Project Description: A field campaign at the Santa Rita Experimental Station using drone-based multispectral data synchronized with NEON hyperspectral overflights showed that canopy and geometry driven shadow strongly affects surface reflectance and noise in synoptic satellite sensors (e.g., Landsat, MODIS, VIIRS). Despite corrections, major uncertainties remain about validation adequacy, the dominance of shadow in satellite observations, and whether centimeter-scale drone data can trace geometry-induced noise that persists in vegetation index products after compositing.
The goal of this (prototypic and currently unfunded) effort is to conduct a detailed analysis of the extent of shadow within synoptic imagery and to quantify its impact on satellite observations.
The intern will help analyze the collected data and identify to what degree the data is impacted.
NASA Relevance: Data accuracy and validation is critical to the derived science, policies, and planning.
Work Description:
- Organize the collected image data
- Write Python code to help analyze the data
- Help interpret and correlate the data to higher order observations
- Potentially help draft the proposal and/or papers
Open or Reserved Project: Open, 2 available positions