西北盐碱土理化性质的高光谱建模及预测

高光谱数据具有光谱分辨率高、波段连续性强、信息丰富等特点,在土壤信息的监测中得到广泛应用。利用高光谱遥感技术测定盐渍化土壤属性对灌区农作物的生长和农业可持续发展具有重要意义。采集玛纳斯河流域221个土壤样品,分别测定土壤电导率(EC)、有机质(SOM)和 Na+,Ca2+,Mg2+三种离子浓度含量等土壤理化性质和光谱反射率曲线,并由三种离子含量得出钠吸附比值(SAR),采用逐步线性回归方法建立 EC,SOM和 SAR与原始光谱反射率(R)、标准正态变量(SNV)、归一化差异植被指数(NDVI)、倒数的对数(LR)、一阶微分(FDR)和去包络线(CR)等六种指标的模型。模型验证结果表明,相较其他五种变量的模型,以R为自变量的EC对数模型精度最高,相关系数为0.782,均方根误差为0.256。以NDVI为自变量的土 SOM预测模型精度最高,相关系数为0.670,均方根误差为5.352。以FDR为自变量的SAR预测模型精度最高,相关系数为0.647,均方根误差为1.932。EC 预测模型效果最好,SOM预测模型次之, SAR预测模型精度最低。最优模型中 EC,SOM和 SAR的敏感波长分别分布于395~1801,352~1144和394~1011 nm波段。由于土壤中各属性的差异和不同成分空间分布的变异性,对于不同土壤性质的建模和验证结果差异较大。本研究可为盐渍化土壤的高光谱遥感监测提供依据。

Hyperspectral Models and Forcasting of Physico-Chemical Properties for Salinized Soils in Northwest China

Hyperspectral remote sensing data have special advantages,i.e.,they have high spectral resolution and strong band continuity,and a great number of spectral information could be widely used in soil properties monitoring research.Using hyperspectral remote sensing technique to analyze saline soil properties makes great significance for the crop growth in the irrigation district and agricultural sustainable development.221 soil samples were collected from Manasi River Basin to measure soil electrical conductivity (EC),soil organic matter (SOM)and 3 kinds of cation concentrations including Na+,Ca2+ and Mg2+,which were used to obtain sodium adsorption ration value (SAR).The soil hyperspectral curves were also measured.EC,SOM and SAR models were established based on the six spectral-related indices,including raw reflectance (R),standard nor-mal variable (SNV),normalized difference vegetation index (NDVI),logarithm of the reciprocal (LR),the first derivative reflectance (FDR)and continuum-removal reflectance (CR)by the stepwise linear regression method.The results showed that,compared to the other five models,the model of log (EC)~R had the high-est accuracy with r value of 0.782 and RMSE value of 0.256.The model of SOM vs.NDVI had the highest accuracy with r value of 0.670 and RMSE value of 5.352.The model of SAR vs.FDR had the highest accura-cy with r value of 0.647 and RMSE value of 1.932.As to the model accuracy of the studied soil physico-chem-ical properties,the log(Ec)model was the most effective one,followed by the SOM model,the SAR model was the most inaccurate.The sensitive wavelengths for EC,SOM and SAR distributed in 395~1 801 nm,352~1 144 nm and 394~1 011 nm,respectively.Since soil physico-chemical properties were highly spatially vari-able,there were large differences for the model establishment and validation of the soil properties.This re-search could be a reference of hyperspectral remote sensing monitoring of salinized soils.