Soil is a huge carbon (C) reservoir, but where and how much extra C can be stored is unknown. Current methods to estimate the maximum amount of mineral‐associated organic carbon (MAOC) stabilized in the fine fraction (clay + silt, ) fit through the MAOC versus clay + silt relationship, not their maxima, making their estimates more uncertain and unreliable. We need a function that ‘envelopes’ that relationship. Here, using 5089 observations, we estimated that the uppermost 30 cm of Australian soil holds 13 Gt (10–18 Gt) of MAOC. We then fitted frontier lines, by soil type, to the relationship between MAOC and the percentage of clay + silt to estimate 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 line fitting and mapped the estimates of these values over Australia using machine learning and kriging with external drift. 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 Gt (25–60 Gt). The deficit in the vast rangelands is 20.84 Gt (13.97–29.70 Gt) and the deficit in cropping soil is 1.63 Gt (1.12–2.32 Gt). Management could increase C sequestration in these regions if the climate allowed it. Our findings provide new information on the C sequestration potential of Australian soils and highlight priority regions for soil management. Australia could benefit environmentally, socially and economically by unlocking even a tiny portion of its soil's C sequestration potential.

The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6–39.0 Gt), and blue carbon ecosystems 0.35 Gt (0.20–0.62 Gt). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to the vast areas they occupy. Our findings suggest these are essential ecosystems for conservation, preservation, emissions avoidance, and climate change mitigation because of the additional co-benefits they provide.

Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental-scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. The method relies on the development of spectrotransfer functions with state-of-the-art machine learning and uses publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared (vis–NIR) wavelengths, to estimate the relative abundances of Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index. The algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), extreme gradient boosting (XGBoost) and one-dimensional convolutional neural networks (1D-CNNs). The spectrotransfer functions were validated with a 10-fold cross-validation (n=577). The 1D-CNNs outperformed the other algorithms and could explain between 45 % and 73 % of fungal relative abundance and diversity. The models were interpretable, and showed that soil nutrients, pH, bulk density, ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R²≥0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R² values of 0.5 and 0.58 respectively. The spectrotransfer functions for the Glomeromycota and diversity were the poorest with R² values of 0.48 and 0.45 respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectrotransfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and diversity under different agronomic and ecological settings.

Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical indicators of soil health. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, accurate, and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMSs) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations, and a full-range vis–NIR spectrometer for modelling 29 soil physical, chemical, and biological properties used to assess soil health at mine sites. We collected topsoil samples from reference, undisturbed native vegetation, and stockpiles from seven mines in Western Australia. We evaluated the spectrometers' repeatability and the accuracy of spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate sand, silt, and clay with similar or better accuracy than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin's concordance correlation, ρ_c≥0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. The miniaturised NIR spectrometers produced comparably accurate soil property estimates to the full-range portable system which is an order of magnitude more expensive, particularly when combined with the visible range sensor. Thus, the miniaturised spectrometers could form the basis for a rapid, cost-effective soil diagnostic capacity to support mine site rehabilitation and deliver significant positive economic and environmental outcomes.

Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical soil health indicators. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, relatively accurate and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMS) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations and a full-range vis–NIR spectrometer to model 29 soil physical, chemical and biological properties used to assess soil health at mine sites. We collected soils from reference undisturbed native vegetation and topsoil stockpiles from seven mines in Western Australia. We evaluated the repeatability of the spectrometers and the accuracy of the spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate soil texture (sand, silt, and clay) more accurately than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin’s concordance correlation, p_c ≥ 0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. Our results show that the miniaturised NIR spectrometers can produce accurate predictions of soil properties comparable to the (orders of magnitude) more expensive full-range portable system, particularly when combined with a visible range spectrometer. Thus, there is potential to develop rapid, accurate, cost-effective diagnostic capacity to support mine site rehabilitation based on miniaturised spectrometers and deliver significant positive economic and environmental outcomes.