Peatlands are globally significant ecosystems, influencing carbon and water cycles at local, regional and planetary scales, as well as harbouring a uniquely adapted biodiverse flora and fauna. However, peatlands in Oceania have received relatively little research or policy attention. This review aimed to document existing knowledge of peatlands in Oceania and identify gaps and opportunities to enhance their management to contribute to national and global outcomes for biodiversity conservation and carbon sequestration. We reviewed and synthesised existing published scientific literature, grey literature accessed via professional affiliations and, where written material was missing or sparse, shared and documented expert practitioner and professional experiences in peatlands across the Oceania region. We found that Oceania contains a diversity of peatlands spread across all five ecological zones from the tropics to temperate environments and from the lowlands up to mountain and alpine regions. There is a notable dominance of the Restionaceae family in the formation of peatlands in Oceania, in contrast to other regions where moss and tree species are recognised as the most important peat forming species. The knowledge gaps uncovered in this review are substantial. There is little documentation regarding peatlands of Pacific Island nations. Mapping is incomplete or out of date in many parts of Oceania and detailed understanding of ecohydrological function is rare in this region. The findings of this review highlight five opportunities nascent within Oceania's peatlands: (1) local and global carbon, biodiversity and water regulation benefits via restoration and protection; (2) partnerships between Indigenous and community groups and government; (3) South to South regional collaboration; and (4) global knowledge sharing including (5) support to implement existing international policies and agreements. Recommendations across the domains of research, management and policy outline seven key actions that could contribute towards realising the full potential of Oceania's peatlands, both in terms of their intrinsic value and ‘Natures Contributions to People’.

Peatlands, occupying merely 5% of the Earth's land surface, are an important carbon sink, storing up to double the carbon of the world's forests. The quantification of global peatlands carbon stock and their spatial distribution, however, poses a significant challenge due to their heterogeneous nature and the complex hydro-ecological processes that govern their formation. Using the Global Peatland Map (GPM 2.0), this study employed a digital soil mapping approach to predict peat thickness, and multilayer bulk density (BD) and carbon content (CC) globally. We applied the Quantile Random Forest (QRF) algorithm, informed by land surface data (soil, climate, organisms, and topography), to develop regional models for peat thickness and global models for BD and CC. Peat thickness models, based on approximately 27,000 data points, demonstrated good predictive performance, with the highest accuracy observed in African peatlands (validation R-2 = 0.61). In contrast, BD (similar to 19,000 points) and CC (similar to 9,000 points) models showed more variable performance across different soil layers (average R-2 = 0.45 and R-2 = 0.22, respectively). Feature importance analysis indicated that elevation and climate were key predictors, particularly in Latin America and South-Southeast Asia. Applying the models to 1 km resolution covariates across the world, our predicted peat thickness map aligned well with existing high-resolution regional maps. By incorporating error propagation rules, we estimated the global peatlands carbon stock to be 942 +/- 312 Pg C over an area of 6.75 million km(2). Our results, including detailed maps, are available to facilitate further global peatland analyses and modelling endeavours.

Soil moisture, an essential parameter for hydroclimatic studies, exhibits considerable spatial and temporal variability, which complicates its mapping at high spatiotemporal resolutions. Although current remote sensing products offer global estimates of soil moisture at fine temporal resolutions, they do so at a coarse spatial resolution. Deep learning (DL) techniques have recently been employed to produce high-resolution maps of various soil properties; however, these methods require substantial training data. This study sought to map daily soil moisture across Tasmania, Australia, at an 80 m resolution using a limited set of training data. We assessed three modeling strategies: DL models calibrated using an Australian dataset (51 411 observation points), models calibrated using the Tasmanian dataset (9825 observation points), and a transfer learning technique that transferred information from the Australian models to Tasmania using region-specific data. We also evaluated two DL approaches, i.e., multilayer perceptron (MLP) and long short-term memory (LSTM). The models included the Soil Moisture Active Passive (SMAP) dataset, weather data, an elevation map, land cover, and multilevel soil property maps as inputs to generate soil moisture at the surface (0–30 cm) and subsurface (30–60 cm) layers. Results showed that (1) models calibrated from the Australian dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using local data, resulted in shortcomings in predicting soil moisture; and (3) transfer learning exhibited remarkable performance improvements (error reductions of up to 45 % and a 50 % increase in correlation) and resolved the drawbacks of the two previous models. The LSTM models with transfer learning had the highest overall performance with an average mean absolute error (MAE) of 0.07 m3 m−3 and a correlation coefficient (r) of 0.77 across stations for the surface layer as well as MAE=0.07m3m-3 and r=0.69 for the subsurface layer. The fine-resolution soil moisture maps captured the detailed landscape variation as well as temporal variation according to four distinct seasons in Tasmania. The models were then applied to generate daily soil moisture maps of Tasmania, integrated into a near-real-time monitoring system to assist agricultural decision-making.

