Rewetting peatland is an ongoing effort in Indonesia to restore the hydrological cycle and carbon balance of the ecosystem. However, quantifying the impact of rewetting on mitigating fire remains a challenge. Here, we assess the impact of large-scale rewetting on fire risks and occurrences (duration, coverage area, and the number of events) in 2015–2021. The weather research and forecasting (WRF) model was integrated with a drought–fire model to spatially quantify fire hazards in Riau, Sumatra. The results show that rewetting has resulted in decreasing the frequency of extreme events in the study area (pre- and post-rewetting, respectively, were seven and four events). Although the area influenced by extreme events reduced following rewetting by 5%, the mean duration of extreme events increased. Our findings reveal that widespread prolonged extreme fire hazards only occurred during drying El Niño events in 2015 and 2019. The findings obtained in this case study provide quantitative evidence of the reduced fire hazard resulting from peat restoration in Indonesia. Further, the findings assist in assessing the success of peatland restoration programs and improve our knowledge of the ability to monitor and forecast fire risks in tropical peatlands.

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 $\text{MAE}=\mathrm{0.07}{\mathrm{m}}^{\mathrm{3}}{\mathrm{m}}^{-\mathrm{3}}$ 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.

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.