Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global AVHRR NDVI products for their suitability for long-term vegetation monitoring in Australia. Comparisons with MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce inter-annual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA’s Climate Data Record AVHRR NDVI to MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (‘AusENDVI-clim’: Australian Empirical NDVI-climate) and another independent of climate data (‘AusENDVI-noclim’). These datasets, spanning 1982–2013 at a spatial resolution of 0.05°, exhibit strong correlation and low relative errors compared to MODIS NDVI, accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO₂ concentration, and woody cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with observations. Finally, we provide a complete 41-year dataset where gap filled AusENDVI from January 1982 to February 2000 is seamlessly joined with MODIS NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia’s annual maximum NDVI revealed increasing values across most of the continent. Moreover, shifts in the timing of annual peak NDVI are identified, underscoring the dataset's potential to address crucial questions regarding changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on terrestrial carbon and water cycles, and provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802704 (Burton, 2024).

中文翻译：

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加强澳大利亚的长期植被监测：协调和填补 AVHRR 和 MODIS NDVI 差距的新方法

摘要。基于卫星的植被状况的长期、可靠数据集对于了解陆地生态系统对全球环境变化的响应至关重要，特别是在以生态系统多样化和气候年际变化剧烈为特征的澳大利亚。我们全面评估了几种现有的全球 AVHRR NDVI 产品对于澳大利亚长期植被监测的适用性。与 MODIS NDVI 的比较凸显了显着的缺陷，特别是在植被茂密的地区。此外，所有评估产品都未能充分再现 MODIS 时代之前的年际变化，如 Landsat NDVI 异常所示。为了解决这些限制，我们提出了一种新方法，使用梯度提升决策树集成方法，将 NOAA 的气候数据记录 AVHRR NDVI 校准和协调澳大利亚的 MODIS MCD43A4 NDVI。开发了两个版本的数据集，一个将气候数据纳入预测变量（“AusENDVI-clim”：澳大利亚经验NDVI-气候），另一个版本独立于气候数据（“AusENDVI-noclim”）。这些数据集跨越 1982 年至 2013 年，空间分辨率为 0.05°，与 MODIS NDVI 相比，表现出很强的相关性和较低的相对误差，准确地再现了植被茂密地区的季节周期。此外，它们紧密地复制了 MODIS 时代之前植被状况的年际变化。还开发了一种利用气候、大气 CO ₂浓度和木质覆盖率预测因子来填补 AusENDVI 记录空白的可靠方法。由此产生的合成 NDVI 数据集与观测结果显示出极好的一致性。最后，我们提供了一个完整的 41 年数据集，其中从 1982 年 1 月到 2000 年 2 月的间隙填充 AusENDVI 与从 2000 年 3 月到 2022 年 12 月的 MODIS NDVI 无缝连接。分析澳大利亚年度最大 NDVI 的 40 年每像素趋势揭示了各个区域的值不断增加大陆的大部分地区。此外，还确定了年度 NDVI 峰值时间的变化，强调了该数据集解决有关植被物候变化及其驱动因素的关键问题的潜力。 AusENDVI数据集可用于研究澳大利亚植被动态变化及其下游对陆地碳水循环的影响，为进一步研究植被变化的驱动因素提供可靠的基础。 AusENDVI 是开放获取的，可在https://doi.org/10.5281/zenodo.10802704上获取（Burton，2024）。