Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na⁺, Cl⁻, and SO₄²⁻ contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC; therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na⁺ content, with the R² reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl⁻ and SO₄²⁻, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments.

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基于高光谱遥感数据的土壤盐分和离子含量估算-以白墩子盆地为例

灌溉引水引起的土壤盐分影响区域农业，开发用于土壤盐分动态监测的土壤组成估算模型对于控制盐分具有重要意义。在这项研究中，我们评估了使用分析光谱设备（ASD）场规范标准分辨率手持式光谱仪和卫星传感器可见短波红外高级高光谱成像仪（AHSI）测得的高光谱数据在估算土壤盐含量（SSC）中的性能。使用原始光谱（RS）将一阶导数分析（FDA）和主成分分析（PCA）应用于数据，以选择最佳的模型输入数据。我们测试了这三组数据作为部分最小二乘回归（PLSR），主成分回归（PCR）和多元线性回归（MLR）输入数据的能力。最后，⁺，氯^-和SO ₄ ^{2 -}利用最佳的输入数据和建模方法建立土壤中的土壤成分，并绘制土壤成分含量的空间分布图。结果表明，当SSC较低时，从卫星高光谱数据（AHSI）和实验室光谱数据（ASD）获得的土壤光谱是一致的，并且随着SSC的增加，ASD数据的光谱曲线几乎没有变化AHSI数据显示出更明显的特征，并且这种变化在AHSI图像中表现为具有较低SSC的较暗像素和具有较高SSC的较亮像素。AHSI数据显示出对SSC变化的强烈反应；因此，在估算土壤含盐量方面，AHSI数据比ASD数据具有更大的优势。在建模过程中，RS在估算SSC和Na方面表现最佳。⁺含量，R ^2分别达到0.79和0.58，并获得较低的均方根误差（RMSE）值。FDA和PCA执行最好在估计氯^-和SO ₄ ^2-，而MLR在估计该区域的土壤成分的含量优于PLSR和PCR。此外，本研究中使用的高光谱相机数据具有很高的成本效益，可潜在地用于大范围和高精度地评估土壤盐碱化，从而减少与手持式单个样品采集相关的误差高光谱仪器。