> For the complete documentation index, see [llms.txt](https://2025.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2025.istvs.org/submissions/papers/6965.md). # 6965 - CORRELATION BETWEEN OPTICAL REFLECTANCE AND LOAD-BEARING CAPACITY OF SOILS {% hint style="info" %} Paper presented at ISTVS 2025 | 55th Conference of the International Society for Terrain-Vehicle Systems {% endhint %} **Authors:** *György Pillinger, Peter Prof. Kiss* **Keywords:** Optical Reflectance; Load-Bearing Capacity; Soils; Moisture content **Abstract:** An important factor in route planning for autonomous off-road vehicles is determining soil condition, particularly load-bearing capacity. Direct (in situ) measurement of this soil property is challenging, and its determination through remote sensing technologies is an emerging area of research. In laboratory tests using prepared soil samples, a strong correlation was observed between the soil surface reflectance in the visible range (400-700 nm) and the load-bearing capacity. A bevameter device was used to determine the load-bearing capacity of each sample at various moisture contents. Measurements were taken for eight different soil textures at different moisture levels, starting from the dry state and gradually increasing to full saturation. This approach allowed us to observe how changes in moisture content affect the load-bearing capacity of each soil texture. Reflectance measurements were then performed on these samples using a portable spectrophotometer. The optical reflectance data were compared with the load capacity measurements obtained from the bevameter. This process enabled the development of a predictive equation that incorporates constants representing the soil's reflectance in its dry state. The resulting equation accurately captures the relationship between reflectance and load-bearing capacity under laboratory conditions. While these results are based on a controlled environment, they offer a valuable foundation for estimating load-bearing capacity in real-world scenarios. Further research, incorporating field data and accounting for environmental variables, will be essential for refining this approach and developing more accurate predictive models for autonomous off-road vehicle navigation. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://2025.istvs.org/submissions/papers/6965.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.