The Soil Moisture Integration and Prediction System (SMIPS) produces national extent daily estimates of volumetric soil moisture at a resolution of approximately 1km or 0.01 decimal degrees. SMIPS also generates an index of between 0-1 which approximates how full the 90cm metre soil moisture store is at a particular location and time. The SMIPS model itself consists of two linked soil moisture stores, a shallow quick responding 10cm upper store and a deeper, slower responding 80cm store. SMIPS is parameterised using physical properties from the <a href ='https://www.clw.csiro.au/aclep/soilandlandscapegrid/'>Soil and Landscape Grid of Australia </a>and takes a data model fusion approach for model forcing. Version 1.0 of the SMIPS model uses precipitation and potential evapotranspiration data from the Bureau of Meteorology’s <a href="http://www.bom.gov.au/water/landscape/assets/static/publications/AWRALv6_Model_Description_Report.pdf">AWRA Model</a>. In addition to version 1.0 of the model, an experimental version of the model is available for user testing. This version of the model uses precipitation data supplied by an experimental CSIRO daily rainfall surface generated using spatial data from the NASA Global Precipitation Mission as a base and enhanced using rainfall observations from the Bureau of Meteorology (BoM) rainfall gauge network, and various landscape covariates, processed using a machine learning approach. <br> To help increase model accuracy, the internal SMIPS model states are adjusted or ‘bumped’ by daily observational data from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite mission.

<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).