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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': Aus tralian E mpirical NDVI-clim ate) 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). [ABSTRACT FROM AUTHOR]

Measuring the spatiotemporal dynamics of lake and reservoir water storage is fundamental for assessing the influence of climate variability and anthropogenic activities on water quantity and quality. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only a few hundred lakes worldwide and cannot estimate absolute (i.e. total volume) water storage. We increased the number of measured lakes by a factor of 300 by using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry, in addition to radar altimetry from the Topex/Poseidon; Jason-1, Jason-2 and Jason-3; and Sentinel-3 and Sentinel-6 instruments. Historical time series (1984–2020) of water storage could be derived for more than 170 000 lakes globally with a surface area of at least 1 km 2 , representing 99 % of the total volume of all water stored in lakes and reservoirs globally. Specifically, absolute lake volumes are estimated based on topographic characteristics and lake properties that can be observed by remote sensing. In addition to that, we also generated relative lake volume changes solely based on satellite-derived heights and extents if both were available. Within this dataset, we investigated how many lakes can be measured in near real time (2020–current) in basins worldwide. We developed an automated workflow for near-real-time global lake monitoring of more than 27 000 lakes. The GloLakes historical and near-real-time lake storage dynamics data from 1984 to current are publicly available through 10.25914/K8ZF-6G46 (Hou et al., 2022c) and a web-based data explorer (http://www.globalwater.online , last access: 12 December 2023). [ABSTRACT FROM AUTHOR]

Measurements of the spatiotemporal dynamics of lake and reservoir water storage are fundamental in the assessment of the influence of climate variability and anthropogenic activities on water quantity and quality, as well as wetland ecology and the estimating greenhouse gas emissions from lakes. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only few hundreds of lakes worldwide. In this study, the number of measured lakes was increased by a factor 400 using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry in addition to radar altimetry from the Topex/Poseidon, Jason-1, -2 and -3, and Sentinel-3 instruments. Time series of relative (i.e., storage change) or absolute (i.e., total stored volume) storage for more than 170,000 lakes globally with a surface area of at least 1 km² (representing 99% of the total volume of all water stored in lakes and reservoirs globally) were retrieved. Within these, we were able to develop an automated workflow for near real-time global lake monitoring of more than 27,000 lakes. The historical and near real-time lake storage dynamics data for 1984 to current are publicly available through https://doi.org/10.25914/K8ZF-6G46 (Hou et al., 2022). [ABSTRACT FROM AUTHOR]