摘要
利用EOS/MODIS可见光、近红外及短红外多通道资料以及新疆地区积雪深度气象台站实测资料等,在考虑积雪性质包括积雪粒子相态、积雪年龄等的差异以及积雪区的下垫面条件包括地表粗糙度、土地覆盖类型等的不同的情况下进行积雪分类,在此基础上,建立EOS/MODIS积雪深度反演模型,实现深度在30 cm以内的积雪深度反演的主要原理、思路及方法,并对模型的反演结果进行了验证。结果表明,利用该模型对30 cm以内的积雪进行深度反演计算,其精度能达到80%以上。
Snow depth, a very significant factor in agriculture and climate research, is one of the most important parameters for total snow amount calculation. Snow depth retrieval using microwave data has been extensively applied in meteorological operation. However, microwave data has a very low spatial resolution. It is proved that there is a good linear relationship between snow depth and snow surface reflectance in visible to short- infrared window channels when snow has a depth within 30cm, which makes it possible to retrieve snow depth using AVHRR data or MODIS data and station - measured snow - depth data. Compared with AVHRR, EOS/ MODIS data has a higher spatial resolution and more channels. The higher resolution makes topographical parameters such as slope, surface coast be more clearly described and matched with satellite data, and having more channels provides more variables to the model establation.
This paper mainly introduces the principle theory and process to establish a snow - depth retrieval model within 30cm using EOS/MODIS visible to short- infrared window channels＇ data and station-measured data, considering snow characteristics in different physical states（ wet snow, dry snow, frozen snow, old snow, new snow and so on） and various complex underneath conditions including DEM（slope; plain, mountainous area）, land cover such as grassland, forest, cropland, desert, shrubland, wetland, tundra and so on. Based on snow characteristics and underneath conditions, snow is derided into many types： old dry snow in flat grassland, new dry snow in flat grassland, old dry snow in mountainous grassland, old dry snow in flat cropland, new dry snow in flat cropland, old dry snow in flat desert, new dry snow in flat desert and so on. Fourteen kinds of snow have been modeled respectively in this retrieval model.
Through 4 years validation in XinJiang Province of China both using station - measured snow depth data and field - measured data since 2002, the precision of snow - depth retrieval model using MODIS visible to short - infrared window channels＇ data can reach more than 80% for snow with a depth within 30cm. In flat area with single underneath condition, where wind power can be ignored, the model can always get a better precision. On the contrary, in mountainous forest, the precision of the model is not as good as others. The model needs to be improved in many ways. More work will be developed in future to solve the problems the model has.
出处
《干旱区地理》
CSCD
北大核心
2006年第5期718-725,共8页
Arid Land Geography
基金
中国气象局气象新技术推广专项资金项目"MODIS雪深反演数学模型与应用"项目资助
