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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 1, pp. 31-41

Development of a new algorithm for the retrieval of total precipitable water of the atmosphere over land from the data of satellite radiothermal monitoring

D.M. Ermakov 1, 2 , V.D. Polyakov 3 , E.V. Polyakova 4 
1 V. A. Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Russia
2 Space Research Institute RAS, Moscow, Russia
3 Gymnasium No. 25, Arkhangelsk, Russia
4 N. P. Laverov Federal Center for Integrated Arctic Research RAS, Arkhangelsk, Russia
Accepted: 11.10.2019
DOI: 10.21046/2070-7401-2020-17-1-31-41
A neural network model for retrieving the total precipitable water of the atmosphere over land based on measurements of satellite radiometers SSM/I (SSMIS) with the involvement of additional information is proposed. An obstacle to the implementation of more traditional approaches developed for processing data from AMSR-E (AMSR2) radiometers is the impossibility of calculating the polarization contrast on the slope of the water vapor absorption line at about 22.4 GHz. The composition of additional input data is analyzed, which, together with SSM/I (SSMIS) measurements, provides the minimum necessary information to solve the problem of retrieving the total precipitable water. One of the key requirements is the availability of these additional data with space-time detail and in the amounts corresponding to the arrays of archive and operational information of radiothermal observations. To demonstrate the fundamental feasibility of the solution, a neural network test model was built and trained on a limited data sample. Analysis of the preliminary results showed the prospects of further development of the proposed approach. It is noted that the approach can be extended to process data from other satellite radiometers, in particular, the domestic MTVZA-GYa device.
Keywords: total precipitable water, satellite radiometry, artificial neural network
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