Inhomogeneous Anisotropic Analysis of the Available Water Content of the Upper Soil Layer According to Ground-Based and Remote Sensing on the Territory of Russia

Inhomogeneous Anisotropic Analysis of the Available Water Content of the Upper Soil Layer According to Ground-Based and Remote Sensing on the Territory of Russia

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 ava...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 9
Main Authors: Bykov, Philipp L., Gordin, Vladimir A., Tarasova, Lydia L., Vasilenko, Evgenii V.
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Atmospheric research
Back propagation
Back propagation networks
Climatic data
Correlation analysis
Data assimilation
Extraterrestrial measurements
Fields
Hydrometeorology
Interpolation
Machine learning
Moisture
Moisture content
Moisture effects
Moisture measurement
Neural networks
Remote sensing
Satellites
Scatterometers
Soil
Soil layers
Soil measurements
Soil moisture
Soil types
Statistical analysis
Statistical methods
Water content
Russia
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3202609