Spectral mixture analysis (SMA) models are highly effective methods used to deal with sub-pixel vegetation coverage estimation, among which linear spectral mixture analysis (LSMA) is the most commonly used one. However, the precision of vegetation coverage estimation retrieved from LSMA is mainly affected by the multiple scattering and end-member spectral variability. Also, anisotropic reflectance effect (ARE), one of the distinctive and inherent properties of surfaces, is very likely to be ignored. This research conducted in situ spectral experiments by using checkerboard-style mixture design which incorporated three types of surfaces. After discussing and analyzing the traditional multiple scattering and the end-member spectral variability, the effect of anisotropic reflectance on the spectral mixture analysis for vegetation coverage estimation was further evaluated. The results indicated that the impact of ARE cannot be neglected. The Carex duriuscula coverage estimation was more accurate after considering of ARE, when minimizing the effect of the traditional multiple scattering and end-member spectral variability. The root mean square error (RMSE) decreased nearly 52%. These results not only emphasized the importance of integrating ARE into vegetation coverage estimation but also indicated that ARE can be regarded as another significant source of variability within the same end-member class. This study broadens the scope of end-member spectral variability, and may put forward a new thinking and direction for vegetation coverage estimation based on SMA.

Land cover classification is fundamental and critical to the investigation and assessment of land resources and the global change. However, the cloud-prone and rainy weather in mountain areas make it difficult to obtain valid optical remote sensing images. In addition, the complexity of terrain also has a negative impact on the accuracy when classifying land covers using only optical remote sensing imagery. Synthetic aperture radar (SAR) can transmit energy at microwave frequencies that are unaffected by weather conditions. This advantage gives SAR all-day and all-weather imaging capability. In this paper, the research area is situated in the mountain areas of Southeast Chongqing. We obtained the backscattering coefficient through a series of preprocessing of the Sentinel-1A polarization data. Then, according to the statistical analyses of VV/VH polarization backscattering coefficients, textures, elevations and slopes of all kinds of land covers, we employed object oriented approach on single temporal and multi-temporal images to improve land cover classification accuracy by combing these features. Finally, we compared the classification results with the result of Landsat 8 OLI data. The research indicated that: 1) The object-oriented classification method with single temporal SAR data has about the same accuracy as the OLI Landsat 8 data, the object-oriented classification with multi-temporal SAR data has the best classification result with the total accuracy 85.65% and Kappa coefficient 0.829 9. 2) SAR data have the advantages in extracting broad-leaved forest and artificial building which improve the accuracy by more than 10%. The classification with multi-temporal data has the advantage in the extraction of coniferous and broad-leaved mixed forest and farmland whose accuracy is about 9% higher than that with single temporal data. 3) Forest is the main land cover type in the study area which accounts for 42.68% of the total area, followed by farmland and shrub, and the artificial construction, grassland and river are less.