植被覆盖度遥感估算研究进展

植被覆盖度是刻画地表植被覆盖的重要参数,在全球变化研究、地表过程模拟和水文生态模型中发挥着重要作用.遥感能够反映不同空间尺度的植被覆盖信息及其变化趋势,是获取区域及全球植被覆盖度参数的一个重要手段.综合分析了用于植被覆盖度估算的遥感数据源,包括高光谱数据、多光谱数据、微波数据和激光雷达数据.而且分析了各种常用的植被覆盖度遥感估算方法及其优缺点,并评价了现有基于遥感数据的植被覆盖度产品及存在问题.最后,针对目前研究中存在的问题,讨论了植被覆盖度遥感估算研究的发展趋势,指出高时空分辨率长时间序列的全球植被覆盖度数据集、多源遥感数据融合和同化技术是未来植被覆盖度遥感估算研究的主要方向.

A Review on Fractional Vegetation Cover Estimation Using Remote Sensing

Fractional vegetation cover is a key parameter for characterizing land surface vegetation coverage, and plays an important role in global change research, earth surface processes simulation and hydro-ecological models. Remote sensing can provide the vegetation coverage information and variation trend on different spatial scales, and become the important means in obtaining the information ofregional or global fractional vegetation cover. In this paper, the commonly used remote sensing data sources including hyperspectral data, multispectral data, microwave data and LiDAR data for fractional vegetation cover estimation are analyzed, and multispectral data will be the long term main data source for fractional vegetation cover estimation because of the advantage of its easily acquisition, wide coverage and continuous observation advantages. Then, characteristics and advantages of different estiation methods are analyzed, which include the regression model, the pixel unmixing model, the machine learning method, the physical model, the spectral gradient difference and the forest canopy density mapping model. The dimidiate pixel model of the pixel unmixing model is extensively used for its simple form and certain physical significance, and the neural network method is widely used for generating products because of its extendibility and rapidity of calculation. Furthermore, the existing remote sensing data based fractional vegetation cover products are presented. However, the validation results indicate that almost all of the fractional vegetation cover products have underestimation problem and variational estimation accuracy in different regions. Finally, the fractional vegetation cover estimation research prospect is discussed and high spatial-temporal resolution global fractional vegetation cover dataset, multi-source remote sensing data fusion and assimilation method are the future development of fractional vegetation cover estimation using remote sensing data.