Introduction

Multispectral remote sensing is a method of acquiring information about the Earth's surface based on recording reflected light across several spectral bands. The key difference between multispectral data and conventional digital imaging is that multispectral data enable the analysis of not only the visual characteristics of objects but also their specific spectral reflectance. This makes the method highly valuable for studying vegetation cover conditions, as plants exhibit varying reflectance intensities across different spectral bands.

The application of multispectral technologies, particularly in agriculture, provides information on the spatial distribution of crop spectral features, which is used to assess and identify crop heterogeneity. The resulting data can be applied for a detailed assessment of overall vegetation health, identifying field areas with varying characteristics and crop development dynamics, which can then be utilised for the site-specific management of agricultural lands. In this context, primary focus is placed on the use of unmanned aerial vehicles (UAVs) equipped with integrated multispectral cameras designed to acquire high-resolution spatial data from the surveyed fields.

However, acquiring multispectral imagery is only the initial stage of working with spectral data. To obtain information in an analysis-ready format, raw satellite or UAV images undergo multiple stages of preprocessing, image stitching, processing, and conversion into specialised cartographic materials for subsequent interpretation and practical implementation. A key role in this process belongs to specialised software suites designed to process agricultural aerial photographs, generate orthomosaics and surface models, delineate field boundaries by clipping raw stitched multispectral data, manipulate spectral bands, and calculate vegetation indices with subsequent map visualisation. Depending on the settings of the camera used, it is possible to calculate standard, widely accepted indices as well as apply custom index variants using a vegetation index calculator.

This study guide presents the theoretical foundations of multispectral remote sensing and examines the integration of multispectral data with geoinformation technologies, the specific features of vegetation spectral reflectance, the operating principles of sensor systems, and the technological aspects of image acquisition using unmanned aerial vehicles. In modern contexts, an objective assessment of crop health heavily depends on the quality of raw data, the accuracy of their processing, and the performance of the applied analytical algorithms, which necessitates the training of specialists to use relevant digital tools effectively.

The use of spatially referenced information enables the comparison of results from different acquisition periods, spatial analysis, the identification of dynamic changes, and the generation of digital maps that can be used to support management decisions in agricultural production.

The study guide combines elements of remote sensing, digital image processing, geographic information systems, photogrammetry, unmanned technologies, and agricultural monitoring. Such a combination fosters a comprehensive understanding of modern digital technologies and their practical application in solving applied tasks.

This study guide was written as part of the project “Development of an integrated system for remote monitoring of spring wheat agrocenoses based on spectral imaging technology for the creation of precision agriculture” (Grant No. AP22784689), funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan.