Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro^TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro^TM) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R² = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro^TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors.

土壤质地是影响许多土壤化学-物理-生物过程的主要变量，为有关可持续土壤管理的决策提供重要信息。然而，用于确定土壤质地的标准传统方法是手动执行的，耗时、成本高，并且会产生化学废物。作为替代方案，便携式 X 射线荧光 (pXRF) 光谱法和可见近红外光谱法 (Vis-NIR) 在全球范围内越来越多地用于预测土壤属性。其他传感器（例如 NixPro ^TM颜色传感器）也很有前途，但迄今为止评估较少。因此，用于预测热带土壤中土壤质地分离（粘土、淤泥和总沙、粗沙和细沙含量）和土壤质地类别（壤土、壤质沙等）的近端传感器数据融合研究很少。因此，本研究旨在通过热带地区的随机森林算法评估用于预测土壤颗粒大小分数和土壤质地类别（家庭颗粒大小类别和美国农业部土壤质地三角形）的近端传感器数据。从巴西的 A (n = 208) 和 B (n = 256) 地层共采集了 464 个土壤样品。将土壤样品提交给实验室分析土壤质地和近端传感器（pXRF、Vis-NIR 和 NixPro ^TM) 扫描。样本被随机分成 70% 用于建模和 30% 用于验证。最佳方法因预测属性而异；然而，pXRF 数据是土壤质地预测准确性的关键信息。最佳结果通过上述用于快速评估土壤质地的近端传感器提供了高度准确的预测（总沙子 R ²  = 0.84, RMSE = 7.60%；淤泥 0.83, 6.11%；粘土 0.90, 5.64%；粗沙子 0.87, 6.30%；细砂 0.82, 5.27%)。当通过土壤顺序子数据集进行预测时，土壤质地类别（家庭颗粒大小类别，总体准确度 = 0.97，Kappa 指数 = 0.95；美国农业部土壤质地三角形，0.83, 0.73）的分类预测准确性得到提高。平滑 Vis-NIR 预处理和干燥 NixPro ^TM颜色数据对结果产生了积极影响。这里报告的结果代表了降低评估土壤质地所需的成本和时间的替代方案，支持巴西条件下的农艺和环境战略。进一步的工作应该将这项研究的结果扩展到温带地区，以证实本文提出的关于这三个近端传感器融合的结论。