Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm

Abstract

Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to explore the impact of vegetation on the extraction of the FSC algorithm, this study developed the normalized difference vegetation index (NDVI)-based Bivariate Linear Regression Model (BV-BLRM) to calculate the FSC. Then, the overall accuracy of the model and its changes under different classification conditions were evaluated and the relationship between the accuracy improvement and different underlying surfaces and elevations was analyzed. The results show that the BV-BLRM model is more accurate than MODIS’s traditional univariate linear algorithm for FSC (MOD-FSC) in each underlying surface. Overall, regarding the accuracy of the BV-BLRM model, the RMSE is 0.2, MAE is 0.15, and accuracy is 28.6% higher than the MOD-FSC model. The newly developed BV-BLRM model has the most significant improvement in the accuracy of FSC retrieval when the underlying surface has high vegetation coverage. Under different classification accuracies, the accuracy of BV-BLRM model was higher than that of MOD-FSC model, with an average of 30.5%. The improvement of FSC extraction accuracy by the model is smaller when the underlying surface is perpetual snow zone, with an average of 12.2%. This study is applicable to the scale mapping of FSC in large areas and is helpful to improve the FSC accuracy in areas with high vegetation coverage.