Spectral Unmixing of Blended Reflectance for Denser Time-series Mapping of Wetlands

The orbiting cycle and frequent cloud contamination have limited the applications of the moderate-resolution remotely sensed data for detecting rapid land cover changes that are critical to the monitoring of wetlands. It is necessary to use multiple remotely sensed data sources that have different spatial resolution and temporal frequency, because both spatial and temporal details are important in understanding the mechanisms in wetland cover changes. This study examined the applicability of linear spectral mixture analysis to the blended reflectance that was generated by incorporating the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Nine TM and MODIS images of the Poyang Lake area, China acquired in 2004 and 2005 were used to blend the reflectance. In order to account for the spectral variations in materials, we incorporated the multiple endmember spectral mixture analysis (MESMA) in unmixing the blended reflectance. The average absolute differences between the land cover fractions derived from the blended image and those from the observed image were calculated as well as correlation coefficients. Our results demonstrated that MESMA could unmix the blended reflectance generated by ESTARFM. However, due to the existence of the blended pixels with large difference in reflectance from the observed reflectance, the land cover fractions derived from the blended reflectance did not match with those derived from the observed reflectance as well as expected. It is also suggested that the comprehensiveness of the endmember spectral libraries was another factor influencing the agreement.