Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling
Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling
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DOI：
10.1016/j.rse.2011.01.001
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发表时间：
2011-05
影响因子：
13.5
通讯作者：
Hua Yuan;Yongjiu Dai;Zhiqiang Xiao;D. Ji;Shangguan Wei
中科院分区：
文献类型：
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作者：
Hua Yuan;Yongjiu Dai;Zhiqiang Xiao;D. Ji;Shangguan Wei
陆地表面和气候建模需要连续和一致的叶面积指数（LAI）。高时空分辨率和长时间记录的数据是现在和未来的需求。MODIS LAI产品在一定程度上满足了这些要求。然而，由于云和季节性积雪的存在、仪器的问题以及反演算法的不确定性，现有的MODIS叶面积指数产品在时空上存在不连续性和不一致性，限制了其在陆面和气候模拟中的应用。为了在全球范围内改进MODIS LAI产品，针对MODIS LAI数据的特点，充分利用质量控制（QC）信息，提出了一种综合的两步法，以有效地在全球范围内获得改进的MODIS LAI产品。首先，我们使用了改进的时空滤波（mTSF）方法，利用背景值和QC信息在每个像素做一个简单的数据同化相对较低的质量数据。然后，我们应用后处理TIMESAT（一个软件包来分析卫星传感器数据的时间序列）Savitzky-Golay（SG）滤波器得到最终的结果。利用10年的MODIS Collection 5叶面积指数数据对该方法进行了验证。与LAI参考图和MODIS LAI数据相比，改进后的MODIS LAI数据在量级上更接近LAI参考图，在时间序列和空间域上更具有连续性和一致性。此外，简单的统计方法被用来评估改进的MODIS LAI和MODIS LAI之间的差异。
Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky–Golay (SG) filter to get the final result. We implemented the method to 10years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.