通过时空张量完成的易云区域长时间序列NDVI重建
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion
摘要：
归一化植被指数（NDVI）时间序列数据的应用不可避免地受到云致间隙和噪声的影响。虽然已经开发了许多重建方法，但由于没有充分利用空间和时间相关性，它们没有有效地解决与多云和多雨区域的时间序列中的大间隙相关的问题。本文提出了一种自适应时空张量完备方法（ST-Tensor），充分利用多尺度时空信息，重建多云地区的长期NDVI时间序列。为此，通过考虑空间邻居之间的相关性，年际变化和周期性特征，建立一个高度相关的张量，以重建丢失的信息通过自适应加权低秩张量完成模型。然后实施迭代l1趋势滤波方法以消除残余时间噪声。使用2001年至2018年在东南亚大陆地区获得的MODIS 16天复合NDVI产品对这种新方法进行了测试，该地区的多雨气候通常会导致数据中的大间隙和噪声。定性和定量的结果表明，ST张量方法是更有效的解决不同的缺失数据问题，特别是时间连续的差距和时空连续的差距比以前的五种方法。它还表明，ST张量方法比其他方法在跟踪NDVI的季节性轨迹，因此是一个更好的选择，为云多发地区生成高质量的长期NDVI时间序列。
英文摘要：
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial and temporal correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative l1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in the region of Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
