Geostatistical Approaches to Conflation of Continental Snow Data
Jingxiong Zhang 1+, Phaedon Kyriakidis 2 and Richard Kelly 3
1 School of Remote Sensing Information, and Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 LuoYu Road, Wuhan 430079, China
2 Department of Geography, University of California, Ellison Hall 5710, Santa Barbara, CA 93106-4060
3 Department of Geography, Faculty of Environmental Studies, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada
Abstract. Information on snow cover extent and mass is important for characterization of hydrological systems at different spatial and temporal scales, and for effective water resources management. This paper explores geostatistics for conflation of ground-measured and passive microwave remotely sensed snow data, which are commonly known as primary and secondary data, respectively. A modification to conventional co- kriging is proposed, which first estimates differenced local means between sparsely distributed primary data and densely sampled secondary data by co-kriging, followed by a best linear estimation of the primary variable based on the primary data and bias-corrected secondary data, with variogram models revised in the light of corrections made to the original secondary data. An experiment was carried out with snow depth (SD) data derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument and the World Meteorological Organization (WMO) SD measurement, confirming the effectiveness of the proposed methodology.
Keywords: geostatistics, conflation, co-kriging, snow data, passive microwave remote sensing