中文摘要：

利用北疆地区2002，2003和2004年11月一次年3月3个积雪季AMSR—E445个时相的亮温数字图像和20个气象台站实测雪深数据，系统分析了雪深模型的影响因子和研究区样本筛选方法。通过对18和36GHz波段的水平、垂直极化方式的亮温差和实测雪深值回归分析比较，建立了北疆地区基于AMSR—E亮温数据的雪深反演模型，并对模型的精度进行了评价。结果表明，1）AMSR—E亮温差受气温、融雪、降水、湿雪、深霜层等因素的严重影响，其中受深霜层的影响最大；2）大于2．5cm的积雪深度SD同垂直极化方式的18和36GHz波段的亮温差（Tb18v—Tb36v）之间具有较好的线性相关性，其回归公式为SD=0．49（Tb18v—Tb36v）＋8．72，相关系数达0．65。3）当雪深为3～10cm时，反演模型平均误差为-7．1cm，平均绝对误差为7．1cm，RMSE值达7．7cm；当雪深为11～30cm时，平均误差为1．8cm，平均绝对误差为4．9cm，RMSE值为9．1cm；当雪深大于30cm时，平均误差为8．9cm，平均绝对误差为9．4cm，RMSE值为18．1cm。4）该模型在北疆地区优于Chang算法，基本能反映北疆地区雪深变化趋势。当地表为中雪覆盖时，反演雪深值和实测值之间的一致性较高，当地表为浅雪和深雪覆盖时，反演模型的误差较大，其反演精度较低，还有待于进一步研究。

英文摘要：

Using 445 temporal AMSR-E brightness temperature and snow depth data measured by 20 meteorological stations during the three snow seasons from December to March in 2002 to 2004 in Northern Xinjiang, we systematically analyzed the approaches of screening samples and impact factors for establishing a snow depth model. Through regression analysis of horizontal and vertical polarization brightness temperature differences in the 18 and 36 GHz bands, and of snow depth values, a snow depth model was established based on the AMSR-E brightness temperature data in northern Xinjiang. Accuracy of the model was evaluated. 1） AMSR-E brightness temperature differences were seriously affected by temperature, snowmelt, rain, wet snow and deep frost layers, of which the most important factor was deep frost layers. 2） There was a significant correlation between snow depth （y） over 2.5 cm and the vertical polarization brightness temperature difference at 18 and 36 G Hz. The equation was SD= 0. 49 （Tb18w-Tb36v）q-8.72, and the correlation coefficient was up to 0.65. 3） The average error was -7. 1 cm and average absolute error was 7. 1 cm and RMSE was 7. 7 cm when snow depth was 3-10 cm. When snow depth was between 11 and 30 cm, the average error was 1.8 cm, average absolute error was 4.9 cm, and RMSE was 9.1 cm. When the snow depth was over 30 cm, average error was 8.9 cm, average absolute error was 9.4 cm, and RMSE was 18.1 cm. 4） The snow depth model is more accurate than the Chang algorithm in Northern Xinjiang. It showed a good agreement between the simulated and measured snow depth values when the surface was covered by middle depth snow. However, the model＇s aecuracy was lower when the surface was covered by fallow or deep snow. Basically, the model can reflect the trend of snow depth variation in Northern Xinjiang, but it has a low accuracy for fallow or deep snow, and needs to be improved in the future.