FINCH is a student-led satellite mission whose novel sensor and cost effective form seek to provide crop residue mapping at a much lower cost and aid in smart-agriculture initiatives. To achieve this, crop residue must be accurately quantified using the limited reflectance range of 900nm to 1700nm. Hence, novel unmixing methods must be developed. Two datasets were evaluated: a laboratory-acquired dataset and a simulated, atmospherically propagated dataset. Multiple unmixing methods were tested, including Linear Regression, a Bayesian Linear Dirichlet model, K-Nearest Neighbors, Random Forest, and deep learning approaches such as a Multi-Layered Perceptron. Strong performance was achieved on the laboratory dataset, with the Multi-Layered Perceptron achieving an R2 for crop residue of 0.8436, total R2 of 0.8935, and an RMSE of 0.0909 when plotting true to predicted abundances, demonstrating the feasibility of accurate unmixing in controlled conditions. However, performance decreased substantially on the atmospherically propagated dataset, likely due to nonlinearities and other stark differences between datasets that limit transfer learning. These findings indicate that while the lab results are highly promising, additional atmospheric measurements and model adaptations are necessary to achieve full confidence in FINCH’s predictions. Further testing and validation will be critical to establish robustness and guide the development of operational unmixing methods for determination of optical design and imaging requirements.