A multisensor fusion approach to improve LAI time series
A multisensor fusion approach to improve LAI time series
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DOI：
10.1016/j.rse.2011.05.006
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发表时间：
2011-10-17
影响因子：
13.5
通讯作者：
Weiss, Marie
中科院分区：
文献类型：
--
作者：
Verger, Aleixandre;Baret, Frederic;Weiss, Marie
可靠的地面监测需要高质量和无间隙的卫星时间序列。中等分辨率传感器为监测陆地表面特征的空间和时间变化提供了全球范围的连续观测。然而，由于云、气溶胶、积雪、算法和仪器问题造成的低质量数据或数据缺失，遥感系统的全部潜力往往受到阻碍。为了提高现有卫星产品的时空连续性、一致性和精度，提出了一种多传感器融合方法。它基于神经网络、缺口填充和时间平滑技术的使用。它适用于任何光学传感器和卫星产品。在这项研究中，基于MODIS和植被反射率数据，论证了该技术在叶面积指数(LAI)产品中的潜力。融合产品与原始MODIS LAI产品总体上表现出良好的一致性，但丢失的LAI值减少了90%，改进了对植被动态的监测，时间平滑，与地面测量结果更好地吻合。(C)2011 Elsevier Inc.保留所有权利。
High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements. (C) 2011 Elsevier Inc. All rights reserved.