> For the complete documentation index, see [llms.txt](https://gryfn.gitbook.io/gryfn-operations/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gryfn.gitbook.io/gryfn-operations/operations/environmental-factors.md). # Environmental Factors ## Sky Conditions As operators of research-grade remote sensing systems, we always pray for good weather. But this is not something we can control. Keeping close tabs on forecasts is a vital part of the success of research remote sensing, especially when involving hyperspectral sensors. Depending on a user's requirements, hyperspectral collection requires bright, consistent lighting for optimal data capture. This means flying within two hours of solar noon during the summer season, limiting to one or one and a half hours from solar noon the further in time from summer solstice. We recommend having no clouds in the sky when possible, but at the very least no clouds may pass in front of the sun during data collection. Extreme haze and other particulate matter can also cause reduced data quality and should be avoided when possible. ## Wind When thinking in the context of remote sensing, especially hyperspectral line-scan sensors that are very sensitive to aircraft attitude and speed, wind plays a big factor. Beyond the obvious limitations of the platform carrying the sensors and its ability to fly safely, we have to consider the implications of wind on the aircraft and how it affects the aircraft's stability, from the lens of maximizing our remote sensing data quality. Many factors will impact how much wind can be tolerated while achieving sufficient data quality. A few of these are: 1. Ratio of payload weight to total payload capacity of an aircraft 2. Aircraft size and number/power of motors 3. Aircraft hull design and how aerodynamic it is 4. Flight controller's response and stability in wind A general recommendation, with the caveats above and your own personal experience in assessing data quality in various wind levels, is that 50% of an aircraft's rated wind resistance may still be suitable for maximizing data quality. Beyond this, caution is advised, and addressing the potential for instability should be considered when flight planning and configuring sensors (adding additional oversampling and sidelap in hyperspectral, for example). --- # 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://gryfn.gitbook.io/gryfn-operations/operations/environmental-factors.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.