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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 4, pp. 253-266

Automated classification of terroir surface based on satellite spectral patterns and soil sample databases

V.A. Orlov 1 , A.A. Lukyanov 1 
1 Anapa Zonal Experimental Station for Viticulture and Winemaking — Branch of North Caucasian Federal Scientific Center for Horticulture, Viticulture, and Winemaking, Anapa, Russia
Accepted: 24.06.2025
DOI: 10.21046/2070-7401-2025-22-4-253-266
Soil characteristics and climatic conditions largely determine the productivity and organoleptic qualities of grapes within a terroir. Terroirs play a pivotal role in agriculture, especially viticulture; however, their typification remains a challenging task. Traditional soil investigation methods — based on laboratory analysis — are labor-intensive, particularly when surveying extensive areas. This study employs an automated method for classifying terroir soil attributes through the analysis of normalized spectral index patterns at soil sampling sites. Vegetation and soil indices were calculated using multispectral satellite data from Sentinel-2 combined with agrochemical test results. Data processing was conducted using a Random Forest (RF) algorithm achieving classification accuracy levels of 85–90 %. Spectral indices indicative of organic matter, clay, sand, stone, salinity, moisture, and soil texture were computed from Sentinel-2 bands. Soil heterogeneity mapping was performed using machine learning algorithms implemented in the cloud-based Google Earth Engine (GEE). The paper presents successful applications of multispectral satellite data and GIS technologies for soil analysis in various viticultural regions, including Italy, California, Chile, Argentina, and Krasnodar Krai of Russia. The results enable the identification of soil zones with different physicochemical properties that significantly affect grape yield and quality. The proposed approach opens new possibilities for automated terroir monitoring, reducing field research costs and providing viticulturists with tools to optimize agronomic practices. The study confirms the high effectiveness of using threshold-based spectral indices combined with RF algorithms for digital classification of soil surfaces in agronomic applications.
Keywords: terroir, spectral patterns, satellite spectrogram, remote sensing, soil samples, machine learning, classification
Full text

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