The soil in terrestrial and blue carbon ecosystems (BCE; mangroves, tidal marshes, seagrasses) is a significant carbon (C) sink. National assessments of C inventories are needed to protect them and aid nature-based strategies to sequester atmospheric carbon dioxide. We harmonised measurements from Australia's terrestrial and BCE and, using consistent multi-scale spatial machine learning, unravelled the drivers of soil organic carbon (SOC) variation and digitally mapped their stocks. The modelling shows that climate and vegetation are continentally the primary drivers of SOC variation. But the underlying regional drivers are ecosystem type, terrain, clay content, mineralogy, and nutrients. The digital soil maps indicate that in the 0-30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6-39.0 Gt), and BCE 0.35 Gt (0.20-0.62 Gt). Tall open eucalypt and mangrove forests have the largest mean SOC per unit area. Eucalypt woodlands and hummock grassland, which occupy vast areas, store the largest total SOC stock. These ecosystems constitute important regions for conservation, emissions avoidance, and preservation because they also provide additional co-benefits.

<p>Soil is a huge carbon (C) reservoir, but where and how much extra C can be stored is unknown. Here, using 5089 observations, we estimated that the uppermost 30&nbsp;cm of Australian soil holds 13&nbsp;Gt (10–18&nbsp;Gt) of mineral-associated organic carbon (MAOC). Using a frontier line analyses, described in Viscarra Rossel et al. (2023), we estimated the maximum amounts of MAOC that Australian soils could store in their current environments, and calculated the MAOC deficit, or C sequestration potential. We propagated the uncertainties from the frontier fitting and mapped the estimates of these values over Australia using machine learning and kriging with external drift (KED). The maps show regions where the soil is more in MAOC deficit and has greater sequestration potential. The modelling shows that the variation over the whole continent is determined mainly by climate, linked to vegetation, and soil mineralogy. We find that the MAOC deficit in Australian soil is 40&nbsp;Gt (25–60&nbsp;Gt). The deficit in the vast rangelands is 20.84&nbsp;Gt (13.97–29.70&nbsp;Gt) and the deficit in cropping soil is 1.63&nbsp;Gt (1.12–2.32&nbsp;Gt). Our findings suggest that the C sequestration potential of Australian soil is limited by climate.

The Sentinel-2 seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the Sentinel-2 seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10&nbsp;m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, the persistent green product has only been produced at 30&nbsp;m pixel resolution based on Landsat imagery, resulting in this Sentinel-2 seasonal ground cover product having a medium 30&nbsp;m pixel resolution also. This is an experimental product which has not been fully validated. This product is similar to the <a href="https://portal.tern.org.au/metadata/23884 ">Seasonal ground cover - Landsat, JRSRP algorithm Version 3.0, Australia Coverage</a> which is based on a different satellite sensor.

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.

We used Digital Soil Mapping (DSM) technologies combined with the real-time collations of soil attribute data from TERN's recently developed Soil Data Federation System, to produce a map of Australian Soil Classification Soil Order classes with quantified estimates of mapping reliability at a 90m resolution.

1. Restoration of degraded landscapes has become increasingly important for conservation of species and their habitats owing to habitat destruction and rapid environmental change. An increasing focus for restoration activity are old-fields as agricultural land abandonment has expanded in the developed world. Studies examining outcomes of ecological restoration predominantly focus on vegetation structure and plant diversity, and sometimes vertebrate fauna. Fewer studies have systematically investigated effects of restoration efforts on soil chemical and biophysical condition or ground-dwelling invertebrates and there is limited synthesis of these data. 2. This dataset comprised data for a global meta-analysis of published studies to assess the effects on soil properties and invertebrates of restoring land that was previously used for agriculture. Studies were included if the site had been either cropped or grazed, restoration was either active (planting) or passive (abandonment, fencing) and if adequate data on soil chemical or physical properties or invertebrate assemblages were reported for restored, control (cropped/grazed) or reference sites. 3. The dataset includes 42 studies, published between 1994 and 2019 that met the inclusion criteria, covering 16 countries across all continents. More studies assessed passive restoration approaches than active planting, and native species were more commonly planted than exotic species.

The monthly blended ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30&nbsp;m per-pixel) for each calendar month. It is derived directly from both the Landsat-based fractional cover product and the Sentinel-2-based fractional cover product by Queensland's Remote Sensing Centre. A 3 band (byte) image is produced: band 1 - bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 - non-green vegetation fraction (in percent). The no data value is 255. This product is derived from the <a href="https://portal.tern.org.au/metadata/TERN/8d3c8b36-b4f1-420f-a3f4-824ab70fb367 ">Monthly blended fractional cover - Landsat and Sentinel-2, JRSRP algorithm Version 3.0, Queensland coverage</a>

This dataset contains soil microbial and genomic analysis files of 9 soil samples from each of three plots at Fletcherview, Northern Queensland (NQ) processed by the <a href='https://agrf.org/'>Australian Genome Research Facility Ltd (AGRF) </a>. The files are available as compressed FastQ formatted sequence files.<br> For the nine Far North Queensland (FNQ) new plots (3 plots in Fletcherview and six plots at Wambiana), soil sampling additional to that done as component of plot installation by TERN have been undertaken. This is aligned with potential future exploratory work on soil eDNA proposed for WA. The protocol is a modified version of the <a href="https://doi.org/10.1186/s13742-016-0126-5">BASE sampling protocol</a>, combined with soil sampling as per <a href="https://www.tern.org.au/wp-content/uploads/TERN-Rangelands-Survey-Protocols-Manual_web.pdf">White et al. (2012)</a>. <br> DNA extracted from the soil samples and Metagenomics 10Gbp (giga base pairs) bundle as per AGRF protocol.

Long term temporal statistic products derived from the seasonal ground cover product for each fraction. There is one raster image for each season and each bare and green fraction for the full time series of imagery available. Statistics include: band 1 – 5th percentile minimum; band 2 – mean value for pixel over full time series for that season only (percentage + 100); band 3 – median value for pixel over full time series for that season only (percentage + 100); band 4 – 95th percentile maximum; band 5 – Standard deviation - the temporal standard deviation of the full time-series for that season only; band 6 – Count - the number of observations statistics for that pixel are based on for that season only. Min/max (5th and 95th percentile) products are also made for each fraction using all seasonal ground cover images available during the long term data period (currently 1990-2020)