Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in-depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment-scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory-data land processes studies.

中文翻译：

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土地数据同化：协调陆地表面过程研究中的理论和数据

数据同化在促进“科学”理解和作为地球系统科学的“工程工具”方面发挥着双重作用。土地数据同化（LDA）已发展成为地球物理学中的一门独特学科，促进理论和数据的协调，并使土地模型和观测结果相互补充和制约。近几十年来，LDA的理论、方法和应用都取得了长足的进步，需要对其进行全面、深入的探索。在这里，我们对 LDA 的理论和方法的发展及其显着特征进行了全面的回顾。这包括在解决地表过程中的强非线性方面取得的突破，探索机器学习方法在数据同化方面的潜力，量化多尺度空间相关性产生的不确定性，以及同时估计模型状态和参数。事实证明，LDA 在增强对各种地表过程（包括土壤湿度、降雪、蒸散、水流、地下水、灌溉和地表温度）的理解和预测方面取得了成功，特别是在水和能源循环领域。本综述概述了全球、区域和流域规模的 LDA 系统和软件平台的开发，提出了土地再分析和推进陆地-大气耦合 DA 的巨大挑战。最后，我们强调了通过吸收大量地球观测和社会传感数据，将 LDA 的应用从纯粹的地球物理系统扩展到耦合的自然和人类系统的机会。本文综合了当前的 LDA 知识，并为其未来的发展提供了踏脚石，特别是在促进双重驱动的理论数据土地过程研究方面。