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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements
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An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements

机译：一种有效的MODIS NDVI时间序列数据重构算法及其基于地面测量的验证

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In this study, a compound technique was developed using eight denoising techniques for reconstructing high-quality normalized difference vegetation index (NDVI) time series data. The new algorithm consists of two major procedures: 1) detecting noisy data according to variation in the modification rates of eight selected denoising techniques and 2) using the medians of the denoised values of the eight techniques to replace the noisy data. The eight techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration (MVI) filter, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight (CW) filter, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. The technique was tested with moderate resolution imaging spectroradiometer (MODIS) NDVI time series data derived from MOD09GQ of the Heihe River Basin in China. In situ NDVI data were obtained during one nearly complete growing season for six land-use types in the study area. Analysis of the temporal and spatial characteristics of the reconstructed data revealed that the compound technique performs better than the other techniques. In addition, the lower root-mean-square error (RMSE) of the compound technique, which was calculated using ground measurements, demonstrated the improved performance of the new technique. The main advantage of the new technique is its ability to effectively denoise data and maintain fidelity such that it can be widely used for other NDVI time series data and for other study areas.
机译：在这项研究中，使用八种降噪技术开发了一种复合技术，用于重建高质量归一化植被指数（NDVI）时间序列数据。新算法包括两个主要过程：1）根据八种选定的降噪技术的修改率变化来检测噪声数据； 2）使用八种技术的降噪值的中位数来替换噪声数据。这八种技术包括改进的最佳指数斜率提取（M-BISE）技术，Savitzky-Golay（SG）技术，均值迭代（MVI）滤波器，非对称高斯（AG）技术，双逻辑（DL）技术，可变权重（CW）滤波器，数据重建插值（IDR）技术和Whittaker平滑器（WS）技术。该技术已使用中分辨率成像光谱仪（MODIS）NDVI时间序列数据进行了测试，该数据来自中国黑河流域的MOD09GQ。在研究区域的六个土地利用类型的近一个完整的生长季节中，获得了原位NDVI数据。对重建数据的时间和空间特征的分析表明，复合技术的性能优于其他技术。此外，使用地面测量值计算的复合技术的较低均方根误差（RMSE）证明了该新技术的改进性能。这项新技术的主要优点是它能够有效地对数据进行降噪和保持保真度，因此可以广泛用于其他NDVI时间序列数据和其他研究领域。

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