基于Sentinel-2A岷江上游地表生物量反演与植被特征分析

 Sentinel-2A光学遥感卫星具有很高的空间分辨率、13个多光谱波段、独特的"红边"波段、大宽幅同时数据免费,为区域植被生态环境特征信息提取分析提供全新的解决方案。以岷江上游流域为研究区,选取欧洲空间局（ESA）发布的Sentinel-2A数据,通过对卫星参数、数据类型、格式等级、预处理方法进行分析,利用ESA的SNAP软件,提取岷江上游流域的L2A级数据和场景分类图;在生成的L2A数据基础上结合SNAP软件中生物物理量参数处理模块,分别计算反演出岷江上游的叶面积指数、光合有效辐射吸收率、叶绿素含量、冠层含水量和植被覆盖度5类生物量指标和5个县域内生物物理量均值与标准差,利用叶面积指数、光合有效辐射吸收率和叶绿素含量指标诠释岷江上游5个县域内植被生长及健康状态,再结合场景分类结果对岷江上游植被特征进行区位分析,该研究为区域生态环境定量评估提供了快捷高效的参考依据。

Retrieval of surface vegetation biomass information and analysis of vegetation feature based on Sentinel-2A in the upper of Minjiang River

The Sentinel-2A has rather high spatial resolution, with its 13 multispectral bands, the unique "red edge" band, very wide and free of charge, as an optical remote sensing satellite, serving a bran-new solution for the analysis of the characteristic information extraction of the regional vegetation ecological environment. As a case study, the upper reaches of Minjiang River are considered, with the Sentinel-2A data downloaded from the European Space Agency. The SNAP software is used for extracting the Sentinel-2A L2A data and the scene classification map after the analyses of the satellite parameters, the data type, the format grade, using the pretreatment method. On the basis of the generated L2A data, five biomass indexes are calculated by using the biophysical parameter processing module in the SNAP software, including the leaf area index, the fraction of absorbed photosynthetically active radiation, the chlorophyll content in the leaf, the canopy water content and the fraction of vegetation cover in the study area. The mean value and the standard deviation of every index are analyzed for five counties in the upper reaches of Minjiang River. And the mean values and the standard deviations of three indexes are estimated for the vegetation growing and health situation of five counties, including the leaf area index, the fraction of absorbed photosynthetically active radiation, and the chlorophyll content in the leaf. The vegetation characteristics of the region are analyzed, combined with the scene classification map in the study area. The result provides a quick and efficient reference for the quantitative assessment of the regional ecological environment.