The management of forests needs well informed decisions by stakeholders to fulfil the goals of sustainability, stability, and productivity. Decisions are guided by forest site maps. The forest site map of Hesse, Germany, consists of six soil nutrient index (SNI) classes (poor, moderate-weak, moderate, moderate-good, rich, carbonatic). Three major challenges regarding the currently available site information exist: (i) the spatial proportion of “moderate” sites is exceptionally high (65% of mapped forest area) and while there is differentiation between parent materials, topography is neglected. (ii) As 80% of Hesse's forests were mapped, there is demand to fill the gaps without site information. (iii) The existing SNI does not take soil analysis into account, which is required to detect finer differences in morphologically similar soil profiles. Objectives were to regionalize soil chemical variables and derive a more differentiated SNI for Hesse's forest soils with complete forest coverage.

Stocks of intermediately available Ca, Mg, and K, base saturation, effective cation exchange capacity, and C/N ratio of 380 profiles from the National Forest Soil Inventory were used to characterize the SNI. Regionalization of soil chemical variables was successfully performed (R² values from 0.54 to 0.79, root mean square deviation 5 to 17%) using generalized additive models. SNI classes were inferred by fuzzy logic for dealing with soil chemical variables. The results were highly sensitive towards parent material and topography. The modelled SNI map provides a much more differentiated and complete map for Hesse, Germany, which mirror actual expectations across landscape units. The approach is transparent and inter-subjectively reproducible. The new map will be used to guide the management and reforestation of sites, which were damaged by biotic or abiotic threats due to recent climatic extreme events.

森林管理需要利益相关者做出明智的决定，以实现可持续性，稳定性和生产力的目标。决策以森林站点地图为指导。德国黑森州的森林站点地图包含六个土壤养分指数（SNI）类（差，中弱，中，好，中等，丰富，含碳）。关于当前可用的站点信息存在三个主要挑战：（i）“中等”站点的空间比例极高（占所绘制森林面积的65％），并且尽管母体材料之间存在差异，但地形却被忽略了。（ii）由于对黑森州80％的森林进行了制图，因此需要在没有站点信息的情况下填补空白。（iii）现有的SNI没有考虑土壤分析，这是检测形态相似的土壤剖面中细微差异所必需的。

利用国家森林土壤资源清单中380个剖面的中度可用Ca，Mg和K的存量，基本饱和度，有效阳离子交换容量和C / N比来表征SNI。成功完成了土壤化学变量的区域化（R ²值从0.54到0.79，均方根偏差为5到17％）。SNI类是通过模糊逻辑推理来处理土壤化学变量的。结果对母体材料和地形高度敏感。建模的SNI地图为德国黑森州提供了更加差异化和完整的地图，该地图反映了景观单位之间的实际期望。该方法是透明的，并且可以在主观之间重复。新地图将用于指导站点的管理和植树造林，这些站点由于近期的气候极端事件而受到了生物或非生物威胁的破坏。