Analysis of the spectral properties of wheat growth in different vegetation periods

The article presents a technique for studying space images based on the analysis of the spectral brightness coefficient (SBC) of space images of the earth's surface. Recognition of plant species, soils, and territories using satellite images is an applied task that allows to implement many processes in agriculture and automate the activities of farmers and large farms. The main tool for analyzing satellite imagery data is the clustering of data that uniquely identifies the desired objects and changes associated with various reasons. Based on the data obtained in the course of experiments on obtaining numerical SBC values, the patterns of behavior of the processes of reflection of vegetation, factors that impede the normal growth of plants, and the proposed clustering of the spectral ranges of wave propagation, which can be used to determine the type of objects under consideration, are revealed. Recognition of these causes through the analysis of SBC satellite images will create an information system for monitoring the state of plants and events to eliminate negative causes. SBC data is divided into non-overlapping ranges, i.e. they form clusters reflecting the normal development of plant species and deviations associated with negative causes. If there are deviations, then there is an algorithm that determines the cause of the deviation and proposes an action plan to eliminate the defect. It should be noted that the distribution of the brightness spectra depends on the climatic and geographical conditions of the plant species and is unique for each region. This study refers to the Akmola region, where grain crops are grown

Spectral properties of soils and agricultural crops for analysis of satellite images of the Akmola region

This paper considers the spectral properties of soils and vegetation and their analysis for further application of the results of the article for processing satellite images. Basically, the soils and soils of the Akmola region and agricultural crops inherent in this region are considered. When analyzing the spectral brightness coefficient (SCR), there are differences in the SCR of soils of different types and vegetation. Based on the results of data analysis, the following conclusions were obtained: soil recognition is informative in the wavelength range from 700 nm to 1300 nm; crop recognition is informative in the wavelength range from 850 nm to 1100 nm. When developing an object recognition algorithm, two fixed points of 0.55 microns and a point of 0.66-0.68 microns will be considered for the presence of extremes to determine the type of object.

Spectral characteristics of some agricultural crops in different phenological phases of vegetation

For deciphering crops from satellite images at different time periods, it is necessary to have information about the spectral reflectivity of plants during their passage through the phenological phases of vegetation. An attempt was made to evaluate the spectral reflectivity of the main fruit crops and grapes in different phenological phases of the growing season using Sentinel-2 satellite images and the ENVI software package. Field research methods, plots were selected on which peach, grapes, cherries, apple trees, plums, and apricots grow are used. It was established that planting crops was carried out by mixing cultivars in order to reduce the risk of additional costs as a result of possible adverse natural processes and phenomena. For each section, the maximum, minimum, and average values of the spectral brightness coefficient were obtained and analyzed within 13 bands of Sentinel-2 satellite images. Space images were selected for 04/07/2019, 04/27/2019 and 05/12/2019, as the most suitable for the periods of the beginning of flowering (04/07/2019), the end of flowering (04/27/2019) and the beginning of fruit ripening (12/05/2019), with minimal cloud overlap values. To eliminate the external influence of the soil within each pixel of the image, the linear spectral separation module of the ENVI software package was used, a reference soil fragment was selected and its spectral characteristics were obtained, which made it possible to depict graphs of the spectral curves of the crops under study within each section. It was not possible to obtain a distinction of the spectral brightness coefficient for all sections, which is associated with the presence of additional external elements.