Prospects for the use of pseudo-color image processing in analysis of long-term time series of satellite data in the task of assessing vegetation cover state; [Перспективы использования псевдоцветных композитов при анализе многолетних временных рядов спутниковых данных в задаче оценки состояния растительного покрова]

Satellite images and the NDVI (Normalized Difference Vegetation Index) are often used to monitor the state of vegetation cover and a significant amount of information has now been accumulated. When processing long‑term time series of satellite data, mathematical difficulties arise, for example, in data clustering. The solution to the problem can be a parameterization of the satellite monitoring informa‑ tion using characteristic moments. For each pixel position, hundreds of NDVI values from satellite data can be reduced to several characteristic functional parameters, in particular to the extreme NDVI and the average long‑term NDVI value, as well as other summing characteristics. This opens the way for the construction of pseudo‑color images and subsequent clustering by any standard algorithm. This research examined Southern Kazakhstan, with a total area of more than 700 thousand km2. Using Google Earth Engine, time series of NDVI from Sentinel‑2 scenes (resolution 10 m) for the period April–October 2018–2022 (about 160 covers) served as the basis for describing the state of vegeta‑ tion cover. An additional parameter was the long‑term maximum of the VSSI (Vegetation Soil Salinity Index). The RGB channels of the pseudo‑color image were based on Sentinel‑2 monitoring data from April–October 2017–2022 and included: Red — a long‑term maximum of VSSI; Green — a long‑ term maximum of NDVI; Blue — a long‑term average of NDVI. The resulting pseudo‑color image displayed in detail the state of vegetation, with a clear separation of agricultural vegetation from natu‑ ral, with a ranking of irrigated arable land according to the features of growth and development of agri‑ cultural crops. This information can serve as a basis for segmentation of preprocessed satellite data for analyzing the vegetation state in the South Kazakhstan in various applied tasks. As an example, using unsupervised ISODATA classification, salinity of irrigated arable land of Kyzylkum Rural District of Zhetysai District of Turkestan Oblast was estimated. The results demonstrated the prospects of such an analysis method and clarified the known results obtained earlier using MODIS satellite data. © 2023 Space Research Institute of the Russian Academy of Sciences. All rights reserved.