Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data

Evergreen forest provides essential ecosystem services such as maintaining the balance in carbon and oxygen cycles and air purification. However, under cloudy and rainy weather conditions, it is difficult to obtain optical remote sensing images with high spatial resolution and complete time series. In addition, surfaces underlying the forest canopy can be complex and fragmented. To solve this problem, we developed a new approach (NDVI
_CV
-LS) for mapping urban evergreen forest at the subpixel scale. In order to capture more accurate growth characteristics of evergreen forest, we harmonized Landsat-8 and Sentinel-2 images with cloud cover less than 10% acquired within 1 year to denser the time-series dataset. In view of the time series fluctuation stability of evergreen forest, the coefficient of variation (CV) of the normalized difference vegetation index (NDVI) was used to distinguish evergreen forest from other vegetation. Meanwhile, the annual minimum NDVI (NDVI
_ann-min
) was used as the parameter in a dimidiate pixel model for estimating fractional evergreen forest cover (FVC
_ever
). Hefei, a cloudy and rainy subtropical city in China, was selected as a case study to evaluate the validity of the model. The verification results revealed that harmonizing Landsat-8 and Sentinel-2 time-series images to extract evergreen forest improved the overall accuracy by 8% compared with using Landsat-8 images alone, indicating that the NDVI
_CV
-LS model can improve the accuracy of FCV
_ever
estimation, especially for areas with complex underlying surfaces under cloudy and rainy conditions.