Comparison of FY-3C VIRR and MODIS Time-Series Composite Data for Regional Land Cover Mapping of a Part of Africa

This paper compared multi-temporal composite products of FY-3C VIRR and MODIS for regional land cover (LC) mapping of a part of Africa. LC classification was conducted using a random forest algorithm in the Scikit-Learn library of Python after the model was trained using reference data that were collected by combining three techniques and employed simultaneously i.e. Landsat 8 image interpretation, referring exiting maps, and crosschecking on Google Earth pro/ maps. Based on the overall accuracy (OA) and kappa value (k) the two instruments showed insignificant performance variation although FY-3C VIRR achieved slightly higher OA (.82) and k (.79) which are 1 % higher than MODIS. However, the two instruments exhibited notable performance differences in discerning individual classes. The FY-3C data are generally better for vegetation classification; MODIS, whereas, classified built-up, bare/sparse-vegetation, and water bodies with better accuracy. Moreover, the incorporation of FY-3C thermal bands improved its accuracy significantly, by 3%.