Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics
Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics
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DOI：
10.1016/j.rse.2009.04.011
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发表时间：
2009-09
影响因子：
13.5
通讯作者：
R. Zurita-Milla;G. Kaiser;J. Clevers;W. Schneider;M. Schaepman
中科院分区：
文献类型：
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作者：
R. Zurita-Milla;G. Kaiser;J. Clevers;W. Schneider;M. Schaepman
监测植被动态对于改进地球系统模型和增加我们对陆地碳循环以及生物圈和气候之间的相互作用的了解是基本的。中等空间分辨率传感器，如MERIS，由于其空间、光谱和时间分辨率，在大范围研究这些动力学方面显示出巨大的潜力。然而，MERIS提供的空间分辨率(在全分辨率模式下为300米)不适合于监测异质地貌，这些动态的典型长度尺度很少达到300米。因此，我们鼓励使用数据融合技术来将中等空间分辨率数据(MERIS全分辨率，FR)降至类似陆地卫星的空间分辨率(25米)。一种基于分解的数据融合方法被应用于在荷兰获取的MERIS FR图像的时间序列。选定的数据融合方法基于线性混合模型，并使用高空间分辨率的土地利用数据库来生成具有MERIS提供的光谱和时间分辨率的图像，但具有类似陆地卫星的空间分辨率。为了验证该方法的有效性，并对MERIS融合图像的辐射特性进行了评估，对融合图像的质量进行了定量评估。随后，将得到的一系列融合图像用于计算专门为MERIS设计的两个植被指数：MERIS陆地叶绿素指数(MTCI)和MERIS全球植被指数(MGVI)。这些指数代表了冠层叶绿素(MTCI)和冠层吸收的光合作用有效辐射(MGVI)的连续场。结果表明，所选择的数据融合方法可以成功地用于缩小MERIS数据的尺度，从而在类似陆地卫星的空间分辨率和类似MERIS的光谱和时间分辨率上监测植被动态。
Monitoring vegetation dynamics is fundamental for improving Earth system models and for increasing our understanding of the terrestrial carbon cycle and the interactions between biosphere and climate. Medium spatial resolution sensors, like MERIS, exhibit a significant potential to study these dynamics over large areas because of their spatial, spectral and temporal resolution. However, the spatial resolution provided by MERIS (300 m in full resolution mode) is not appropriate to monitor heterogeneous landscapes, where typical length scales of these dynamics rarely reach 300 m. We, therefore, motivate the use of data fusion techniques to downscale medium spatial resolution data (MERIS full resolution, FR) to a Landsat-like spatial resolution (25 m). An unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over The Netherlands. The selected data fusion approach is based on the linear mixing model and uses a high spatial resolution land use database to produce images having the spectral and temporal resolution as provided by MERIS, but a Landsat-like spatial resolution. A quantitative assessment of the quality of the fused images was done in order to test the validity of the proposed method and to evaluate the radiometric characteristics of the MERIS fused images. The resulting series of fused images was subsequently used to compute two vegetation indices specifically designed for MERIS: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). These indices represent continuous fields of canopy chlorophyll (MTCI) and of the fraction of photosynthetically active radiation absorbed by the canopy (MGVI). Results indicate that the selected data fusion approach can be successfully used to downscale MERIS data and, therefore, to monitor vegetation dynamics at Landsat-like spatial, and MERIS-like spectral and temporal resolution.