Over the last 2 decades, geospatial technologies such as Geographic Information System and spatial interpolation methods have facilitated the development of increasingly accurate spatially explicit assessments of soil erosion. Despite these advances, current modelling approaches rest on (a) an insufficient definition of the proportion of arable land that is exploited for crop production and (b) a neglect of the intra-annual variability of soil cover conditions in arable land. To overcome these inaccuracies, this study introduces a novel spatio-temporal approach to compute an enhanced cover-management factor (C) for revised universal soil loss equation-based models. It combines highly accurate agricultural parcel information contained in the Land Parcel Identification System with an object-oriented Landsat imagery classification technique to assess spatial conditions and interannual variability of soil cover conditions at field scale. With its strong link to Land Parcel Identification System and Earth observation satellite data, the approach documents an unprecedented representation of farming operations. This opens the door for the transition from the currently used potential soil erosion risk assessments towards the assessment of the actual soil erosion risk. Testing this method in a medium-size catchment located in the Swiss Plateau (Upper Enziwigger River Catchment), this study lays an important foundation for the application of the very same methods for large-scale or even pan-European applications. Soil loss rates modelled in this study were compared with the insights gained from emerging techniques to differentiate sediment source contribution through compound-specific isotope analysis on river sediments. The presented technique is adaptable beyond revised universal soil loss equation-type soil erosion models.

中文翻译：

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面向对象的土壤侵蚀建模：农业环境中从潜在风险评估到实际风险评估的可能范式转变

在过去的 20 年中，地理信息系统和空间插值方法等地理空间技术促进了土壤侵蚀的空间明确评估越来越准确的发展。尽管取得了这些进展，当前的建模方法依赖于 (a) 对用于作物生产的耕地比例的定义不充分，以及 (b) 忽略了耕地土壤覆盖条件的年内变化。为了克服这些不准确之处，本研究引入了一种新的时空方法来计算基于修订的通用土壤流失方程模型的增强覆盖管理因子 (C)。它将土地包裹识别系统中包含的高精度农业包裹信息与面向对象的 Landsat 影像分类技术相结合，以评估田间规模的空间条件和土壤覆盖条件的年际变化。凭借与地块识别系统和地球观测卫星数据的紧密联系，该方法以前所未有的方式记录了农业作业。这为从目前使用的潜在土壤侵蚀风险评估过渡到实际土壤侵蚀风险评估打开了大门。在位于瑞士高原（Upper Enziwigger River Catchment）的一个中等规模的流域中测试该方法，这项研究为将完全相同的方法应用于大规模甚至泛欧洲应用奠定了重要基础。本研究中模拟的土壤流失率与从新兴技术中获得的见解进行比较，通过对河流沉积物的化合物特定同位素分析来区分沉积物来源的贡献。所提出的技术适用于修订后的通用土壤流失方程型土壤侵蚀模型。