Soil moisture has various critical roles in agricultural and environmental processes, yet monitoring its real-time spatial variability across a large area is challenging. Here, we assimilated soil moisture models and near-real-time datasets to generate daily soil moisture content prediction across Tasmania at an 80-m resolution. The maps have been embedded in a website for land monitoring since September 2023, with a fully automated mapping procedure to update the published maps within a day. A testing period in September-November 2023 showed that the predicted moisture maps performed well (daily correlation coefficient values varied between 0.60 to 0.75) against 50 observation stations across Tasmania.

Abstract. Peatlands, which only cover 3 to 5 percent of the global land area, can store up to twice the amount of carbon as the world’s forests. Although recognised for their significant role in the global carbon cycle, discovering the global extent of peatlands and their carbon stock remains challenging. Referring to the UNEP's global peatland map, here we present PEATGRIDS, a data product containing global maps of peat thickness and carbon stock created created using the digital soil mapping approach. We compiled over 25,000 observations of peatland thickness, bulk density (BD) and carbon content (CC), globally. Using the Random Forest (RF) algorithm, we estimated peat thickness and peat BD and CC at ~1 km resolution at multiple depths (0–2 m) globally. The estimates were generated using 19 land surface covariates from digital maps and remote sensing images of land use, soil characteristics, topographical features, and climate parameters. The RF models for peat thickness were trained on 25,200 points grouped into six geographic regions. Validation of the peat thickness estimates showed a good performance, with the coefficient of determination (R2) ranging from 0.15 to 0.72. The prediction for peat BD and CC followed the same model architecture and were trained on 17,000 and 7,000 points, respectively. Overall, BD and CC models performed well and consistently across soil layers with average R2 values of 0.61 for BD and 0.48 for CC. Based on the estimated peat thickness, BD and CC, the carbon stock of global peatland was estimated to be 1,029 Pg C for peat dominated area of 6.57 million km2. PEATGRIDS is made available at https://doi.org/10.5281/zenodo.12559239 (Widyastuti et al., 2024) to support further analyses and modelling of peatlands across the globe.

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.

Abstract. Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here, we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualitative comparisons to other maps in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r2 of 0.73 with a root mean squared error and mean bias error of 9.11 % and −0.36 %, respectively. Our second error estimate was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping products generated through more traditional approaches, at least for boreal peatlands.

Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (DY and DIWR for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha- 1) and IWR (137 mm). DY variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.

Digital Soil Mapping and Assessment (DSMA) has progressed from challenging traditional soil science paradigms, through small scale prototyping, to large-scale implementation capturing quantitative measures of soil attributes and functions. This paper considers the future for DSMA in the context of a highly uncertain world where high-quality knowledge of soil dynamics will be important for responding to the challenges of sustainability. Irrespective of whether the need is for survival, increased productivity or broadening the services provided from land management, or simply securing the soil itself, we see DSMA as a fundamental approach and essential tool. With a broadening need and a strong foundation in the practice of DSMA now in place, the theory, tools and technology of DSMA will grow significantly. We explore expected changes in covariate data, the modelling process, the nature of base data generation and product delivery that will lead to tracking and forecasting a much wider range of soil attributes and functions at finer spatial and temporal resolutions over larger areas, particularly globally. Equally importantly, we expect the application and impact of DSMA to broaden and be used, directly and collaterally, in the analysis of land management issues in coming decades. It has the capacity to provide the background to a soil and landscape ‘digital twin’ and the consequent transformation in monitoring and forecasting the impacts of land management practices. We envision the continued growth of DSMA skills amongst soil scientists and a much broader community of practice involved in developing and utilizing DSMA products and tools. Consequently, there will be a widening and deepening role of public-private partnerships in this development and application.

