Evaluation of a useful method to identify snow‐covered areas under vegetation – comparisons among a newly proposed snow index, normalized difference snow index, and visible reflectance
Evaluation of a useful method to identify snow‐covered areas under vegetation – comparisons among a newly proposed snow index, normalized difference snow index, and visible reflectance
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DOI：
10.1080/01431160600639693
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发表时间：
2006-11
影响因子：
3.4
通讯作者：
Y. Shimamura;T. Izumi;H. Matsuyama
中科院分区：
文献类型：
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作者：
Y. Shimamura;T. Izumi;H. Matsuyama
利用 Landsat-7 卫星飞越日本降雪量最多的地区之一新泻县平原上空进行的同步观测，对光学遥感监测积雪地区的客观方法进行了评估。这些观测是在 2002 年和 2003 年春季进行的。使用三种方法识别积雪覆盖区域：(1) 可见光（红色）反射率，(2) 使用可见光和短波红外反射率的归一化差异积雪指数 (NDSI)，以及 (3) 新提出的称为 S3 的积雪指数，它使用可见光、近红外和短波红外反射率。积雪覆盖率（SCR）定义为积雪覆盖区域的像素数与图像中总像素数的比率。用于识别积雪覆盖区域的三个指数的阈值被定义为 SCR 的 50%，无论分析的图像如何，该阈值都会收敛到几乎相同的值。在晴朗条件下，如果不存在植被，可见光（红色）反射率可以准确识别积雪覆盖的区域。 NDSI可以参考归一化植被指数（NDVI），将积雪区域与积雪和植被的混合像素（混合像素）区分开来。 S3 可以在没有任何参考数据的情况下区分积雪覆盖的区域和积雪和植被的混合区域。因此，S3 比 NDSI 更有用，因为它会自动将积雪覆盖的区域与积雪和植被的混合区域区分开来。
Objective methods of monitoring snow‐covered areas by optical remote sensing were evaluated using synchronous observations conducted with the passage of the Landsat‐7 satellite over the plains of Niigata prefecture, one of the snowiest regions in Japan. The observations were conducted in the springs of 2002 and 2003. Snow‐covered areas were identified using three methods: (1) visible (red) reflectance, (2) Normalized Difference Snow Index (NDSI) which uses visible and shortwave‐infrared reflectances, and (3) a newly proposed snow index called S3 which uses visible, near‐infrared and shortwave‐infrared reflectances. The Snow‐Cover Ratio (SCR) was defined as the ratio of the number of pixels in snow‐covered areas to the total number of pixels in an image. The threshold value for the three indices used to identify snow‐covered areas was defined as 50% of SCR, which converged to nearly the same value regardless of the images analysed. Under clear conditions, visible (red) reflectance can identify snow‐covered areas accurately if no vegetation is present. NDSI can distinguish snow‐covered areas from mixels (mixed pixels) of snow and vegetation by referring to the Normalized Difference Vegetation Index (NDVI). S3 can distinguish snow‐covered areas from mixels of snow and vegetation without any reference data. S3 is, therefore, more useful than NDSI because it automatically distinguishes snow‐covered areas from mixels of snow and vegetation.