Wei Guangfei, Li Yu, Zhang Zhitao, Chen Yinwen, Chen Junying, Yao Zhihua, Lao Congcong, Chen Huifang
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China.
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, China.
PeerJ. 2020 Apr 28;8:e9087. doi: 10.7717/peerj.9087. eCollection 2020.
Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0-20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination ( ), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy ( = 0.835, = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.
土壤盐渍化是一个与社会经济可持续发展密切相关的全球性问题。与常用的卫星传感器相比，配备多光谱传感器的无人机为以按需提供的高空间和时间分辨率监测土壤盐渍化提供了契机。本研究旨在利用无人机搭载的多光谱影像定量估算土壤盐分含量（SSC），并探索多光谱数据的深度挖掘。为此，在中国内蒙古的沙壕渠灌区共采集了60个土壤样本（0 - 20厘米）。同时，从无人机传感器获取多光谱数据，在此基础上构建了22个光谱协变量（6个光谱波段和16个光谱指数）。通过灰色关联分析（GRA）、连续投影算法（SPA）和投影变量重要性（VIP）选择敏感光谱协变量，并分别使用反向传播神经网络（BPNN）回归、支持向量回归（SVR）和随机森林（RF）回归从这些选定的协变量构建估算模型。通过决定系数（ ）、均方根误差（RMSE）和性能与偏差比（RPD）评估模型性能。结果表明，使用三种变量选择方法后模型的估算精度显著提高，且VIP优于GRA，GRA优于SPA。然而，三种机器学习算法的模型精度差异显著：RF > SVR > BPNN。所有12个SSC估算模型均可用于定量估算SSC（RPD > 1.4），而VIP - RF模型精度最高（ = 0.835， = 0.812，RPD = 2.299）。本研究结果证明无人机搭载的多光谱传感器是估算SSC的可行仪器，并为进一步的类似研究提供了参考。
