Satellite Data Assimilation for Land Surface Modelling

Dear Colleagues,

The recent advancements in remote sensing techniques have gained a lot of attention as it becomes more important to improve our understanding of the Earth’s system. Traditionally, various types of models, such as hydrologic and atmospheric models, have been used for modelling/simulating and predicting hydro‐meteorological processes at regional and global scales. Nevertheless, due to various sources of uncertainties such as imperfect modelling, data limitations on both temporal and spatial resolutions, their errors, as well as limited knowledge about empirical model parameters, the accuracy of model simulations can be degraded.

Assimilation of satellite data has been shown to be effective for improving the models’ performance and their forecasting skills. This allows us to better study, for example, water resources and their distribution, mass variations and balance, extreme events such as droughts and floods, and ice transfer, and also helps us adapt to long-term environmental challenges posed by climate changes on a continental scale. Data assimilation (DA) facilitates this data integration by constraining the models’ simulations based on observations and errors associated with them. The method has become more popular with the advent of the space era, since scientific observational methods are no longer limited to terrestrial only, and offer high spatiotemporal resolution data with global coverage.

Therefore, satellite DA has great potential in hydro-climate studies. The main goal of this Special Issue is to publish both innovative and practical solutions to the complex DA problems when integrating satellite data products with land surface models. High-quality and original submissions are also encouraged to discuss the current satellite DA challenges in this issue.

Dr. Mehdi Khaki
Guest Editor

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