Abstract. Weathering is widely used for pedogenesis and soil fertility studies, once it affects several soil attributes. Understanding the intensities of weathering can provide answers for environmental issues, soil and geosciences studies. Recently, there are available geotechnologies (such as geophysics and machine learning algorithms) that can be applied in soil science to provide pedosphere information. In this research, we performed a method to evaluate weathering intensity in a heterogeneous tropical area by proximal remote sensing data acquired by geophysical and satellite images respectively. The area is located in southwest Brazil, with 184ha and we sampled 79 sites (all with soil analysis) using toposequence knowledge. Afterwards, the principal component analysis and the ideal number of clusters was determined. Then, we determine and used the ideal number of clusters, weathering index, as input data in four modelling (prediction and spatialization) algorithms to infer different weathering intensities in soils formed from the same soil parent material. The results showed that the best model performance was for the random forest reaching 3 clusters as the ideal number. The surface pixel reflectance acquired from a Synthetic Soil Image, the terrain surface convexity and digital elevation model were the covariates that most contributed to modelling processes. The model’ specificity was greater than sensitivity. The East areas over diabase such as the Nitisol presented greater weathering intensity than the Nitisol over West diabase areas. The areas over siltite/metamorphosed siltite and Lixisols presented moderate weathering rates. The relief and topographic position strongly affected the weathering, once they controlled the hydric dynamics. The geophysical variables were related to soil attributes and weathering, which contributed to modelling and clusterization processes. The different weathering rates are mainly modulated by geomorphic processes that relief, topographic position, and the associated soil types control water dynamic at the landscape and directly affect the weathering intensities.

中文翻译：

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通过机器学习、聚类和地球物理传感器评估热带土壤的风化强度

摘要。一旦风化作用影响多种土壤属性，风化作用就被广泛用于成土作用和土壤肥力研究。了解风化的强度可以为环境问题、土壤和地球科学研究提供答案。最近，有可用的岩土技术（如地球物理学和机器学习算法）可应用于土壤科学以提供土壤圈信息。在这项研究中，我们执行了一种方法，通过地球物理和卫星图像分别获取的近端遥感数据来评估异质热带地区的风化强度。该地区位于巴西西南部，占地 184 公顷，我们使用拓扑序列知识对 79 个地点（均进行了土壤分析）进行了采样。然后，确定主成分分析和理想的聚类数。然后，我们确定并使用理想的聚类数、风化指数作为四种建模（预测和空间化）算法的输入数据，以推断由相同土壤母质形成的土壤中不同的风化强度。结果表明，最佳模型性能是随机森林达到 3 个集群作为理想数量。从合成土壤图像获得的表面像素反射率、地形表面凸度和数字高程模型是对建模过程贡献最大的协变量。该模型的特异性大于敏感性。东部辉绿岩区如尼蒂索比西部辉绿岩区表现出更大的风化强度。粉砂岩/变质粉砂岩和 Lixisols 上的区域表现出中等风化速率。一旦控制了水力动力学，地势和地形位置强烈影响风化。地球物理变量与土壤属性和风化有关，这有助于建模和聚类过程。不同的风化速率主要受地貌过程的调节，地形、地形和相关的土壤类型控制着景观的水动力并直接影响风化强度。