基于NDSI-Albedo特征空间的MODIS积雪丰度信息反演方法研究

Inversion of the MODIS snow abundance ratio based on NDSI-Albedo feature space

积雪是新疆地区重要的水源补给,是冰冻和融雪洪水灾害的直接原因,也是水资源管理、气候变化、灾害防治和融雪模拟预报的主要参数。针对多种积雪信息提取方法的优缺点,提出运用特征空间方法,构建积雪丰度反演模型,并与支持向量机提取积雪丰度进行精度对比分析,NA模型方法的相关系数(R^2)值比支持向量机方法高2.4百分点,而均方根误差(RMSE)提高了0.106。结果表明:利用归一化差分积雪指数(NDSI)和反照率(Albedo)建立二维特征空间反演积雪丰度的方法是可行的,并且提取精度优于支持向量机(SVM)方法。因此,该方法对水资源管理、气候变化以及洪水模拟预测等方面的研究具有一定参考意义。

As one of the most important surface water resources in Xinjiang, accumulated snow （snowcover） plays an irreplaceable role in sustainable economic and social development. And it is also an important factor affecting the ground water resource management, climate change, disaster prevention and snowmelt simulation forecast. While the snowcover area is the main parameters of accumulated snow, as well as one of the main input parameters for water management, climate change and snowmeh flood simulation and prediction research. At present, the access to snow- cover area is via the image data＇ s NDSI obtained from Landsat TM and MODIS, mixed pixel decomposition or di- rect application of MODIS snoweover products MOD10A2 data. However, the applicability of the various ways in dif- ferent locations varies. Snowcover abundance represents the pixel within the snowcover content, so the improvement of snowcover abundance extraction accuracy can promote snowcover classification accuracy increases, thereby in- creasing the extraction accuracy of the snoweover area. For the problem of the fluctuation of snowcover the normal- ized difference snowcover index （ NDSI ） values （usually 0.4） for different locations, this paper introduces Albedo which is sensitive to accumulated snow, and normalized difference snowcover index （NDSI） to construct feature space. According to the scatter in the feature space and the actual situation of the study area to establish the inver- sion NA model which is applicable to Xinjiang snowcover abundance. In this study, two indicators of NDSI and A1- bedo are calculated by the 500 m-resolution MODIS images data. The NA model is built based on the upper left arc distribution of the scattered points, and further use the higher resolution Landsat TM data relative to MODIS data to test the accuracy of model inversion results, and then compare with the results obtained from Support Vector Ma- chine （SVM） method. In this paper, 53 validation points were randomly selected based on MODIS images, and de- termine the pixel corresponding to the NDSI image calculated by Landsat TM data, then take this pixel as the cen- ter to acquire 5 pixels x 5 pixels square, furthermore, get the mean of the 25 pixels as the center pixel value. Final- ly, two indicators of the root mean square error （ RAISE ） and correlation coefficient （ Rg ） are used for the test of two extraction methods respectively. The results show that the NA model established by two-dimensional feature space can better invert snowcover abundance of the study area. The correlation coefficient （ RE ） and the root mean square error （ RMSE ） of the first method is 0.857 and 0.093 ; while the correlation coefficient （ R2 ） and root mean square error （ RMSE ） of the second method is 0.833 and 0.199, the correlation coefficient （ R2 ） of NA model is 2.4 percentage points higher than the model of SVM, while the root mean square error （ RAISE ） improved 0.106, which shows that the NA model retrieval accuracy is better than the SVM method. This paper introduces the feature space method to the inversion of snow information, which is widely used in many areas, such as desertification monitoring, drought monitoring and soil salinization monitoring. Through the test of high-resolution image and comparison with the SVM method, it proves that the method of this paper is feasible, and its accuracy is better than the SVM method. So it provides a valuable reference to the inversion of accumulated snow information in the arid areas.