Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2
Lian He, Binghua Xue, Fengming Hui, Xiao Cheng
Abstract
Snow on sea ice plays an important role in the polar climate system and accurate snow depth (SD) information on sea ice is necessary for satellite estimates of sea ice thickness from both radar and laser altimeters. In this study, three independent SD products have been generated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data using different algorithms which are trained separately based on three different reference data sets, including the Ice Mass Balance Buoy (IMB) measured snow depth, the Operation IceBridge (OIB) airborne SD measurements and the monthly altimetric snow depth (ASD) product derived from CryoSat-2 (CS2) and ICESat-2 (IS2). An in-situ validation based on SD measurements from OIB and IMB and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these SD products. Furthermore, a merging scheme based on error variance estimates obtained from TC analysis has been tested for merging these three SD products into a single data set. Results indicate that TC can provide information about the error characteristics of each product which is complementary to in-situ validation. Meanwhile, the merged SD product is superior to its input parent products and achieves an overall good accuracy with the correlation (r) and root mean square error (RMSE) values being 0.70 and 6.06 cm when validating using OIB data, and 0.75 and 9.12 cm when validating using IMB data. This study demonstrates the great potential of the TC method in validating and merging snow depth estimates on sea ice.