The Hydrometeorological Center of Russia receives agrometeorological information from about 950 stations one time per ten days and the remote sensing Advanced Scatterometer (ASCAT) data from three Meteorological Operational (MetOp) satellites. We suggest a combined objective analysis (OA) of the available water content based on the available water content measurements at agrometeorological stations and on remote sensing data. The new version of OA is constructed using two neural networks and the backpropagation of error to learn it simultaneously. The first neural network is used to convert the ASCAT data into the available water content values, and the second network is used to estimate the inhomogeneities of soil moisture fields. We use the optimal interpolation (OI) method for assimilation of the ground-based data. In the new version, we evaluate the correlation functions (CFs) of inhomogeneous non-Gaussian fields, not from sample statistics but from machine learning methods. The method takes into account the combining of various datasets: ASCAT data, Food and Agriculture Organization (FAO) soil types, European Space Agency (ESA) GlobCover, and National Center for Atmospheric Research (NCAR) climate data.

中文翻译：

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根据俄罗斯境内地基和遥感对上层土壤有效含水量的非均匀各向异性分析


俄罗斯水文气象中心每十天一次接收来自约950个站点的农业气象信息以及来自三颗气象业务（MetOp）卫星的遥感高级散射仪（ASCAT）数据。我们建议根据农业气象站的可用含水量测量值和遥感数据对可用含水量进行组合客观分析（OA）。新版本的 OA 是使用两个神经网络和误差反向传播来同时学习的。第一个神经网络用于将 ASCAT 数据转换为可用的含水量值，第二个网络用于估计土壤湿度场的不均匀性。我们使用最优插值（OI）方法来同化地面数据。在新版本中，我们不是通过样本统计数据而是通过机器学习方法来评估非齐次非高斯场的相关函数（CF）。该方法考虑了各种数据集的组合：ASCAT 数据、粮食及农业组织 (FAO) 土壤类型、欧洲航天局 (ESA) GlobCover 和国家大气研究中心 (NCAR) 气候数据。