Sensitivity of the spectral brightness coefficient to variation of the components of seawater

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.

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Analysis of Indicatrices of the Spectral Brightness Coefficient for Surfaces with Different Microgeometry

Izvestiâ vysših učebnyh zavedenij Priborostroenie ◽

10.17586/0021-3454-2015-58-4-312-314 ◽

2015 ◽

pp. 312-314

Author(s):

I.Yu. Shmigelsky

Keyword(s):

Spectral Brightness ◽

Brightness Coefficient ◽

Spectral Brightness Coefficient

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Spectral brightness coefficient of natural ground surfaces in spectral ranges 0.35 to 0.85 μm based on airborne measurements

10.1117/12.284763 ◽

1997 ◽

Author(s):

Alexander V. Vasilyev ◽

Irina N. Melnikova ◽

Ludmila N. Poberovskaya ◽

Igor A. Tovstenko

Keyword(s):

Spectral Brightness ◽

Brightness Coefficient ◽

Airborne Measurements ◽

Spectral Brightness Coefficient ◽

Spectral Ranges

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

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2021.249278 ◽

2021 ◽

Vol 6 (2 (114)) ◽

pp. 96-102

Author(s):

Akbota Yerzhanova ◽

Akmaral Kassymova ◽

Gulzira Abdikerimova ◽

Manshuk Abdimomynova ◽

Zhuldyz Tashenova ◽

...

Keyword(s):

Plant Species ◽

Satellite Images ◽

Action Plan ◽

Main Tool ◽

Normal Growth ◽

Spectral Brightness ◽

Space Images ◽

Brightness Coefficient ◽

Spectral Brightness Coefficient ◽

Spectral Ranges

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

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

Bulletin of Kazakh Leading Academy of Architecture and Construction ◽

10.51488/1680-080x/2021.1-41 ◽

2021 ◽

Vol 79 (1) ◽

pp. 315-324

Author(s):

A.E. Yerzhanova ◽

◽

S.E. Kerimkulov ◽

Keyword(s):

Data Analysis ◽

Wavelength Range ◽

Spectral Properties ◽

Satellite Images ◽

Recognition Algorithm ◽

Spectral Brightness ◽

Agricultural Crops ◽

Brightness Coefficient ◽

Spectral Brightness Coefficient ◽

Different Types

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.

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Designing a methodological concept for the diagnosis of early development of the main wheat diseases pathogens

BIO Web of Conferences ◽

10.1051/bioconf/20202100002 ◽

2020 ◽

Vol 21 ◽

pp. 00002

Author(s):

Oksana Kremneva ◽

Roman Danilov ◽

Olga Tutubalina ◽

Igor Sereda ◽

Kurilov Artem

Keyword(s):

Early Development ◽

Near Infrared ◽

Puccinia Striiformis ◽

Plant Protection ◽

Spectral Characteristics ◽

Infrared Range ◽

Spectral Brightness ◽

Wheat Varieties ◽

Brightness Coefficient ◽

Experimental Plots

The studies presented in the article were carried out in 2018-2019 on the experimental field of the All-Russian Research Institute of Biological Plant Protection. The aim of the research was to assess the feasibility of diagnosing the early development of major diseases pathogens based on the results of ground-based spectrometry and the use of phytomonitoring technology, taking into account the genotypes of different winter wheat varieties. There were three options of the experimental plots for the research: the 1st – protected against diseases by fungicides, the 2nd – with an artificial infectious background, the 3rd – with the natural development of diseases. According to the results of data analysis, the most significant changes in the spectral characteristics of the studied plant backgrounds were noted at the time of the first signs of disease in the form of a decrease in the spectral brightness coefficient in the near infrared range. Using special tools in the experimental plots, the following pathogens were identified before the appearing of disease symptoms: Blumeria graminis (DC.) Speer f. sp. tritici Marchal , Puccinia striiformis West., Pyrenophora tritici-repentis Died., Puccinia triticina Erikss. Data on the diseases development, plant infestation by pathogens are compared with spectrometric measurements.

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Landscape optical properties of land within iron ore deposits based on satellite data

Ukrainian journal of remote sensing ◽

10.36023/ujrs.2019.23.160 ◽

2019 ◽

pp. 14-20

Author(s):

Tetiana Yefimenko ◽

Zinayda Tovstyuk

Keyword(s):

Magnetic Field ◽

Factor Analysis ◽

High Magnetic Field ◽

Iron Ore ◽

Ore Deposits ◽

Optical Characteristics ◽

Landsat 8 ◽

Spectral Brightness ◽

Brightness Coefficient ◽

Iron Ore Deposits

In the area of the Krivoy Rog-Kremenchug suture zone, the optical characteristics of landscape elements were studied, both at iron ore deposits and above predicted morphostructures on iron ore deposits. Within the predicted morphostructures with a high magnetic field (iron ore rocks of the Krivoy Rog series), measurements were made of the spectral brightness coefficient (CMF) of vegetation cover. The Landsat-8 satellite image was used to perform spectrometry analysis using factor analysis (factor analysis 2 was most indicative) and elevated values of the optical characteristics of vegetation within the morphostructures above ferruginous rocks were revealed. This made it possible to predict deposits of iron ores within the predicted morphostructures with a high magnetic field intensity.

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