Geostatistical approaches to conflation of continental snow data

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, here referred to as primary and secondary data, respectively. A modification to conventional cokriging is proposed, which first estimates differenced local means between sparsely distributed primary data and densely sampled secondary data by cokriging, 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.

Acknowledgements

The research is partially supported by a ‘973 Program’ grant (2007CB714402-5). Advice on remote sensing of snow and products validation from Dr James Foster and Dr Dorothy Hall (NASA Goddard Space Flight Center) and Prof. David Robinson (Rutgers University) is greatly appreciated. Helpful suggestions from anonymous referees are received with thanks. The second author would also like to acknowledge funding provided by the National Geospatial Intelligence Agency (NGA) under award number HM1582-07-1-2020.