ИСТИНА

The Lower Volga arid wetlands maintain the high-productive ecosystems amongst the surrounding desert and dry steppe being the only source of fresh water for drinking, fishery, irrigation. It plays an important role in the local economy. Due to the vastness of the study area, which covers over 9000 (Volga-Akhtuba floodplain) and 20000 square kilometers (Volga delta), continuous in-situ observations on the numerous water bodies are challenging. The water body dynamics in arid wetlands being an important indicator of wetland ecosystem state, can be estimated by the satellite multispectral image time series analysis. Since that, satellite data combined with meteorological, hydrological, and other in-situ data is the only source of information for balanced and broad view on the wetland hydrographic network dynamics to develop the proposals for proper water resource management. The key attributes of water bodies in arid climatic zones are its high rates and ranges of temporal and spatial variability, and its considerable heterogeneity in optical features caused by salinization, plant overgrowth, algal blooms, pollution, sediment load, etc., which arise from the drastic seasonal and long-term environmental variability. Many of conventional methods based on optical satellite data analysis for such water bodies were often found to produce artifacts or unreliable results. Thus, the methods for satellite imagery processing need to be specifically adjusted to water bodies of arid wetlands, in order to reduce a shoreline, and an area estimation errors. This study represents the methodological approaches to arid wetland water body mapping in Lower Volga, and estimation of water body dynamics in 1984-2017, based on the Landsat 4-8, and the Sentinel-2 imagery. The study includes the comparison of different kinds of image classification, tasseled cap transformations (wetness component), and the indices based on the selected spectral bands. The application results of conventional methods, such as supervised, and unsupervised image classification, and MNDWI index, together with the advanced approaches, such as Tasseled cap wetness component derived by the locally adjusted Jackson's algorithm and DFI index, were tested on different types of water bodies located in arid Lower Volga wetlands. The combined application of water indices with vegetation indices was also found to be productive. Our approaches showed a great potential in water body mapping and analysis of the wetland hydrographic network dynamics. The methods undergo the continuous development. Some new results on their applications are to be reported.