Preprocessing tools for Landsat data: BRDF (Bidirectional Reflectance Distribution Function) and Topographic Corrections.
Please contact Shi Qiu (shi.qiu@uconn.edu), Rong Shang (shangrong@uconn.edu), and Zhe Zhu (zhe@uconn.edu) at Department of Natural Resources and the Environment, University of Connecticut if you have any questions.
The BRDF correction is to use the c-factor approach (Roy, D. P. et al., 2016) based on the RossThick-LiSparse-R BRDF model (Schaaf, Crystal B., et al. 2002).
The SCS correction is equivalent to projecting the sunlit canopy from the sloped surface to the horizontal surface in the direction of illumination (Gu, D. et al., 1998).
The SCS+C model is based on the same SCS model, but it integrates a semi-empirical parameter (C) that can significantly reduce the overcorrection caused by the scattered radiation from the source of illumination (Soenen, S. A. et al., 2005). Also, the computing efficiency was improved by a stratified sampling approach (Qiu, S., et al., 2017).
The IC model was proposed to remove the dependency of the reflectance on the cosine of the local solar incidence angle (cosi) based on the same linear regression shown (Tan, B. et al., 2013).
If using those functions, please cite the following papers:
paper 1: Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L. and Zhu, Z., 2019. Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sensing, 11(1), p.51.https://doi.org/10.3390/rs11010051.
paper 2: Qiu, S., He, B., Zhu, Z., Liao, Z. and Quan, X., 2017. Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images. Remote Sensing of Environment, 199, pp.107-119. https://doi.org/10.1016/j.rse.2017.07.002. (ONLY for SCS+C function)