisture and vegetation. This system has shown benefits but also limitations, which are highlighted and addressed in our study using synthetic experiments. We propose an improved data assimilation algorithm and test it for different sites and levels of model and observation error. For temperate regions, the new method estimates irrigation more accurately (20%) than the model alone, provided that frequent (daily or every other day) observations are available. With further developments, the new methodology could be used in a real-world experiment. Model-driven irrigation estimation has limitations, also when based on improved soil moisture conditions obtained via data assimilation A new method based on an adaptive outlier detection improves the estimated irrigation in a synthetic backscatter data assimilation setup The method performs best with frequent data assimilation in a temperate climate, reducing irrigation errors by 20%