Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, i...Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica. The biases of all the models are less than 0.0003 in both visible and near-infrared regions. Moreover, with the exception of the Staylor model, all models have root-mean-square errors of around 0.02, indicating that they can simulate the reflectance magnitude well. The R2 performances of the Ross-Li and Roujean models are higher than those of the others, indicating that these two models can capture the angle distribution of snow surface reflectance better.展开更多
基金supported by the National "863" Program of China (No. 2009AA122101)the National Natural Science Foundation of China (Nos. 40871160and 60841006)
文摘Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica. The biases of all the models are less than 0.0003 in both visible and near-infrared regions. Moreover, with the exception of the Staylor model, all models have root-mean-square errors of around 0.02, indicating that they can simulate the reflectance magnitude well. The R2 performances of the Ross-Li and Roujean models are higher than those of the others, indicating that these two models can capture the angle distribution of snow surface reflectance better.
