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Retrieval of Volcanic Sulfate Aerosols Optical Parameters from AHI Radiometer Data

Advances in Atmospheric Sciences 2024年第10期41卷 1953-1968页

作者： Andrei FILEI Olga GIRINA aleksei sorokin Far-Eastern Center of State Research Center for Space Hydrometeorology 《Planeta》 Institute of Volcanology and Seismology Far Eastern Branch of the Russian Academy of Sciences Computing Center of the Far Eastern Branch of the Russian Academy of Sciences

This paper presents a method for retrieving optical parameters from volcanic sulfate aerosols from the AHI radiometer on board the Himawari-8 satellite. The proposed method is based on optical models for various mixtures of aerosol components from volcanic clouds, including ash particles, ice crystals, water drops, and sulfate aerosol droplets. The application of multi-component optical models of various aerosol compositions allows for the optical thickness and mass loading of sulfate aerosol to be estimated in the sulfuric cloud formed after the Karymsky volcano eruption on 3 November 2021. A comprehensive analysis of the brightness temperatures of the sulfuric cloud in the infrared bands was performed,which revealed that the cloud was composed of a mixture of sulfate aerosol and water droplets. Using models of various aerosol compositions allows for the satellite-based estimation of optical parameters not only for sulfate aerosol but also for the whole aerosol mixture.

关键词： H2SO4 Himawari-8 mass loading optical thickness the Karymsky volcano volcanic ash volcano

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Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield

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The Crop Journal 2022年第5期10卷 1452-1459页

作者： Alexey Stepanov Konstantin Dubrovin aleksei sorokin Far Eastern Agriculture Research Institute Vostochnoe680521 KhabarovskRussia Computing Center of the Far Eastern Branch of the Russian Academy of Sciences 680000 KhabarovskRussia

Forecasting crop yields based on remote sensing data is one of the most important tasks in *** is the main crop in the Russian Far *** is desirable to forecast soybean yield as early as possible while maintaining high *** study aimed to investigate seasonal time series of the normalized difference vegetation index(NDVI) to achieve early forecasting of soybean *** research used data from the Moderate Resolution Image Spectroradiometer(MODIS),an arable-land mask obtained from the VEGA-Science web service,and soybean yield data for 2008-2017 for the Jewish Autonomous Region(JAR) *** approximating functions were fitted to model the NDVI time series:Gaussian,double logistic(DL),and quadratic and cubic *** the period from calendar weeks 22-42(end of May to mid-October),averaged over two districts,the model using the DL function showed the highest accuracy(mean absolute percentage error-4.0%,root mean square error(RMSE)-0.029,P <0.01).The yield forecast accuracy of prediction in the period of weeks 25-30 in JAR municipalities using the parameters of the Gaussian function was higher(P <0.05) than that using the other *** mean forecast error for the Gaussian function was 14.9% in week 25(RMSE was0.21 t ha) and 5.1%-12.9% in weeks 26-30(RMSE varied from 0.06 to 0.15 t ha) according to the2013-2017 *** weeks 31-32,the error was 5.0%-5.4%(RMSE was 0.07 t ha) using the Gaussian parameters and 7.4%-7.7%(RMSE was 0.09-0.11 t ha) for the DL *** the method was applied to municipal districts of other soy-producing regions of the Russian Far *** was0.14-0.32 t hain weeks 25-26 and did not exceed 0.20 t hain subsequent weeks.

关键词： NDVI Function fitting Early prediction Yield Soybean

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