Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment
Introduction
New Zealand relies largely on water as its main renewable energy source. More than one-quarter of New Zealand's total energy needs are provided by electricity generation, of which 60% to 70% comes from hydro-electric plants. In the Southern Alps of New Zealand, where most of the hydro-electric plants are located, a significant part of the water resource is temporarily stored in the form of snow and ice. The seasonal snow cover in New Zealand largely impacts the regional climate and hydrology (Cutler and Fitzharris, 2005, Fitzharris et al., 1999, Weingartner and Pearson, 2001) as in other parts of the world (Elder et al., 1991, Schmugge et al., 2002, Verbunt et al., 2003). In 2003 and 2006, power crises stressed the strong dependence of New Zealand on water resources for electricity generation. Water managers suggested a strong link between the water available in the reservoirs and the snow stored during the previous winter. These repetitive water shortages revealed that New Zealand needs to improve its understanding about seasonal inflows originating from snow cover and to develop new technology to improve the monitoring and forecast of water availability.
It has been long-established that satellite remote sensing is a very powerful tool to monitor remote and hardly accessible areas (Andersen, 1982, Hall and Martinec, 1985, Rango et al., 1977). Recent technological advances and the multiplicity of sensors have made observations from satellite even more essential for monitoring snow cover (Dozier and Painter, 2004, Hall et al., 2002, König et al., 2001). Various approaches were reported for mapping snow-cover using a variety of sensors (Dozier, 1989, Kelly et al., 2003, Shi et al., 1994). Maps of snow cover can be used to calibrate, assess, and improve snow model (Dozier & Painter, 2004) and thus contribute to the better prediction of snow water equivalent (SWE) (Clark et al., 2006). Therefore the potential of spatial observation of snow and its contribution to runoff modelling has been the focus of numerous works in many parts of the world (Baumgartner and Apfl, 1994, Bernier et al., 2003, Molotch et al., 2004, Swamy and Brivio, 1997, Tekeli et al., 2005). In New Zealand, only marginal studies have investigated the potential of remote sensing for monitoring snow cover (Fitzharris and McAlevey, 1999, Hickman, 1972).
In this context we aimed to contribute to the local efforts towards a better management of water resources in New Zealand by addressing the mapping of seasonal snow cover on a routine basis. MODIS daily repeat time, multi-spectral capabilities and medium spatial resolution proved to be successful for this task at both local and global scale (Hall et al., 2002, Wang et al., 2008). Despite the availability and general performances of the MODIS MOD10 Snow Product (Hall and Riggs, 2007, Hall et al., 2002, Klein and Barnett, 2003, Liang et al., 2008, Maurer et al., 2003, Zhou et al., 2005), obvious limitations affected our local environment and stressed the need for an adapted and robust algorithm to map snow cover with the highest possible amount of spatial details. Improved spatial resolution as well as subpixel capability of the proposed method are highly desirable factors in rugged terrain. They enable an improved determination of snow as well as more accurate depiction of the winter snowline which elevation can be monitored. An improved spatial resolution is also relevant to satisfy better the target of 100 m spatial resolution recommended for the Global Climate Observing System (GCOS) with regard to the scale of satellite observations (GCOS, 2006). Alternatively to the classic binary representation of snow cover, maps of subpixel snow fraction are becoming increasingly popular (Foppa et al., 2004, Painter et al., 2003, Rosenthal and Dozier, 1996, Salomonson and Appel, 2006, Vikhamar and Solberg, 2003). They are recommended for mapping snow in alpine terrain because they limit the overestimation error usually made by binary classification algorithms when applied on spatially highly-variable or patchy snow cover (Dozier & Painter, 2004). Despite the fact that linear mixture analysis is still not suitable for mapping snow at the global scale partly due to computing requirements (Dozier & Painter, 2004), such models have proven to be efficient at classifying snow (Klein and Isacks, 1999, Nolin et al., 1993). However, to date, few or no examples exist applying linear mixture analysis to produce maps of snow fraction in an automated and operational way, either at the local or global scale (König et al., 2001).
In this paper, we propose a comprehensive methodology, well-suited to our mountainous environment, to map routinely the seasonal snow cover from MODIS Level-1B Swath data product. The method implements an innovative wavelet-based fusion technique (Sirguey et al., 2008) that makes possible the mapping of snow at 250 m spatial resolution instead of 500 m. Then, a constrained linear unmixing technique is applied to estimate subpixel snow fractions. In order to achieve unmixing on a routine basis, the proposed method is based on an integrated atmospheric and topographic correction of MODIS top of atmosphere radiance data using the ATCOR3 approach (Richter, 1998). The method was applied to produce 679 snow maps for the period from February 2000 to May 2007 in the upper Waitaki catchment (South Island, New Zealand). In the first section we present the upper Waitaki catchment and the imagery used. The second section details the various steps of the routine implemented to convert the imagery into maps of subpixel snow fraction. It involves the projection of the MODIS Level-1B Swath imagery, the masking of clouds, the multispectral fusion, the atmospheric and topographic correction (ATOPCOR), the unmixing process, and the cleaning of snow maps. Finally the last section provides a careful assessment of the accuracy of the snow maps and of the robustness of the method based on the cross-comparison with high-resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).
Section snippets
Area of interest
With almost 12,000 km2 of which 2.5% are covered with perennial snow and ice, the Waitaki basin is the most important hydro catchment in New Zealand. It provides approximately one-third of New Zealand's hydro electric generation, which means around 20% of the total electricity production. Situated in the South Island of New Zealand (Fig. 1), the catchment is dominated by Kā Tiritiri o te Moana/Southern Alps whose highest peak Aoraki/Mt. Cook reaches 3754 m above sea level. The extreme variation
Processing of MODIS level-1b data
All steps leading from the MODIS Level-1B Swath data to the retrieval of the maps of subpixel snow fraction are detailed in the following sub sections. The entire process is illustrated in Fig. 2.
Accuracy assessment and discussion
An efficient way to assess the accuracy of subpixel snow fraction maps is by taking advantage of simultaneous imagery with a finer spatial resolution when available (Foppa et al., 2007, Painter et al., 2003, Rosenthal and Dozier, 1996, Salomonson and Appel, 2004, Salomonson and Appel, 2006, Vikhamar and Solberg, 2003). In this study we took advantage of the simultaneity of acquisition between the ASTER and MODIS sensors on the TERRA platform. ASTER spatial resolution (15 m) compared to the
Conclusion and outlook
In this study, we developed a robust methodology to monitoring, with MODIS imagery, seasonal snow cover in alpine terrain with a relatively high level of spatial details. We successfully integrated various remote sensing techniques in a single algorithm to produce snow maps on a routine basis. Our approach benefits from an image fusion algorithm that enabled us to obtain 250-m spatial resolution snow maps, a feature highly desirable in rugged topography. A comprehensive model for atmospheric
Acknowledgments
The authors thank the Meridian Energy for providing inflow data and Tim Kerr from the University of Canterbury for providing SNOWSIM-Pukaki model outputs. The satellite data used in this study were acquired as part of the NASA's Earth Science Enterprise. The MODIS Level-1B data were processed by the MODIS Adaptive Processing System (MODAPS) and the Goddard Distributed Active Archive Center (DAAC), and are archived and distributed by the Goddard DAAC. We also thank the Global Land Ice
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