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Estimating Soil Moisture by Radar Data Based on Multiple Regression

  • METHODS AND TOOLS FOR PROCESSING AND INTERPRETING SPACE INFORMATION
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Abstract

The problem of estimating soil moisture by remote (satellite) methods remains topical. To do this, regression models based on the correlation between radar data and ground-based measurements of soil moisture are constructed. Ground-based measurements were taken at two stations in Germany (Falkenberg and Gevenich), which are a part of the International Soil Moisture Network (ISMN). Sentinel-1 satellite data are used as radar data. Multiple regressions with a determination coefficient up to 0.91 are constructed. It is proposed to use in regressions not only radar, but also meteorological data, which makes it possible to increase the coefficient of determination and reduce the standard error of regression. For the possible spread of regressions obtained for one territory to another, two criteria are proposed: proximity of the values of the Selyaninov hydrothermal coefficient (HTC) and similarity of the soil texture. According to these conditions, two stations in Ryazan oblast and in Kalmykia were chosen. Their archival information on soil moisture is contained in the ISMN database up to 1998. Each of the selected stations satisfies only one of the chosen criteria.

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Funding

This work was carried out within the framework of the state contract for the Kotelnikov Institute of Radio Engineering and Electronics for the topic “Cosmos.”

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  1. Kotelnikov Institute of Radio Engineering and Electronics (Fryazino Branch), Russian Academy of Sciences, 141190, Fryazino, Moscow oblast, Russia

    N. V. Rodionova

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  1. N. V. Rodionova
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Correspondence to N. V. Rodionova.

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Translated by A. Nikol’skii

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Rodionova, N.V. Estimating Soil Moisture by Radar Data Based on Multiple Regression. Izv. Atmos. Ocean. Phys. 59, 1281–1289 (2023). https://doi.org/10.1134/S0001433823120186

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