Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m/m in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

中文翻译：

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通过将 SMAP 土壤湿度纳入印度土地数据同化系统 (ILDAS)，改进土壤湿度估计和灌溉信号检测


地表模型有助于估计一系列时空尺度的土壤湿度。然而，模型参数化的局限性和人为过程的代表性不足限制了它们估计局部尺度土壤湿度变化的能力，特别是在灌溉地区。将基于卫星的土壤湿度反演同化到地表模型中可能是克服这些限制的可行方法，特别是在印度等高度灌溉的国家，这种应用很少见。此外，由于缺乏代表性的站网络，迄今为止，印度各地模拟土壤湿度的大规模验证一直受到限制。通过将基于土壤湿度主动被动 (SMAP) 的估计同化到最先​​进的印度土地数据同化系统 (ILDAS) 中，并与由 200 多个站点组成的新土壤湿度站网络相结合，本研究表明土壤湿度有所改善估计和捕获该地区的灌溉信号。 Noah-MP 地表模型由多个本地和全球气象数据集驱动，并使用集成卡尔曼滤波器 (EnKF) 来同化土壤湿度。开环和同化土壤湿度数据与站土壤湿度数据的比较显示，相关性的相对空间平均改善为 0.0178，RMSE 为 0.0029 m/m。与现场数据的进一步统计比较也显示了大多数站点的更好结果，从同化后相关性的改善和无偏 RMSE 的降低就可以明显看出。 最后，不同灌溉部分的土壤湿度气候学表明，在干燥的冬季，灌溉网格单元的数据同化输出往往具有较高的土壤湿度，这证明了捕获灌溉信号的能力。这些发现量化了数据同化在改善土壤湿度估计和捕获灌溉等未建模过程的能力方面的价值，这为即将到来的太空任务（例如 NASA ISRO 合成孔径雷达（NISAR））奠定了科学基础。