Browsing by Author "Joel, Michael Foredapwa"

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    Prediction of the spatial distribution of soil organic carbon content in Central European agriculturally used peatlands: a case study of the Grójec Valley, Central Poland
    (Springer, 2026-03-14) Pindral, Sylwia; Mendyk, Łukasz; Coblinski, João Augusto; Sykuła, Marcin; Joel, Michael Foredapwa; Glina, Bartłomiej
    Purpose Soil organic carbon (SOC) plays a crucial role in ecosystem functioning, especially in agriculturally used grassland habitats, where organic soils often constitute a significant share. They provide a wide range of ecosystem services, such as carbon sequestration and climate regulation, water cycling, and biomass production. At the same time, the grassland environment can be easily degraded by intensive agricultural practices. Understanding the spatial distribution of SOC is crucial for sustainable land management. In this study, we focus on the Grójec Valley, a grassland dominated area in central Poland, to predict the distribution of SOC. The valley is characterized by diverse land cover, including arable land, grasslands, forests, and wetlands. We aimed to use fine-scale auxiliary variables to predict the distribution of SOC content in the uppermost (0–30 cm) soil layers and compare the produced map with existing fine-scale soil maps. Materials and methods Soil samples were collected from 85 locations within the valley and analyzed for SOC content. We implemented the Digital Soil Mapping (DSM) approach using the Quantile Regression Forests (QRF) algorithm to predict SOC content within the study area. As a set of covariates, we included Sentinel-2 data-based indices and various layers produced from a digital elevation model. Results The model proved effective in predicting SOC content across the entire valley. Areas with high SOC content were associated with specific land cover types and corresponded with the organic soil contours on the soil-agricultural map. Our findings can provide important information for decision-makers and farmers about the state of soils within the studied area (which well represent the agriculturally used fen peatlands of Central Europe) to help with targeted soil conservation efforts. Moreover, fine-scale maps of SOC can be useful for precision agriculture and sustainable land use planning. Conclusion This study highlights the potential of integrating digital soil mapping techniques with remote sensing data to predict SOC content at a fine spatial scale. Our results demonstrate that such an approach can effectively capture the heterogeneity of organic carbon in agriculturally used fen peatlands. These findings contribute to a better understanding of carbon dynamics in drained soils, which is essential for improving soil carbon accounting, supporting climate change mitigation strategies, and guiding sustainable land management practices across temperate regions.