Studying the growth rate of plants according to the values of the vegetation index

The article discusses a method for assessing the growth rate of plants, as a variant of understanding the ripening time and understanding the harvesting time of an agricultural crop. In most well-known software products, as a rule, they operate with averaged data on the field area, which is a very rough approximation, since fields can have different topography, water supply, soil types, etc., which affects the growth rate of plants. This article presents an approach to assessing the growth rate of an agricultural crop based on the dynamics of the NDVI vegetation index for each pixel of a satellite image. Multispectral images of the MSI Sentinel-2 device are taken as satellite data. The results of the assessment of the vegetation index and growth rate of agricultural crops, as well as the values of temperature, humidity and total precipitation for four months (May, June, July and August) are shown. The proposed results can be used in various farms to estimate the time of ripening and, accordingly, the harvesting of crops. Keywords: Sentinel-2 multispectral instrument, NDVI vegetation index, winter wheat, growth rate estimation

Event-driven simulation of the state institution activity for the service provision based on business processes

The paper presents an approach, based on business processes, assessment and control of the state of the state institution, the social insurance Fund. The paper describes the application of business processes, such as items with clear measurable parameters that need to be determined, controlled and changed for management. The example of one of the business processes of the state institutions, which shows the ability to solve management tasks, is given. The authors of the paper demonstrate the possibility of applying the mathematical apparatus of imitative simulation for solving management tasks

Studying the growth rate of plants according to the values of the vegetation index

The article discusses a method for assessing the growth rate of plants, as a variant of understanding the ripening time and understanding the harvesting time of an agricultural crop. In most well-known software products, as a rule, they operate with averaged data on the field area, which is a very rough approximation, since fields can have different topography, water supply, soil types, etc., which affects the growth rate of plants. This article presents an approach to assessing the growth rate of an agricultural crop based on the dynamics of the NDVI vegetation index for each pixel of a satellite image. Multispectral images of the MSI Sentinel-2 device are taken as satellite data. The results of the assessment of the vegetation index and growth rate of agricultural crops, as well as the values of temperature, humidity and total precipitation for four months (May, June, July and August) are shown. The proposed results can be used in various farms to estimate the time of ripening and, accordingly, the harvesting of crops. Keywords: Sentinel-2 multispectral instrument, NDVI vegetation index, winter wheat, growth rate estimation

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O2 A-band at 760 nm and the CO2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands