沙质土壤热红外高光谱特征及其含沙量预测研究

为明确土壤热红外高光谱数据反演土壤含沙量的应用潜力,利用102F型便携式傅里叶变换红外光谱仪对沙质土壤进行测量,在进行相关分析和主成分分析的基础上,对土壤发射率光谱特征进行了分析,并采用偏最小二乘回归和主成分回归两种建模方法预测土壤含沙量.结果表明,沙质土壤发射率光谱中二氧化硅的Reststrahlen特征表现明显,在...

Characteristics of thermal infrared hyperspectra and prediction of sand content of sandy soil

To explore the potential of thermal infrared hyperspecra for retrieving sand content in soil, the sandy soil was measured using a 102F Fourier Transform Infrared Spectroradiometer (FTIR), and the characteristics of sandy soil's emissivity spectra were discussed based on correlation analysis and principal component analysis. Moreover, the sand contents were predicted using two modeling methods: Partial least squares regression (PLSR) and principal component regression (PCR). The results show that the Reststrahlen feature (RF) of SiO2 is obvious in the emissivity spectra of sandy soil with two large asymmetrical absorption troughs near 8.13 and 9.17 microm and two small troughs in the region of 12-13 microm. Soil emissivity becomes lower when sand content increases, this trend is more evident especially in the regions of 8-9.5 microm and 9.5-10.4 microm of which correlation coefficients are above 0.65 and 0.5 respectively, and these two regions can account for 84.07% of total emissivity variance. Predictive precision varies significantly when sand content is predicted by different modeling methods or spectral variables. The PLSR model can achieve the highest predictive precision by using first-order derivative spectra, and it's RMSE of modeling and prediction is 0.45 and 0.53 respectively, and the R2, 0.9907 and 0.9836, which means that the thermal hyperspectra has promising potential for retrieving sand content in soil.