Identification of degradation risk zones in an irrigated field using remote sensing methods

DOI: 10.34736/FNC.2026.133.2.006.55-61   ___ ___ ___

Kalinichenko Roman Vladimirovich, Candidate of Agricultural Sciences, Senior Researcher, Department of Soil Physics, Hydrology and Erosion, Federal State Budgetary Scientific Institution "Federal Research Center V. V. Dokuchaev Soil Science Institute", 7, bldg. 2, Pyzhevsky Lane, 119017, Moscow, Russia. ORCID: 0000-0003-3136-8468.

e-mail: kalinichenko_rv@esoil.ru

Romanovskaya Anna Yurievna, Candidate of Agricultural Sciences, Researcher, Department of Soil Genesis, Geography, Classification and Digital Mapping, Federal State Budgetary Scientific Institution "Federal Research Center V. V. Dokuchaev Soil Science Institute", 7, bldg. 2, Pyzhevsky Lane, 119017, Moscow, Russia. ORCID: 0000-0001-9434-3095.

e-mail: burmistrovaann13@mail.ru

Bedenko Alexey Evgenievich, Junior Researcher, Department of Geoinformation Technologies and Monitoring of Reclamation Systems, Federal State Budgetary Scientific Institution "All-Russian Scientific Research Institute for Irrigation and Farming Water Supply Systems "Raduga", Raduzhny settlement, Kolomna City District, Moscow Region, 140483, Russia.ORCID: 0009-0005-7522-0395.

e-mail: timbothdoom@gmail.com

Abstract Intensive agriculture in arid and semi-arid regions of Southern Russia is closely linked to irrigation and land reclamation systems. However, while irrigation is a powerful tool for increasing yields, it also acts as a catalyst for specific degradation processes, such as secondary salinization, waterlogging, and loss of soil structure. This paper presents a novel methodology for identifying degradation risk zones in irrigated areas of the Rostov region using time-series satellite data from Landsat 8/9. The proposed algorithm is based on a three-stage spectral filtering system designed to distinguish random noise from indicators of sustained soil stress. The methodology involves sequential analysis of three parameters: mean seasonal NDVI values to assess biomass accumulation, NDVI standard deviation to identify areas of persistent growth stagnation, and springtime soil moisture estimated using NDMI, reflecting soil heterogeneity prior to canopy closure. Statistical processing and geospatial analysis using QGIS enabled segmentation of the 50.1-hectare study area into zones with varying levels of stress. The results show that 81.8 % of the area remains stable, while 10 % (5.0 ha) falls within a high degradation risk zone. The proposed approach enables a transition from labor-intensive continuous monitoring to precision planning. This allows agronomists and land reclamation specialists to optimize operational costs by focusing field surveys and reclamation measures on problem areas, thereby supporting soil fertility conservation under changing climate conditions.

Keywords .. remote sensing, irrigated agriculture, NDVI, Landsat imagery, agricultural landscape monitoring.

For citation. Kalinichenko R. V., Romanovskaya A. Y., Bedenko A. E. Identification of degradation risk zones in an irrigated field using remote sensing methods // Scientific Agronomy Journal. 2026; 2(133):55-61. 10.34736/FNC.2026.133.2.006.55-61.

Funding. The work was carried out without external financing.

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