基于实测高光谱与Sentinel-2B数据的银北土壤Na+含量估测

为了探讨不同传感器对土壤Na⁺含量的估测能力,本研究以宁夏银北地区典型样点土壤实测光谱和Sentinel-2B影像光谱为对象,运用逐步回归(SR)和主成分回归分析(PCA)方法对光谱数据进行敏感参量筛选,然后采用偏最小二乘回归(PLSR)、支持向量机(SVM)和反向传播神经网络模型(BPNN)分别建立实测光谱和影像数据的土壤Na⁺含量估算模型。结果表明: 除Band9外,实测重采样数据与影像数据呈极显著相关。基于SR筛选方式建立的模型估算精度普遍高于PCA(SVM模型除外),PCA-SVM模型为影像最佳Na⁺含量估算模型,预测精度为0.792;SR-BPNN模型为实测最佳Na⁺含量估算模型,预测精度达到0.908。经重采样实测光谱模型校正后的SR-PLSR影像光谱土壤Na⁺含量估算模型精度从0.481提高到0.798,有效提高了较大尺度下的土壤Na⁺含量估算精度。本研究实现了遥感监测土壤Na⁺含量由点向面的空间转换,为Sentinel-2B影像监测盐渍化土壤Na⁺含量提供了科学参考。

Estimation of soil Na content based on measured hyperspectral and Sentinel-2B data in northern Ningxia,China

To explore the ability of different sensors to estimate soil Na⁺content,we got the mea-sured soil spectra and Sentinel-2 B 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-2 B image to monitor Na⁺content in salinized soil.