Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland) DOI Creative Commons
А. В. Чинилин, Nikolai Lozbenev, P. M. Shilov

и другие.

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

Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland) DOI Creative Commons
А. В. Чинилин, Nikolai Lozbenev, P. M. Shilov

и другие.

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

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