Методологія інтеграції даних дистанційного зондування землі у регіональні ґрунтові інформаційні системи для аналізу морфогенетичних властивостей ґрунтів та оптимізації землекористування

Abstract

У статті досліджено методологічні засади поєднання технологій дистанційного зондування Землі з архітектурою регіональних ґрунтових інформаційних систем. Проаналізовано роль космічної зйомки як провідного джерела актуальних даних для верифікації та оновлення цифрових ґрунтових моделей. Розглянуто процеси формалізації ґрунтової інформації на фізичному, логічному та семантичному рівнях. Детально висвітлено використання спектральних індексів та об’єктно-орієнтованого аналізу для моніторингу антропогенної трансформації ґрунтів. Обґрунтовано необхідність створення цілісної інфраструктури ґрунтових даних для забезпечення сталого землекористування.

The research addresses the fundamental scientific and practical problem of the qualitative discrepancy between outdated soil mapping materials and the contemporary needs of sustainable land management, precision agriculture, and environmental monitoring in Ukraine. In the context of global climate change and intensifying anthropogenic pressure, traditional soil survey methods, primarily based on periodic field descriptions and manual interpolation, no longer provide the necessary operational efficiency or spatial accuracy. The article presents a comprehensive methodology for the seamless integration of remote sensing data into the functional and logical architecture of regional soil information systems (SIS). The theoretical foundation of the study is rooted in the concept of informational soil science, where soil is viewed as a complex, open, and multi-level natural system that acts as a global accumulator and translator of environmental and anthropogenic information. The author proposes a systemic approach to the formalization of soil data across three hierarchical levels: physical, logical, and semantic. At the physical level of formalization, the research focuses on the primary processing of multispectral satellite imagery (specifically from the Sentinel-2 and Landsat-8/9 constellations). The core challenge at this stage is the transformation of raw digital numbers into physically correct surface reflectance coefficients. The study details the application of advanced atmospheric and radiometric correction algorithms, such as Sen2Cor and the 6S model, which minimize aerosol interference and water vapor distortion. This process is essential for establishing stable spectral signatures for various soil types, allowing for quantitative comparison in multi-temporal monitoring series. The logical level of formalization involves the transformation of processed remote sensing data into thematic spectral indices that describe specific soil properties. The research highlights the significance of the Soil Brightness Index as a reliable predictor for soil organic matter content and erosion degradation. For areas with sparse or emerging vegetation, the study emphasizes the necessity of the Soil Adjusted Vegetation Index, which incorporates a soil-background correction factor. Within the SIS architecture, these indices function as independent variables in complex predictive models. The methodology demonstrates a synergistic effect when multispectral indices are combined with high-resolution digital elevation models. By calculating morphometric parameters such as slope, aspect, profile curvature, and the Topographic Wetness Index, the SIS can account for the catenary variability of soils, significantly improving the accuracy of soil boundary delineation by up to 35-40% compared to traditional manual methods. At the semantic level, the research formalizes the translation of genetic soil science categories into formal digital codes and classifiers compatible with relational database management systems. To ensure international compatibility, the study adopts the World Reference Base for Soil Resources (WRB) nomenclature. The methodology utilizes Object Based Image Analysis to segment satellite imagery into logical landscape units, so called elementary soil areas. This approach minimizes the subjectivity of the cartographer and allows for the automated identification of soil units based on their shape, texture, and spatial context. A significant portion of the material is dedicated to the practical application of remote sensing data in the diagnosis of specific soil properties, such as humus content, acidity (pH), and the state of peatlands. Based on empirical Фізична географія ISSN 2519-4577 (online) Наукові записки. №1. 2026 research conducted in the Lviv region (specifically the Busk district), the author establishes a stable inverse correlation between soil organic matter and spectral brightness: higher humus concentrations result in lower integral reflectance. Furthermore, the study explores the possibility of indirect soil pH diagnosis through indicator vegetation states and micro-relief features detectable in multi-spectral Sentinel-2 bands. The research also details the architectural implementation of a regional SIS using open-source GIS platforms (specifically QGIS). The database is structured on the principle of hierarchical integrity: "soil point – profile – genetic horizon – analytical attribute." A key innovation presented is the integration of an "historical layer", such as digitized legacy soil surveys from the mid-20th century. This allows for a robust retrospective analysis, enabling the system to identify long-term trends in soil de-humification, erosion, and anthropogenic transformation over the last four decades using the Landsat satellite archive. The application of machine learning algorithms, such as Random Forest and Support Vector Machines, within a cloud computing environment (Google Earth Engine) is analyzed as a pathway toward SIS "third-generation" systems. These network-based systems provide real-time processing of Big Data, transforming soil science into a predictive discipline capable of modeling soil evolution scenarios under various climate and farming scenarios. The economic and practical implications of the developed SIS are substantial. Automated soil quality assessment (bonitation) and land valuation based on high-precision digital models reduce administrative costs and ensure transparency in land relations. For the agricultural sector, the integrated SIS structure serves as the foundational infrastructure for precision agriculture, enabling differentiated fertilizer application based on the actual spectral heterogeneity of soil units. In conclusion, the integration of remote sensing data into SIS structures represents the primary methodology for the digital transformation of modern soil science. The study demonstrates that the symbiosis of remote sensing and GIS creates a reliable foundation for a national soil data infrastructure, which is critical for Ukraine's post-war agricultural recovery, environmental security, and sustainable land capital management in the 21st century.