藻类水华爆发已成为影响内陆水体生态环境的重要因素。 遥感能够提供实时的大范围观测, 在水华监测中起到越来越重要的作用。 遥感植被指数已广泛应用于藻类水华监测中, 通过对研究区植被指数图像进行阈值分割, 能够反映不同子区域内的藻类爆发程度； 然而阈值分割法的结果只能反映某一时间点（图像获取时）的藻类爆发状况, 无法表征长时间内藻类的变化。 相比于单个时间点的植被指数, 植被指数时间谱（时谱）包含藻类的物候信息, 能够更加全面准确地反映藻类的长时间变化。 目前, 植被指数时间谱还尚未应用到水华相关研究中。 选取2001年—2013年太湖区域的MODIS NDVI数据, 构建年度NDVI时谱数据, 利用(support vector machine, SVM)方法对每年的太湖蓝藻水华爆发强度进行分类, 将太湖重度、 中度和轻度蓝藻水华爆发的区域以及水生植物的区域提取出来, 得到其空间分布和面积； 并从2007年的时谱数据中抽取了8个时间点的NDVI图像, 利用传统阈值分割法提取太湖重度、 中度和轻度蓝藻水华爆发的区域, 将结果与2007年时谱数据分类的结果进行对比。 结果表明： 所提出的方法能够更加全面准确地对太湖蓝藻爆发强度进行分类, 通过NDVI时谱曲线提供的丰富物候信息可准确区分蓝藻与水生植被区域。 本研究有望为准确掌握和预测藻类水华的爆发趋势及强度提供有效手段。

Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae. With threshold segmentation of VI, the area of algal bloom can be extracted from images. However, the result of threshold segmentation only reflects the condition of algae when images were generated. Compared to separated VI data obtained at a particular moment of time, temporal spectral VI data contains phonological information of algae, which may be used to evaluate algal bloom more accurately and comprehensively. This study chose MODIS NDVI data of the Lake Taihu from 2001 to 2013, and constructed temporal spectral data for each year. Then, we determined the feature temporal spectra of severe cyanobacteria bloom, moderate cyanobacteria bloom, slight cyanobacteria bloom and aquatic plants, and separated these four kinds of objects using SVM (Support Vector Machine) algorithm, getting the spatial distribution and area of them. In order to compare the results of our method with traditional threshold segmentation method, we chose 8 separated NDVI images from the temporal spectral data of 2007. With the threshold 0.2 and 0.4, cyanobacteria bloom was classified into three degrees: severe cyanobacteria bloom, moderate cyanobacteria bloom, and slight cyanobacteria bloom. By comparison, it showed that our method reflected cyanobacteria bloom more comprehensively, and could distinguish cyanobacteria and aquatic plants using the phonological information provided by NDVI temporal spectra. This study provides important information for monitoring the algal bloom trends and degrees of inland lakes, and temporal spectral method may be used in the forecast of algal bloom in the future.