森林地区光学遥感积雪制图方法研究

积雪是地表重要的覆盖物，流域尺度上的积雪分布对该地区的水资源合理利用、气候与环境变化等研究具有重要的意义。目前常用的基于NDSI的遥感积雪制图方法在森林地区具有较低的精度，森林积雪制图成为光学积雪遥感面临的挑战任务。本研究拟加强地面积雪观测，获取高频率的积雪分布真值；通过地物光谱测量，分析不同条件下的森林积雪混合光谱特性。在此基础上，（1）改进SNOMAP算法在森林地区的积雪识别，添加归一化差值林地积雪指数NDFSI实现高精度的Landsat OLI二值森林积雪制图；（2）使用Landsat OLI二值积雪制图结果评价MODIS积雪面积比例产品在我国主要森林地区的精度；（3）发展基于统计的森林地区MODIS积雪面积比例制图方法；（4）建立适用于我国森林地区的积雪面积比例反演模型。通过本研究获取流域尺度高精度的像元和亚像元级积雪面积产品，为水文过程模拟提供更加精确的关键积雪面积输入参数。

Snow is the important coverage on the earth surface. Snow distribution in basin scale is crucial to the rational use of water resources, the research of climate and environment change. The traditional snow cover mapping method produces low accuracy when the underlying surface covered with forests，and snow cover mapping in forests becomes the challenge of optical remote sensing. In this study, we will apply more ground-based testing in order to get more true value of snow cover in forests, and will analyze the mixture spectral characteristics of snow and forests based on the spectral measurement. The main issues being solved in this project include: (1) Developing the binary forest snow cover mapping method of Landsat OLI with high accuracy through adding Normalized Difference Forest Snow Index (NDFSI) to the SNOMAP algorithm; (2)Evaluating the accuracy of MODIS Fractional Snow Cover(FSC) in the typical forests of China, with the Landsat OLI binary snow cover mapping results as true value; (3) Developing the MODIS FSC mapping method based on statistics; (4) Building the FSC inversion model being suitable to the forests of China. The objective of this project is to produce the snow cover products with good accuracy in pixel or sub-pixel, and to provide snow cover area with higher accuracy which is the key input parameter for the simulation of hydrological processes in basin scale.