College of Resources and Environmental Science, Ningxia University, Yinchuan 750021, China.
Institute of Environmental Engineering, Ningxia University, Yinchuan 750021, China.
Ying Yong Sheng Tai Xue Bao. 2021 Mar;32(3):1023-1032. doi: 10.13287/j.1001-9332.202103.018.
To explore the ability of different sensors to estimate soil Na content, we got the mea-sured soil spectra and Sentinel-2B image spectra of the typical soil samples from the northern area of Ningxia. We filtered the sensitive parameters from the spectra data by means of stepwise regression (SR) and principal component regression analysis (PCA). We established the models to estimate soil Na content based on the measured spectra and image data using partial least square regression (PLSR), support vector machine (SVM) and back propagation neural network model (BPNN). The results showed that, except for Band9, there was significant correlation between the resampling data and the image data. The estimation accuracy of models based on SR-screening was generally higher than the PCA (excluding SVM model). The PCA-SVM model was the best image estimation model for soil Na content, with a prediction accuracy of 0.792. The SR-BPNN model was the best measured estimation model, with a prediction accuracy of 0.908. The estimating accuracy of the SR-PLSR image-spectra-based model increased from 0.481 to 0.798 after calibrated by the resampled measured spectrum model, which effectively enhanced the accuracy in estimating the soil Na content at large scale. We successfully made the spatial transformation of soil Na content from point to surface. Our results provided a scientific reference for Sentinel-2B image to monitor Na content in salinized soil.
为了探索不同传感器对土壤 Na 含量的估算能力，我们获取了宁夏北部地区典型土壤样本的实测土壤光谱和 Sentinel-2B 图像光谱。通过逐步回归（SR）和主成分回归分析（PCA），从光谱数据中筛选出敏感参数。基于实测光谱和图像数据，利用偏最小二乘回归（PLSR）、支持向量机（SVM）和反向传播神经网络模型（BPNN），建立了估算土壤 Na 含量的模型。结果表明，除了波段 9 之外，重采样数据与图像数据之间存在显著相关性。基于 SR 筛选的模型的估计精度普遍高于 PCA（不包括 SVM 模型）。PCA-SVM 模型是土壤 Na 含量的最佳图像估算模型，预测精度为 0.792。SR-BPNN 模型是最佳的实测估算模型，预测精度为 0.908。通过重采样实测光谱模型对图像光谱基 SR-PLSR 模型进行校准后，其估算精度从 0.481 提高到 0.798，有效提高了大尺度估算土壤 Na 含量的精度。我们成功地实现了土壤 Na 含量从点到面的空间转换。我们的研究结果为 Sentinel-2B 图像监测盐渍化土壤中 Na 含量提供了科学参考。
