Proximal sensors techniques, such as portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS), are becoming increasingly popular for predicting soil properties worldwide. However, there are few studies investigating the effectiveness of combining these proximal sensors for prediction and mapping soil texture in tropical soils. This work evaluated the feasibility of combining such sensors for the prediction and mapping of soil texture (sand, silt, and clay contents) through random forest algorithm in an area with varying parent materials, soil classes and land uses. A total of 236 soil samples were collected from A and B horizons, following a regular-grid design with 200 m distance between samples. All samples were scanned with pXRF and susceptibilimeter. Models for A and B horizons separately and combined were built using 70% of the samples and validated with the remaining 30% of the samples. The models with the lowest RMSE values were chosen for soil mapping and further validation. The predictions produced acceptable accuracy in modeling and mapping clay and sand fractions, but were less effective to directly predict silt fraction, although it can be easily calculated through: silt = 100 - sand – clay. MS, Fe, K₂O, and SiO₂, properties related to soil parent material, were the most important variables for the predictions. The best models achieved an R² for sand, silt and clay of 0.79, 0.44 and 0.71, respectively. These results represent alternative methods for reducing costs and accelerating the assessment of soil texture spatial variability, supporting agronomic and environmental decision makings.

近端传感器技术，例如便携式X射线荧光（pXRF）光谱法和磁化率（MS），在预测全球土壤特性方面正变得越来越流行。但是，很少有研究调查结合使用这些近端传感器来预测和绘制热带土壤中土壤质地的效果。这项工作评估了通过随机森林算法在具有不同母体材料，土壤类别和土地用途的区域中组合此类传感器以预测和绘制土壤质地（沙，淤泥和粘土含量）的可行性。按照常规网格设计，样品之间的距离为200 m，从A和B层共采集了236个土壤样品。用pXRF和磁化率计扫描所有样品。使用70％的样本分别构建和组合了A和B层的模型，并使用其余30％的样本进行了验证。选择具有最低RMSE值的模型进行土壤测绘和进一步验证。这些预测在模拟和绘制粘土和砂分数时产生了可接受的精度，但是直接预测粉砂分数的效果较差，尽管可以通过以下方式轻松地计算得出：泥= 100-砂-粘土。MS，Fe，K与土壤母体材料有关的₂ O和SiO ₂特性是最重要的预测变量。最佳模型得出的沙，粉砂和粘土的R ^2分别为0.79、0.44和0.71。这些结果代表了降低成本，加快土壤质地空间变异性评估，支持农艺和环境决策的替代方法。