Retrospective evaluation has consistently shown that soil information has value beyond the investment used to produce it. Digital soil mapping and assessment (DSMA) is the new paradigm for soil survey and a key source of soil and land information. It promises increased utility and flexibility for the users of soil information. Does DSMA methodology add value? What are some of the outcomes and emerging impacts? Seven examples from the burgeoning use of DSMA in and near Australia have been explored to determine the nature and extent of outcomes and impact achieved. The analysis began with a workshop of key soil scientists, involved a survey of the use of DSMA and attitudes to impact amongst practitioners of DSMA and looked at each of the seven examples in the context of the systems they seek to influence. There is evidence of progress along impact pathways in each case. In the simpler systems, the products of DSMA are being used as envisaged and change is occurring. In more complex systems, the role of soil information meshes with many other influences and impact is harder to discern. Importantly, we find that few practitioners using DSMA explicitly identify impact pathways and design projects at the outset to optimise the chance of more extensive impact. Thus, an approach to planning for impact in DSMA is proposed that could improve the chance of impact and allow for iteration as our understanding of the systems in which change is expected improves through our interaction with them.

Peatland can be found throughout Australia from the wet tropics in the north to the temperate zone, the alpine regions in the south east to the coastal plains in the south west. However, the extent of peatland in Australia mainland is relatively small and receives little attention. The most studied peats in Australia are those found in Tasmania but there has been limited detailed mapping of peat formation in Tasmania, with field data scarce due to the access constraints inherent in the inhospitable south-west wilderness environments. Digital Soil Mapping of Organic Soil Carbon (SOC) throughout Tasmania has been done,and using 18% topsoil SOC content as a lower limit for peat soil, a map of peatland has been derived. Future work will improve the estimated coverage of carbon mapping and peat soils in Tasmania and Australia.

Surface air temperature ( T a ) is important for a wide variety of applications that require real-time monitoring of environmental change; yet, few studies have attempted to deliver such products at a resolution appropriate for local-scale analysis. We investigated the feasibility of producing hourly T a maps in near real time at a spatial grid resolution of 80 m across the state of Tasmania (area: 68,401 km 2 ), Australia. We first assimilate 267 non-telemetered logger recording sites for data recorded in the 1-year period from September 2013 to 2014 and statistically calibrate them to 43-telemetered Australian Bureau of Meteorology (BoM) weather station sites for real-time application. This was evaluated in the following year using held back recordings in addition to evaluating regression trees (RT) and thin plate splines (TPS) to interpolate the hourly T a estimates. For real-time operational mapping, the system was fully automated in the R programming language and hosted on a cloud-based computing platform to assess performance over a 7-day period in February 2020. The calibration procedure yielded accurate results with the root mean square error (RMSE) ranging between 1.33 °C in summer and 1.29 °C in winter. The TPS interpolation method was optimal in summer with an RMSE of 1.35 °C compared to RT with 1.39 °C; however, RT performed better in winter with an RMSE of 1.34 °C compared to 1.42 °C. The mapping system was capable of producing spatial outputs within the hour of the BoM observations becoming available with the TPS interpolation proving to be more efficient at producing outputs in a timelier manner.

Australia has advanced the science and application of Digital Soil Mapping (DSM). Over the past decade, DSM in Australia has evolved from being purely research focused to become ‘operational’, where it is embedded into many soil-agency land resource assessment programs around the country. This has resulted from a series of ‘drivers’, such as an increased need for better quality and more complete soil information, and ‘enablers’, such as existing soil information systems, covariate development, serendipitous project funding, collaborations, and Australian DSM ‘champions’. However, these accomplishments were not met without some barriers along the way, such as a need to demonstrate and prove the science to the soil science community, and rapidly enable the various soil agencies' capacity to implement DSM. The long history of soil mapping in Australia has influenced the evolution and culmination of the operational DSM procedures, products and infrastructure in widespread use today, which is highlighted by several recent and significant Australian operational DSM case-studies at various extents. A set of operational DSM ‘workflows’ and ‘lessons learnt’ have also emerged from Australian DSM applications, which may provide some useful information and templates for other countries hoping to fast-track their own operational DSM capacity. However, some persistent themes were identified, such as applicable scale, and communicating uncertainty and map quality to end-users, which will need further development to progress operational DSM.

Input data aggregation influences crop model estimates at the regional level (Eyshi Rezaei et al., 2015). Previous studies have focused on the impact of aggregating the climate data used to compute crop yields (Hoffmann et al., 2015; van Bussel et al., 2011; Zhao et al., 2015). Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolution input data on model outputs (irrigated and rainfed yield and irrigation water demand [IWD]) in Tasmania, Australia by (i) separately assessing the DAEc and DAEs of model input data and (ii) assessing the combined impact of DAEc and DAEs. We provide a framework to quantifying the input uncertainty introduced by using aggregated data to meet the objectives of modelling exercises.

