Dong Zhenyu, Wang Ni, Xie Jiancang, Ke Xinyue
Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China.
Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Oct 5;318:124496. doi: 10.1016/j.saa.2024.124496. Epub 2024 May 20.
Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg, 1.457 g kg, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg and 1.967 g kg, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.
快速准确地掌握农田土壤有机碳含量的变化，对于及时有效地掌握农田土壤肥力和改善土壤物理性质具有重要意义。本研究采用ASD FieldSpec 4光谱仪，对采自中国陕西省榆林市靖边县的128个农业土壤样本进行光谱反射率数据采集。首先，对研究区土壤有机碳进行描述性统计，其次，在进行10种光谱变换后，分别采用相关性分析和Boruta算法提取土壤有机碳特征波段，以减少数据冗余。最后，通过比较不同策略的精度，构建了一个集成最优预处理、特征选择策略和建模方法的西北农牧交错区农田土壤有机碳光谱预测模型。结果表明：1）研究区农田土壤有机碳平均含量较低，处于养分缺乏水平，建模集和验证集的标准误差和变异系数分别为1.596 g/kg、1.457 g/kg、54%和52%；2）不同土壤有机碳含量的光谱特征曲线形状和趋势具有一致性，土壤有机碳含量与光谱反射率呈负相关；3）CA选择的特征波段较多，但特征波段较为同质化，而Boruta算法能有效去除无关变量，提高土壤有机碳特征选择效果；4）基于Boruta-FD-RF的土壤有机碳预测模型对土壤有机碳的估算效果较好，训练集和验证集的R分别为0.899和0.748，RMSE分别为1.432 g/kg和1.967 g/kg，RPD分别为2.557和1.647。结果表明，集成最优光谱预处理、特征选择策略和化学计量学策略建立的土壤有机碳模型在预测精度和稳定性方面有明显提高，本研究为西北农牧交错区农田土壤有机碳含量的快速准确估算提供了重要参考。
