Synergetic Use of Bare Soil Composite Imagery and Multitemporal Vegetation Remote Sensing for Soil Mapping (A Case Study from Samara Region’s Upland)

Andrey V. Chinilin (), Nikolay I. Lozbenev, Pavel M. Shilov, Pavel P. Fil, Ekaterina A. Levchenko and Daniil N. Kozlov
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Andrey V. Chinilin: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
Nikolay I. Lozbenev: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
Pavel M. Shilov: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
Pavel P. Fil: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
Ekaterina A. Levchenko: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
Daniil N. Kozlov: FRC “V. V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia

Land, 2024, vol. 13, issue 12, 1-16

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

Keywords: remote sensing; bare soil image; bare soil mosaic; spectral indices; predictive soil mapping; Google Earth Engine; soil cover patterns (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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