Proximal sensor data fusion for tropical soil property prediction: Soil fertility properties

• 全文链接

用于热带土壤特性预测的近端传感器数据融合：土壤肥力特性

关键词：

来源：

ScienceDirect

全文链接：

//agri.nais.net.cn/topic/downloadFile/bae127f6-cca3-4996-a7c9-b1b11bc76d27

来源地址：

https://doi.org/10.1016/j.jsames.2022.103873

资源所属：

农业水土资源监测专题

类型：

学术文献

语种：

英语

原文发布日期：

2022-06-04

摘要：

Proximal sensors have proven capable of predicting multiple soil properties under different conditions. However, doubts remain about which sensor is preferable for delivering optimal prediction models and which preprocessing methods produce the most accurate results. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) diffuse reflectance spectroscopy have been widely used, while the NixProTM color sensor has been explored more recently. This study evaluated the use of pXRF, Vis-NIR, and NixProTM data to predict soil organic matter content (SOM), pH, base saturation (BS), the sum of bases (SB), cation exchange capacity (CEC) at pH = 7 and effective CEC (eCEC), via each sensor in isolation, and via combined sensors data. Moreover, variables interfering in the prediction models' accuracy (data preprocessing methods, soil horizon, soil class, parent material) were used as auxiliary variables. 604 soil samples were collected in Brazil, encompassing ten soil orders and 19 parent materials. Numerical and categorical prediction models (7,980) were created for six soil properties using a random forest algorithm, totaling 7980 models, delivering almost 24,000 results, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), residual prediction deviation (RPD) for validation of numerical predictions, and overall accuracy and kappa coefficient for categorical predictions. Although the combination of sensors provided most of the best predictions, pXRF in isolation achieved accuracies close to the three sensors combined. NixProTM offered superior contributions to SOM and CEC predictions, but pXRF and Vis-NIR were responsible for the best results of most studied variables. On average, by adding pXRF to Vis-NIR data, predictive accuracy improved 32%; while adding Vis-NIR to pXRF data increased accuracy by c. a. 6%. Soil-order-specific models improved predictions for Ultisols compared to general models (without soil order distinction), reaching R2 > 0.90. Soil parent material and horizon did not improve models significantly. Categorical predictions improved the accuracy for some properties, reaching an overall accuracy of 100% and kappa index of 1.0 for pH in A horizons of Ultisols via pXRF + Vis-NIR data. Proximal sensor data with no auxiliary variables provided almost all the best results. The fusion of proximal sensors can provide better predictions, but pXRF alone can deliver satisfactory results in most cases for the six soil properties.