Li Aiwen, Cheng Jinli, Chen Dan, Li Wendan, Mao Yaruo, Chen Xinyi, Zhao Bin, Shi Wenjiao, Yue Tianxiang, Li Qiquan
College of Resources, Sichuan Agricultural University, Chengdu, 611130, China.
College of Environmental Sciences, Sichuan Agricultural University, Chengdu, 611130, China.
Sci Rep. 2025 Mar 7;15(1):8008. doi: 10.1038/s41598-025-91335-y.
Large sample sizes are crucial for accurately capturing spatial changes in soil properties by spatial interpolation methods. However, soil bulk density (BD) data in historical datasets is often incomplete, and it's uncertain if filled values enhance spatial interpolation accuracy. Using 2,883 cropland soil BD samples from the Sichuan Basin in China, we developed the best prediction models from traditional pedotransfer function (PTF), multiple linear regression (MLR), random forest (RF), and radial basis function neural network (RBFNN) to fill missing BD values for 1,336 samples. We then applied ordinary kriging (OK) and inverse distance weighting (IDW) to map soil BD, incorporating the filled BD as modeling points. The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination (R) by 19.54-37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91-14.81%, 9.02-16.22% and 7.71-13.61%, respectively. Incorporating filled BD data reduced the MAE, MRE, and RMSE of OK and IDW by 4.17%, 4.36%, 4.96%, and 6.54%, 6.92%, 8.15%, respectively, significantly lowering spatial interpolation uncertainty. This methodology improves the accuracy of soil property mapping in regions with incomplete historical data.
大样本量对于通过空间插值方法准确捕捉土壤属性的空间变化至关重要。然而，历史数据集中的土壤容重（BD）数据往往不完整，填充值是否能提高空间插值精度尚不确定。利用中国四川盆地的2883个农田土壤BD样本，我们从传统的土壤传递函数（PTF）、多元线性回归（MLR）、随机森林（RF）和径向基函数神经网络（RBFNN）中开发了最佳预测模型，以填充1336个样本的缺失BD值。然后，我们应用普通克里格法（OK）和反距离加权法（IDW）来绘制土壤BD图，将填充后的BD作为建模点。针对每个子流域量身定制的RBFNN模型在填充缺失BD方面具有最高的精度，决定系数（R）提高了19.54 - 37.36%，平均绝对误差（MAE）、平均相对误差（MRE）和均方根误差（RMSE）分别降低了8.91 - 14.81%、9.02 - 16.22%和7.71 - 13.61%。纳入填充后的BD数据分别将OK和IDW的MAE、MRE和RMSE降低了4.17%、4.36%、4.96%和6.54%、6.92%、8.15%，显著降低了空间插值的不确定性。该方法提高了历史数据不完整地区土壤属性制图的准确性。
