Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.
The paper discusses the methodology and results of electronic ice charts processing. The charts taken from AARI archive. The Barents, Kara, Laptev, East Siberian and Chukchi seas Ice maps refl ect ice conditions for the period from 1997 to 2018 for the April-May inter-annual interval. The total stage lengths of «Sabetta-the Kara Gate-Murmansk» and «Sabetta-the Vilkitski Strait-the Bering Strait» standard routes were calculated at certain conditions of ice navigation. The route "Sabetta-the Bering Strait" was divided into sections within the Kara sea, Laptev Sea, East Siberian and Chukchi Seas for analysis. The purpose of the study is to obtain the values of the length of the routes in different categories of ice and to analyze changes trend of navigation in ice conditions for the period 1997-2018. The series were checked for the presence of trends using the integral curves method. The homogeneity of the series was checked using Wilcoxon-Mann-Whitney and Siegel-Tukey rank non-parametric criteria. Most of the series proved to be non-homogeneous. The following
The paper presents a hybrid approach for short-term river flood forecasting. It is based on multi-modal data fusion from different sources (weather stations, water height sensors, remote sensing data). To improve the forecasting efficiency, the machine learning methods and the Snowmelt-Runoff physical model are combined in a composite modeling pipeline using automated machine learning techniques. The novelty of the study is based on the application of automated machine learning to identify the individual blocks of a composite pipeline without involving an expert. It makes it possible to adapt the approach to various river basins and different types of floods. Lena River basin was used as a case study since its modeling during spring high water is complicated by the high probability of ice-jam flooding events. Experimental comparison with the existing methods confirms that the proposed approach reduces the error at each analyzed level gauging station. The value of Nash–Sutcliffe model efficiency coefficient for the ten stations chosen for comparison is 0.80. The other approaches based on statistical and physical models could not surpass the threshold of 0.74. Validation for a high-water period also confirms that a composite pipeline designed using automated machine learning is much more efficient than stand-alone models.
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