Abstract. A critical tool to succeed in sustainable spatial planning is accurate and detailed maps. 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 with high accuracy (Kappa = 0.69, MCC = 0.68). As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5 479 soil plots across the country) and the continuous SLU 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 visible from airplanes – the historical approach of mapping. Most importantly we also provided a continuous map of the organic layer, which ranged 6–95 cm organic layer thickness, with an R² of 0.67 and RMSE 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. green-house gas or biodiversity inventories and landscape ecological research.

中文翻译：

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描绘北部北方地区景观尺度的矿质土壤和泥炭土壤的分布

摘要。成功进行可持续空间规划的一个关键工具是准确而详细的地图。为了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的测绘。在这里，我们提出了一种基于高分辨率数字土壤水分图识别泥炭土壤的新方法，该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 m 空间分辨率的土壤水分和高准确度（Kappa = 0.69，MCC = 0.68）。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 5 479 个土壤样地测量）和连续 SLU 土壤水分图（R²= 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出现的分类图。与传统的第四纪沉积物图 (MCC = 0.65) 和地形图 (MCC = 0.61) 相比，预测的泥炭图具有更高的整体质量 (MCC = 0.69–0.73)，并且与 50– 相比，泥炭地的召回率约为 80% 70% 在传统地图上。预测的泥炭地图比以前的地图确定了更多的泥炭地面积，并且面积覆盖率估计与国家实地调查的升级估计值相同。我们的方法能够识别较小的泥炭地，从而产生更准确的泥炭土壤图，这不仅限于从飞机上看到的大泥炭地——历史的测绘方法。最重要的是，我们还提供了有机层的连续图，有机层厚度范围为 6-95 cm，R²的 0.67 和 19 厘米的 RMSE。连续地图展示了有机层从矿质土壤到泥炭土壤的平稳过渡，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壤划定的直观不确定性估计，对于可持续空间规划至关重要，例如温室气体或生物多样性清单和景观生态研究。