盐渍化土壤光谱特征的区域异质性及盐分反演

该文通过分析中国新疆、浙江、吉林3个不同地区盐渍化土壤的高光谱特征,研究了盐渍化土壤高光谱特征的区域异质性,并对构建高精度的跨区域土壤盐分高光谱定量反演模型,应用25种数据处理方式来提高全局建模的精度,旨在提高具有光谱异质性土壤的盐分反演精度。结果表明:不同地区的盐渍化土壤,无论是反射率还是光谱曲线形态方面,均存在较明显的差异,但经过一阶微分处理后,光谱差异有所降低;对3个地区土壤盐分含量局部建模与全局建模的精度进行比较,在所选用的直线回归、主成分回归、多元线性回归、偏最小二乘回归4种建模方法中,全局建模精度均低于局部建模精度;不同地区盐渍化土壤的盐分敏感波段不一致,在所采用的25种数据处理方式中,SG3点一阶微分(savitzky golay)、SG5点一阶微分、SG7点一阶微分、线性基线校正+SG3点一阶微分、SG平滑+SG3点一阶微分、SG平滑+线性基线校正+SG3点一阶微分这6种数据处理方式对全局建模的建模精度有明显改善作用,模型的相对分析误差均达到2.0以上,其中以SG平滑+SG3点一阶微分为最佳,其决定系数、均方根误差、相对分析误差分别为0.80、0.43、2.23。研究结果为跨区域土壤盐渍化的航天高光谱遥感监测提供了一定的参考依据。

Regional heterogeneity of hyperspectral characteristics of salt-affected soil and salinity inversion

Abstract: The objectives of this study were to analyze regional heterogeneity of hyperspectral characteristics of salt-affected soils from Xinjiang Uygur Autonomous Region, Zhejiang and Jilin provinces and to establish hyperspectral inversion model of salinity for cross-regional salt-affected soils with high precision. One hundred and fifty-nine soil samples at 0-20 cm depth were taken from Xinjiang Uygur Autonomous Region (58 soil samples), Zhejiang (68 soil samples) and Jilin (33 soil samples) provinces, respectively. Electrical conductivity(1:5 soil to water, EC1:5) and spectral reflectance (SR) of all the 159 soil samples were determined. Regression models between EC1:5 and SR were fitted using principal component regression (PCR), multiple linear regression (MLR), and partial least squares regression (PLSR) based on local and global models, respectively. The prediction accuracies of these models were assessed by comparing determination coefficients (R2), relative percent deviation (RPD) and root mean squared error (RMSE) between predicted and measured EC1:5. Results showed that there were obvious differences not only in spectral reflectances but also in spectral curve shapes among the salt-affected soils from different regions. After a first derivative data processing, however, these differences were decreased. Values of R2, RMSE and RPD between the predicted and measured EC1:5 for the global model were 0.06, 0.93 and 1.03 for PCR equation, 0.10, 0.91 and 1.04 for MLR equation, 0.71, 0.51 and 1.85 for PCR equation, and 0.71, 0.51, 1.86 for PLSR equation, respectively. Values of R2, RMSE and RPD between the predicted and measured EC1:5 for the local model were 0.45, 0.73 and 1.30 for LR equation, 0.50, 0.69 and 1.38 for MLR equation, 0.76, 0.46 and 2.05 for PCR equation, and 0.78, 0.44, 2.15 for PLSR equation, respectively. The values of R2 and RPD between the predicted and measured EC1:5 were higher for local models than those of global models, but the values of RMSE of local models between the predicted and measured EC1:5 were lower than that of global models. This indicated that the local models were more accurate than the global models in predicting EC1:5 from soil spectral reflectances. In order to improve the prediction accuracy of global model, 25 data processing methods were carried out for soil spectral reflectances. It was shown that the sensitive bands of EC1:5 varied with study regions. Among all of the 25 data processing methods, the prediction accuracy of global model based on Savitzky Golay Second Derivative (SGSD) method decreased drastically compared with that based on the spectral reflectance method. Prediction accuracies of inversion models decreased slightly based on area normalization (AN), mean normalization (MN), unite vector normalization(UVN), maximum normalization (MAN), range normalization(RN), linear baseline correction(LBC), Savitzky Golay Smoothing (SCS) and multiplicative scatter correction (MSC). Six data processing methods including three-point savitzky golay first derivative (SGFD3), five-point cavitzky golay first derivative (SGFD5), seven-point savitzky golay first derivative (SGFD7), LBC+SGFD3, SGS+ SGFD3 and SGS+LBC+SGFD3 improved inversion accuracies of global models. The values of PRD were greater than 2.0 for inversion equations based on these 6 data processing methods. The inversion accuracy based on SGS+SGFD3 data processing method was best with the R2, RMSE and RPD of 0.80, 0.43, and 2.23, respectively. The study can provide valuble information for aerospace hyperspectral remote sensing of cross-regional soil salinization.