基于小麦冠层无人机高光谱影像的农田土壤含水率估算  

Estimating soil moisture contents of farmland using UAV hyperspectral images of wheat canopy

作　　者：王梦迪 何莉 刘潜 李志娟 王冉 贾中甫 王敬哲 邬国峰 石铁柱[1] WANG Mengdi;HE Li;LIU Qian;LI Zhijuan;WANG Ran;JIA Zhongfu;WANG Jingzhe;WU Guofeng;SHI Tiezhu(MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area&Guangdong Key Laboratory of Urban Informatics&Shenzhen Key Laboratory of Spatial Smart Sensing and Services,Shenzhen University,Shenzhen 518060,China;College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen 518060,China;Inner Mongolia Autonomous Region Surveying,Mapping and Geoinformation Center,Hohhot 010020,China;School of Artificial Intelligence,Shenzhen Polytechnic,Shenzhen 518055,China)

机构地区：[1]深圳大学自然资源部大湾区地理环境监测重点实验室&广东省城市空间信息工程重点实验室&深圳市空间信息智能感知与服务重点实验室,深圳518060 [2]深圳大学机电与控制工程学院,深圳518060 [3]内蒙古自治区测绘地理信息中心,呼和浩特010020 [4]深圳职业技术学院人工智能学院,深圳518055

出　　处：《农业工程学报》2023年第6期120-129,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：深圳市科创委基础研究(重点项目)(No.20210324120209027);广东省教育厅重点领域专项项目(2020ZDZX1052)。

摘　　要：精准监测农田土壤含水率(soil moisture content,SMC)有助于提高中国水资源利用率以及农业可持续发展水平,为实现国家农业经济的稳定发展及可持续发展目标打下坚实的基础。为了探索基于无人机遥感数据进行准确、快速的土壤含水率监测的方法,该研究选取新疆阜康绿洲田块为研究区,使用无人机(unmanned aerial vehicle,UAV)高光谱传感器采集田块尺度小麦冠层光谱信息,进行SMC定量估算和制图。对小麦冠层光谱进行savitzky-golay(SG)平滑,利用7种不同的小波基函数(bior4.4、coif4、db4、fk14、haar、rbio3.9、sym4)对光谱信息进行连续小波变换(continuous wavelet transform,CWT)处理,并采用遗传算法(genetic algorithm, GA)对小波系数进行特征提取,最后结合偏最小二乘回归(partial least square regress,PLSR)、支持向量机(support vector machine,SVM)、人工神经网络(artificial neural network,ANN)、随机森林(radom forest,RF)以及极端梯度提升(extreme gradient boosting,XGBoost)估算SMC并实现其空间制图。结果表明:基于GA的特征波段选择方法可有效提高SMC的估算精度。使用全波段小波系数构建模型的决定系数R2在0.20~0.44之间,而使用特征小波系数的R2为0.25~0.82。与其他小波基函数相比,采用db4特征小波系数的估算精度最优,PLSR、SVM、ANN、RF和XGBoost模型估算SMC的R2分别为0.82、0.72、0.79、0.76和0.45。基于PLSR和ANN最优模型进行SMC空间制图,基于CWT和机器学习结合模型能够有效估算小田块尺度SMC。该研究基于无人机高光谱数据实现了SMC精确估算,为农田尺度SMC监测提供了有效手段。Accurate monitoring of soil moisture content(SMC)in agricultural fields can greatly contribute to the utilization of water resources and sustainable development.Low SMC can cause the soil to harden during crop growth,which in turn affects the absorption of the crops'water and nutrients.In this study,the field-scale wheat canopy spectra were gathered to quantitatively estimate and map the SMC using an unmanned aerial vehicle(UAV)hyperspectral sensor.The study area was selected as Fukang City,Xinjiang Uygur Autonomous Region,China(87°51'15′′E,44°21'14′′N)at the transition zone between a desert and an oasis.The soil samples were also collected concurrently with the hyperspectral data.70 sampling units of 0.5 m×0.5 m were evenly selected in the target field,where the GPS locations were recorded.The wheat canopy spectra were smoothed using the savitzky-golay(SG).The spectral data were then transformed using the continuous wavelet transform(CWT)with seven different wavelet functions(bior4.4,coif4,db4,fk14,haar,rbio3.9,and sym 4).The wavelet coefficients were extracted by the genetic algorithm(GA),and finally combined with partial least squares(PLSR),Support Vector Machine(SVM),artificial neural network(ANN),radom forest(RF),and extreme gradient boosting(XGBoost)to predict the SMC.A neural Network was used to predict the SMC,and then map the SMC at a spatial scale.The results demonstrate that the accuracy of SMC estimation greatly increased using the GA-based feature band selection.The determination coefficient(R2)of the feature band was 0.35-0.82 with the wavelet coefficients,while the R2 of the complete wavelet band ranged from 0.20 to 0.44.Furthermore,the R2 of SMC reached 0.82,0.72,0.79,0.76,and 0.46,respectively,using PLSR,SVM,ANN,RF,and XGBoost models.The best estimation accuracy was achieved in the feature band with the db4 wavelet coefficients,compared with the rest wavelet functions.The predicted and measured values were remarkably similar.The GA significantly reduced the number of variables to maxim

关 键 词：土壤含水率 无人机 高光谱 连续小波变换 遗传算法 

分 类 号：S159.3[农业科学—土壤学] S252[农业科学—农业基础科学]