Xiao Zhen-zhen, Li Yi, Feng Hao
Guang Pu Xue Yu Guang Pu Fen Xi. 2016 May;36(5):1615-22.
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 normal 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 highest 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 accuracy with r value of 0.647 and RMSE value of 1.932. As to the model accuracy of the studied soil physico-chemical 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 3951 801 nm, 3521 144 nm and 3941 011 nm, respectively. Since soil physico-chemical properties were highly spatially variable, there were large differences for the model establishment and validation of the soil properties. This research could be a reference of hyperspectral remote sensing monitoring of salinized soils.
高光谱遥感数据具有特殊优势，即具有高光谱分辨率和强波段连续性，大量光谱信息可广泛用于土壤性质监测研究。利用高光谱遥感技术分析盐碱土性质对灌区作物生长和农业可持续发展具有重要意义。从玛纳斯河流域采集了221个土壤样本，测量土壤电导率（EC）、土壤有机质（SOM）以及包括Na +、Ca2 +和Mg2 +在内的3种阳离子浓度，用于获取钠吸附比值（SAR）。还测量了土壤高光谱曲线。基于原始反射率（R）、标准正态变量（SNV）、归一化植被指数（NDVI）、倒数对数（LR）、一阶导数反射率（FDR）和连续统去除反射率（CR）这六个光谱相关指数，采用逐步线性回归法建立了EC、SOM和SAR模型。结果表明，与其他五个模型相比，log（EC）R模型精度最高，r值为0.782，RMSE值为0.256。SOM与NDVI模型精度最高，r值为0.670，RMSE值为5.352。SAR与FDR模型精度最高，r值为0.647，RMSE值为1.932。就所研究土壤理化性质的模型精度而言，log（Ec）模型最有效，其次是SOM模型，SAR模型最不准确。EC、SOM和SAR的敏感波长分别分布在3951801nm、3521144nm和3941011nm。由于土壤理化性质在空间上高度可变，土壤性质的模型建立和验证存在很大差异。本研究可为盐渍化土壤的高光谱遥感监测提供参考。
