Land, Год журнала: 2024, Номер 13(12), С. 2229 - 2229

Опубликована: Дек. 20, 2024

This study presents an approach for predicting soil class probabilities by integrating synthetic composite imagery of bare with long-term vegetation remote sensing data and survey data. The goal is to develop detailed maps the agro-innovation center “Orlovka-AIC” (Samara Region), a focus on lithological heterogeneity. Satellite were sourced from cloud-filtered collection Landsat 4–5 7 images (April–May, 1988–2010) 8–9 (June–August, 2012–2023). Bare surfaces identified using threshold values NDVI (<0.06), NBR2 (<0.05), BSI (>0.10). Synthetic generated calculating median reflectance across available spectral bands. Following adoption no-till technology in 2012, average additionally calculated assess condition agricultural lands. Seventy-one sampling points within classified both Russian WRB classification systems. Logistic regression was applied pixel-based prediction. model achieved overall accuracy 0.85 Cohen’s Kappa coefficient 0.67, demonstrating its reliability distinguishing two main classes: agrochernozems agrozems. resulting map provides robust foundation sustainable land management practices, including erosion prevention use optimization.

Язык: Английский