| 一种高时空分辨率NDVI数据集构建方法-STAVFM |
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| 引用本文： | 蒙继华.一种高时空分辨率NDVI数据集构建方法-STAVFM[J].遥感学报,2011,15(1). |
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| 作者姓名： | 蒙继华 |
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| 作者单位： | 中国科学院遥感应用研究所 |
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| 基金项目： | 国家自然科学基金项目（面上项目，重点项目，重大项目）;中国科学院重大资助项目 |
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| 摘 要： | ETM NDVI可以用来在30m的尺度上开展植被的监测，然而在Landsat卫星16天的重访周期和云污染等因素的影响下，常常会在相当长的一段时间内无法获取有效的ETM NDVI数据，给这一尺度下的植被动态监测带来了一定困难。相比之下，MODIS虽然在空间上只有250m分辨率的NDVI产品，却可以每天进行相同区域的监测。针对ETM空间分辨率高和MODIS时间分辨率高的特点，本研究选择实验区，基于对STARFM方法的改进，构建不同时空分辨率NDVI的时空融合模型-STAVFM，使用该模型对ETM NDVI与MODIS NDVI融合，构建了高时空分辨率NDVI数据集。研究结果表明，通过MODIS NDVI时间变化信息与ETM NDVI空间差异信息的有机结合，实现缺失高空间分辨率NDVI的有效预测（3景预测NDVI与实际NDVI的相关系数分别达到了0.82、0.90和0.91），从而构建高时空分辨率NDVI数据集。所构建的高时空分辨率NDVI数据集在时间上保留了高时间分辨率数据的时间变化趋势，空间上又反映了高空间分辨率数据的空间细节差异。
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| 关 键 词： | NDVI 数据融合 遥感 高时空分辨率 STAVFM |
| 修稿时间： | 4/15/2010 2:17:35 PM |
A Method to Construct High Spatial and Temporal resolution NDVI Data Set-STAVFM |
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| Meng Jihua.A Method to Construct High Spatial and Temporal resolution NDVI Data Set-STAVFM[J].Journal of Remote Sensing,2011,15(1). |
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| Authors: | Meng Jihua |
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| Institution: | Institute of Remote Sensing Applications |
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| Abstract: | While Landsat images have advantageous spatial characteristics for describing vegetation properties, the temporal resolution of the data is 16 days which will be easily extended due to cloud contamination. This lengthy data re-acquirement rate often results in a dearth of imagery for a desired time interval. In contrast, although MODIS (MODerate-resolution Imaging Spectroradiometer) can only achieve an spatial resolution of 250m in its NDVI product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution of Landsat and high temporal resolution of MODIS data, an 18km*18km study area in upper reaches of Guanting reservoir was selected. The new method (STAVFM) for blending NDVI of different spatial and temporal resolutions to produce high temporal-spatial resolution NDVI dataset was developed based on STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model). The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of ETM NDVI and predict the missed ETM NDVI with a high accuracy (the correlation coefficients of 3 pairs of observed and predicted ETM NDVI are 0.82, 0.90 and 0.91). A high temporal and high spatial resolution NDVI dataset was constructed, which maintain the temporal trend of high temporal resolution data and the detailed spatial difference information of high spatial resolution data. |
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| Keywords: | NDVI data fusion remote sensing high temporal and spatial resolution STAVFM |
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