Experience in mapping erode soils based on remote sensing data
Abstract
Purpose. Demonstration of the capabilities of modern Earth remote sensing data and geoinformation technologies for the identification and large-scale mapping of eroded soils with a special emphasis on the detection of sheet water erosion, which remains insufficiently represented on the existing soil maps of Ukraine.
Methods. Data processing was performed on the Google Earth Engine platform by generating a bare soil composite image based on the Bare Soil Index (BSI), scene classification masks, and median pixel reduction. Identification of eroded areas was carried out by visual interpretation taking into account spectral, spatial, and morphological features of water erosion.
Results. The study was conducted within the territory of the Novoodeska territorial community of Mykolaiv region using archival maps of agro-production soil groups at a scale of 1:10,000 and multitemporal Sentinel-2 satellite images for the period 2020–2025. Significant discrepancies were identified between archival soil maps and the current spatial distribution of eroded soils, indicating an intensification of degradation processes over recent decades. The application of multitemporal bare soil composites ensured reliable delineation of severely eroded soils and outcrops of parent materials within agricultural landscapes. As a result, an updated large-scale map of severely eroded soils at a scale of 1:10,000 was created, which significantly exceeds existing cartographic materials in terms of detail and reliability.
Conclusion. It has been proven that the integration of multitemporal Sentinel-2 satellite imagery and cloud-based geoinformation analysis significantly increases the accuracy of water erosion mapping, especially its sheet forms. The proposed approach is an effective tool for updating soil-cartographic materials.
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References
Verkhovna Rada of Ukraine. (2022). On approval of the Concept of the National Target Program for land use and protection (Order No. 70-r). https://zakon.rada.gov.ua/laws/show/70-2022-%D1%80#Text (in Ukrainian).
Overview of soil conditions of arable land in Ukraine. (2020). FAO. https://doi.org/10.4060/ca7761en (in Ukrainian).
Achasov, A., Achasova, A., Titenko, G., Seliverstov, O., & Krivtsov, V. (2021). Assessment of the Ecological Condition of Soil Cover Based on Remote Sensing Data: Erosional Aspect. SHS Web of Conferences, 100, 05014. https://doi.org/10.1051/shsconf/202110005014
Wang, J., Yang, J., Li, Z., Ke, L., Li, Q., Fan, J., & Wang, X. (2024). Research on Soil Erosion Based on Remote Sensing Technology: A Review. Agriculture, 15(1), 18. https://doi.org/10.3390/agriculture15010018
Seutloali, K. E., Dube, T., & Mutanga, O. (2017). Assessing and mapping the severity of soil erosion using the 30-m Landsat multispectral satellite data in the former South African homelands of Transkei. Physics and Chemistry of the Earth, Parts A/B/C, 100, 296–304. https://doi.org/10.1016/j.pce.2016.10.001
Polovina, S., Radić, B., Ristić, R., & Milčanović, V. (2024). Application of Remote Sensing for Identifying Soil Erosion Processes on a Regional Scale: An Innovative Approach to Enhance the Erosion Potential Model. Remote Sensing, 16(13), 2390. https://doi.org/10.3390/rs16132390
Žížala, D., Juřicová, A., Zádorová, T., Zelenková, K., & Minařík, R. (2018). Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic. European Journal of Remote Sensing, 52(sup1), 108–122. https://doi.org/10.1080/22797254.2018.1482524
Malinowski, R., Heckrath, G., Rybicki, M., & Eltner, A. (2022). Mapping rill soil erosion in agricultural fields with UAV‐borne remote sensing data. Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.5505
Wang, B., Zhang, Z., Wang, X., Zhao, X., Yi, L., & Hu, S. (2021). The Suitability of Remote Sensing Images at Different Resolutions for Mapping of Gullies in the Black Soil Region, Northeast China. Remote Sensing, 13(12), 2367. https://doi.org/10.3390/rs13122367
Britannica Editors (2019, September 24). Chernozem. Encyclopedia Britannica. https://www.britannica.com/science/Chernozem-FAO-soil-group
Sentinel-2. Copernicus Data Space Ecosystem. https://dataspace.copernicus.eu/data-collections/copernicus-sentinel-data/sentinel-2
Google Earth Engine. Google Earth Engine. https://earthengine.google.com/
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/jstars.2020.3021052
Achasov, A., & Achasova, A. (2020). Features of Visual Decoding of Water Erosion by Remote Sensing Data. Man and Environment. Issues of Neoecology, (33). https://doi.org/10.26565/1992-4224-2020-33-13 (in Ukrainian).
Achasov, A. B., Achashova, A. O., Buligin, S. Yu., et al. (2010). Large-scale soil mapping using integrated analysis of remote sensing data and digital elevation models: Methodological recommendations. Kharkiv National Agrarian University. (in Ukrainian).
Achasov, A. B. (2016). Anti-erosion optimization of agricultural landscapes: A geoinformation approach. Kharkiv National Agrarian University. (in Ukrainian).
Achasov, A., Dyadin, D., Sinna, O., & Siedov, A. (2026). Maps for the EcoProfile: Novoodeska territorial community. International Renaissance Foundation; Foundation for the Development of Modern Media. https://nodmr.gov.ua/images/kontent/side_menu/Stratehiya_ta_investytsiyna/2025/ЕкоПрофіль_A4_НТГ_1.pdf (in Ukrainian).
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
Velastegui-Montoya, A., Montalván-Burbano, N., Carrión-Mero, P., Rivera-Torres, H., Sadeck, L., & Adami, M. (2023). Google Earth Engine: A Global Analysis and Future Trends. Remote Sensing, 15(14), 3675. https://doi.org/10.3390/rs15143675
Schmitt, M., Hughes, L. H., Qiu, C., & Zhu, X. X. (2019). SEN12MS – A curated dataset of georeferenced multi-spectral Sentinel-1/2 imagery for deep learning and data fusion. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W7, 153–160. https://doi.org/10.5194/isprs-annals-iv-2-w7-153-2019
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
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