Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity in time, space or their combination, they typically require known and accurate data quality information. It is still challenging to effectively reconstruct high-quality NDVI from Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3g+), which is one of the longest observation records but lacks reliable data quality information. This study introduces an adaptive spatiotemporal tensor reconstruction algorithm that leverages the spatial and temporal patterns of vegetation to produce high-quality long-term NDVI datasets without the need of data quality information. The reconstruction process employs two different tensor completion models to satisfy the low-rank constraints. These two models can effectively remove the high-frequency noises originating from atmospheric contamination, while preserving the abrupt or low-frequency changes attributable to disturbances such as drought, even in the absence of data quality information. The resultant NDVI shows good consistency with observations from geostationary satellites. Regions that show a strong correlation (r > 0.7) with geostationary satellite NDVI increased from 46.7 % (original GIMMS-3g+) to 62.2 % and 62.3 % (two reconstructions results) for East Asia, and from 41.4 % to 58.0 % and 59.0 % for Amazon. Our method also demonstrates superior performance to traditional methods such as Whittaker, HANTS, SG-filter, and comparable performance with the state-of-the-art ST-Tensor method when the fraction of contaminated observations is low. The proposed method can also be applied to other datasets such as EVI, LAI, etc., without additional data quality inputs. The resultant vegetation index dataset has the potential to improve plant phenology retrievals and evaluation of ecosystem responses to extremes.

中文翻译：

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一种面向 GIMMS-3g+ NDVI 的自适应时空张量重建方法


卫星衍生的归一化植被指数 （NDVI） 不可避免地受到云和气溶胶的污染，给陆地生态系统的季节性和年际变化带来了很大的不确定性，并可能歪曲它们对气候变化和极端气候的响应。尽管已经开发了各种方法来利用时间、空间的相似性或其组合来重建 NDVI 时间序列，但它们通常需要已知且准确的数据质量信息。从全球库存建模与地图研究-第三代 V1.2 （GIMMS-3g+） 中有效重建高质量的 NDVI 仍然具有挑战性，该版本是最长的观测记录之一，但缺乏可靠的数据质量信息。本研究引入了一种自适应时空张量重建算法，该算法利用植被的空间和时间模式来生成高质量的长期 NDVI 数据集，而无需数据质量信息。重建过程采用两种不同的张量完成模型来满足低秩约束。这两个模型可以有效地去除来自大气污染的高频噪声，同时保留由于干旱等干扰引起的突然或低频变化，即使没有数据质量信息也是如此。由此产生的 NDVI 与对地静止卫星的观测结果具有良好的一致性。与对地静止卫星 NDVI 具有很强相关性的区域 （r > 0.7） 从东亚的 46.7 %（原始 GIMMS-3g+）增加到 62.2 % 和 62.3 %（两个重建结果），亚马逊从 41.4 % 增加到 58.0 % 和 59.0 %。 我们的方法还表现出优于 Whittaker、HANTS、SG-filter 等传统方法的性能，并且在受污染的观察分数较低时，其性能与最先进的 ST-Tensor 方法相当。所提出的方法也可以应用于其他数据集，如 EVI、LAI 等，而无需额外的数据质量输入。由此产生的植被指数数据集有可能改进植物物候检索和生态系统对极端事件响应的评估。