Species composition is limiting production in >65% of pastures in Tasmania, Australia—a situation not unique to Tasmania. There are many reasons for degradation and poor persistence of improved pastures, with species selection crucial. Selection currently relies on producers making an assessment based on experience, external advice from agronomists and seed merchants, and experimental trial data. This project sought to assess the benefit of using fine-scale soils data and long-term rainfall data to determine the suitability of pasture species at a farm level across >3 Mha of agricultural land in Tasmania. Suitability rules were developed for perennial ryegrass (Lolium perenne L.) and lucerne (Medicago sativa L.) involving growth responses to soil characteristics (pH, soil depth, electrical conductivity, drainage, and coarse fragments) and average annual rainfall. Suitability classes were defined as well suited, suitable, moderately suitable, and unsuitable, with additional subclasses to account for soil limitations that could be mitigated through management. Soil grids were generated using digital soil mapping techniques from ~6500 new and existing site data sources spread across Tasmania. Rainfall data from 539 Bureau of Meteorology rainfall-recording sites were modelled using regression kriging interpolation. Soil pH was found to be a major constraint on lucerne, with 61.3% of the land area having a pH <5.7. Ameliorating the soil with lime could reduce this constraint to 33.5% of the land area. Drainage was another major constraint on lucerne suitability, with 37.8% of land constrained by imperfectly or poorly drained soils. Improving drainage by installing surface or underground drains could reduce the affected area to 22.1%. The mapping showed that perennial ryegrass was constrained by soil pH, with 38.2% of land having a pH <5.5. However, liming could reduce this constraint to just 9.6%. Accurate identification of the likely constraints on pasture production and persistence before sowing and choice of appropriate species and management intervention will result in fewer failed sowings and greater productivity. The feasibility of expanding this approach is being assessed for a larger area of south-eastern Australia and across a wider range of pasture species.

Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation. Climate change and rapid land use change are degrading peatlands, liberating their stored carbon (C) into the atmosphere. To conserve peatlands and help in realising the Paris Agreement, we need to understand their extent, status, and C stocks. However, current peatland knowledge is vague—estimates of global peatland extent ranges from 1 to 4.6 million km2, and C stock estimates vary between 113 and 612 Pg (or billion tonne C). This uncertainty mostly stems from the coarse spatial scale of global soil maps. In addition, most global peatland estimates are based on rough country inventories and reports that use outdated data. This review shows that digital mapping using field observations combined with remotely-sensed images and statistical models is an avenue to more accurately map peatlands and decrease this knowledge gap. We describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping. We found that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing. Many studies have delineated peat extent using land cover from remote sensing, ecology, and environmental field studies, but rarely perform validation, and calculating the uncertainty of prediction is rare. This paper then reviews various proximal and remote sensing techniques that can be used to map peatlands. These include geophysical measurements (electromagnetic induction, resistivity measurement, and gamma radiometrics), radar sensing (SRTM, SAR), and optical images (Visible and Infrared). Peatland is better mapped when using more than one covariate, such as optical and radar products using nonlinear machine learning algorithms. The proliferation of satellite data available in an open-access format, availability of machine learning algorithms in an open-source computing environment, and high-performance computing facilities could enhance the way peatlands are mapped. Digital soil mapping allows us to map peat in a cost-effective, objective, and accurate manner. Securing peatlands for the future, and abating their contribution to atmospheric C levels, means digitally mapping them now.

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998-2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7-12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6-26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Soil Security is an emerging sustainability science concept with global application for guiding integrated approaches to land management, while balancing ecosystem services, environmental, social, cultural, and economic imperatives. This discussion paper sets the scene for an Australian Soil Security framework as an example of how it might be developed for any country, defining the key issues and justification for Soil Security, as well as detailing implementation requirements and benefits; two examples of beneficial outcomes are provided in terms of facilitating decommoditization of agricultural products and the impact of urban encroachment on productive land. We highlight research gaps, where new knowledge will contribute to well-rounded approaches that reflect differing stakeholder perspectives. We also provide key nomenclature associated with a potential Soil Security framework so that future discussions may use a common language. Through this work we invite scientific and policy discourse with the aim of developing more informed responses to the myriad of competing demands placed on our soil systems.