This geostationary land surface temperature (LST) collection was retrieved using Himawari/AHI observations and calibrated against MODerate-resolution Imaging Spectroradiometer (MODIS) best-quality retrievals for Australia. It was developed under an academic collaboration between the Australian National University (ANU) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). It has a spatial resolution of 2 km and temporal frequency of 10 min, and has been periodically updated since its inception in July 2015. This record has a temporal length of 8.5 years (i.e., Jul 2015 - Dec 2023) and the subsequent updates will be published annually.

This product provides locations of areas affected by fire including the approximate day of burning. Inputs are daily day time observations from MODIS sensors on Terra and Aqua. Observations are atmospherically corrected and the resulting time series is investigated for sudden changes in reflectance, persistent over multiple days. Variations in observation and illumination geometry are taken into account through application of a kernel driven Bidirectional Reflectance Distribution Function (BRDF) model.

The MODIS Land Condition Index (LCI) is an index of total vegetation cover (green and non-photosynthetic vegetation ), and so is also an index of soil exposure. The LCI is a normalised difference index based on MODIS bands in the mid-infrared portion of the spectrum. The index is produced from 500-m MODIS nadir BRDF adjusted reflectance (NBAR) data. As with all products derived from passive remote sensing imagery, this product represents the world as seen from above. Therefore, the cover recorded by this product represent what would be observed from a birds-eye-view. Therefore, dense canopy may prevent observation of significant soil exposure.

RSMA measures change in the relative contributions of photosynthetic vegetation (PV, or GV green vegetation), non-photosynthetic vegetation (NPV) and soil reflectance compared to a baseline date. These spectral changes correspond to changes in fractional cover relative to the baseline date. Full details on the RSMA method are presented in Okin (2007). One of the key advantages of the RSMA, its insensitivity to changes in soil spectra, is a result of the fact that it does not require us to know the soil reflectance profile for a region. This strength is also the cause of a major weakness in RSMA. Since the measure is relative to a baseline date, and the absolute cover levels for every pixel are unknown at the baseline, the RSMA does not convey the absolute cover levels at any other point in time. However, if the absolute cover levels are known at any point in time, it is theoretically possible to convert the RSMA to absolute relative spectral mixture analysis (ARSMA).<br> As with all products derived from passive remote sensing imagery, this product represents the world as seen from above. Therefore, the cover recorded by this product represent what would be observed from a bird's-eye-view. Therefore, dense canopy may prevent observation of significant soil exposure.

Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series. Monthly: The monthly product is aggregated from the 8-day composites using the medoid method. Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series. MODIS fractional cover has been validated for Australia.

<p>This dataset provides accurate, high-resolution (30 m) / high-frequency (monthly) / continuous (no gaps due to cloud) actual evapotranspiration (AET) for Australia using the CMRSET algorithm. The CMRSET algorithm uses reflective remotely sensed indices to estimate AET from potential evapotranspiration (PET; calculated using daily gridded meteorological data generated by the Bureau of Meteorology). Blending high-resolution / low-frequency AET estimates (e.g., Landsat and Sentinel-2) with low-resolution / high-frequency AET estimates (e.g., MODIS and VIIRS) results in AET data that are high-resolution / high-frequency / continuous (no gaps due to cloud) and accurate. These are all ideal characteristics when calculating the water balance for a wetland, paddock, river reach, irrigation area, landscape or catchment. </p><p> Accurate AET information is important for irrigation, food security and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation and better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots", and knowing their water needs allows for enhanced management of these critical areas in the landscape. Having high-resolution, frequent and accurate AET estimates for all of Australia means this AET data source can be used to model the water balance for any catchment / groundwater system in Australia. </p><p> Details of the CMRSET algorithm and its independent validation are provided in Guerschman, J.P., McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Peña-Arancibia, J.L. and Chen, Y. (2022) Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data. Journal of Hydrology. 605, 127318, doi:10.1016/j.jhydrol.2021.127318</p> <p> <i>We strongly recommend users to use the TERN CMRSET AET V2.2</i>. Details of the TERN CMRSET AET V2.2 data product generation are provided in McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Guerschman, J.P., Peña-Arancibia, J.L. and Stenson, M.P. (2022) Generating a multi-decade gap-free high-resolution monthly actual evapotranspiration dataset for Australia using Landsat, MODIS and VIIRS data in the Google Earth Engine platform: Development and use cases. Journal of Hydrology (In Preparation).