To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape. As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture map (R²= 0.66, p < 0.001). We generated categorical maps of peat occurrence using three different definitions of peat (30, 40, and 50 cm thickness of the organic layer) and a continuous map of organic layer thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional Quaternary deposits maps (MCC = 0.65) and topographical maps (MCC = 0.61) and captured the peatlands with a recall of ca. 80 % compared to 50 %–70 % on the traditional maps. The predicted peat maps identified more peatland area than previous maps, and the areal coverage estimates fell within the same order as upscaling estimates from national field surveys. Our method was able to identify smaller peatlands resulting in more accurate maps of peat soils, which was not restricted to only large peatlands that can be visually detected from aerial imagery – the historical approach of mapping. We also provided a continuous map of the organic layer, which ranged 6–88 cm organic layer thickness, with an R² of 0.67 and RMSE (root mean square error) of 19 cm. The continuous map exhibits a smooth transition of organic layers from mineral soil to peat soils and likely provides a more natural representation of the distribution of soils. The continuous map also provides an intuitive uncertainty estimate in the delineation of peat soils, critically useful for sustainable spatial planning, e.g., greenhouse gas or biodiversity inventories and landscape ecological research.

中文翻译：

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在北部北方地区的景观尺度上描绘矿质和泥炭土的分布

为实现可持续发展目标并实现泥炭地的可持续管理和保护，迫切需要改进泥炭地测绘。在这里，我们提出了一种基于高分辨率数字土壤水分图来识别泥炭土的新方法，该地图是通过结合机载激光扫描衍生的地形指数和机器学习来模拟瑞典景观中 2 米空间分辨率的土壤水分而生成的。由于土壤水分是泥炭形成的关键因素，我们拟合了有机层厚度（在全国 5479 个土壤样地测量）与连续 SLU（瑞典农业科学大学）土壤水分图（R ² ）之间的经验关系=  0.66，p  <  0.001). 我们使用泥炭的三种不同定义（有机层厚度为 30、40 和 50 厘米）和有机层厚度的连续图生成了泥炭发生的分类图。 与传统的第四纪沉积图 (MCC  =  0.65  ) 和地形图 (  MCC = 0.61）并通过召回 ca 占领了泥炭地。80 %，而传统地图为 50 %–70 %。预测的泥炭地图确定了比以前的地图更多的泥炭地面积，并且面积覆盖估计值与国家实地调查的放大估计值处于同一顺序。我们的方 我们还提供了有机层的连续图，其范围为 6–88 cm 有机层厚度，R ²的 0.67 和 RMSE（均方根误差）为 19 厘米。连续地图展示了有机层从矿质土壤到泥炭土的平滑过渡，并可能提供更自然的土壤分布表示。连续地图还提供了泥炭土划分的直观不确定性估计，这对可持续空间规划（例如温室气体或生物多样性清单和景观生态研究）至关重要。