激光诱导击穿光谱(LIBS)和可见近红外光谱(vis-NIRS)是光谱技术,为传统实验室方法提供了有希望的替代方案,可以快速,经济有效地大规模测定土壤性质。尽管他们的个人局限性,与单传感器方法相比,结合LIBS和vis-NIRS已被证明可以提高确定土壤特性的预测精度。在这项研究中,我们使用了一个全面的丹麦国家尺度的土壤数据集,其中包括从各种土地利用和土壤深度收集的大部分沙质土壤,以评估LIBS和vis-NIRS的性能。以及它们的组合光谱,预测土壤有机碳(SOC)和质地。首先,开发了偏最小二乘回归(PLSR)模型,以将LIBS和vis-NIRS光谱与参考数据相关联。随后,我们合并了LIBS和vis-NIRS数据,并开发了合并光谱的PLSR模型.最后,采用区间偏最小二乘回归(iPLSR)模型评估变量选择对LIBS和vis-NIRS预测准确性的影响.尽管技术完全不同,LIBS和vis-NIRS对所研究的土壤特性显示出可比的预测性能。LIBS实现了纹理预测均方根误差(RMSEP)<7%,SOC预测均方根误差<0.5%,而vis-NIRS实现了RMSEP<8%的质地和0.5%的SOC。结合LIBS和vis-NIRS光谱将粘土的预测精度提高了16%,6%为淤泥和沙子,与单传感器LIBS预测相比,SOC为2%。另一方面,粘土的vis-NIRS单传感器预测提高了10%,17%为淤泥,沙子占16%,SOC为4%。此外,应用iPLSR进行变量选择提高了LIBS和vis-NIRS的预测精度。与LIBSPLSR预测相比,iPLSR在粘土和砂预测的RMSEP中实现了27%和17%的降低,分别,淤泥和SOC预测减少8%。同样,vis-NIRSiPLSR模型显示粘土和SOC的RMSEP降低了6%和4%,分别,淤泥和沙子减少3%。有趣的是,LIBSiPLSR模型在预测准确性方面优于LIBS-vis-NIRS组合模型。尽管LIBS和vis-NIRS的结合提高了纹理和SOC的预测精度,结合变量选择的LIBS在预测准确性方面具有更大的益处。未来的研究应研究参考方法不确定性对预测准确性的影响。
Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.