张智韬,王海峰,韩文霆,边江,陈硕博,崔婷.基于无人机多光谱遥感的土壤含水率反演研究[J].农业机械学报,2018,49(2):173-181.
ZHANG Zhitao,WANG Haifeng,HAN Wenting,BIAN Jiang,CHEN Shuobo,CUI Ting.Inversion of Soil Moisture Content Based on Multispectral Remote Sensing of UAVs[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):173-181.
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基于无人机多光谱遥感的土壤含水率反演研究   [下载全文]
Inversion of Soil Moisture Content Based on Multispectral Remote Sensing of UAVs   [Download Pdf][in English]
投稿时间:2017-06-21  
DOI:10.6041/j.issn.1000-1298.2018.02.023
中文关键词:  土壤含水率  多光谱遥感  无人机  回归分析  逐步回归
基金项目:国家重点研发计划项目(2016YFD0200700、2017YFC0403302)、中国博士后科学基金项目(2015M570855)和中央高校基本科研业务费专项资金项目(2452016072)
作者单位
张智韬 西北农林科技大学 
王海峰 西北农林科技大学 
韩文霆 西北农林科技大学 
边江 西北农林科技大学 
陈硕博 西北农林科技大学 
崔婷 西北农林科技大学 
中文摘要:为研究无人机多光谱遥感技术对裸土土壤含水率的大范围快速测定和最佳监测深度的确定,以杨凌地区粘壤土为试验材料,分别配制成2种不同深度(5cm和10cm)、含水率为3%~30%的土壤样本。用无人机搭载多光谱相机对土样连续监测3d,监测时刻均为15:00。采集6个波段(490、550、680、720、800、900nm)处的土壤光谱反射率,同时对2种不同深度的土壤样本表层(约1cm)含水率和整体含水率进行测定。分别采用偏最小二乘回归法、逐步回归法和岭回归法,建立不同波段光谱反射率因素反演土壤含水率的回归模型,并分析其定量关系。试验结果表明,逐步回归预测精度最佳,决定系数(R2)分别为0.775、0.764、0.798、0.694,而预测均方根误差(RMSE)分别为0.028、0.042、0.037、0.038;其次为岭回归法;偏最小二乘法的预测精度最低。综合比较得最佳回归方法为逐步回归法,最佳监测深度为土壤表层(约1cm),其次为5cm深度,最后为10cm深度。
ZHANG Zhitao  WANG Haifeng  HAN Wenting  BIAN Jiang  CHEN Shuobo  CUI Ting
Northwest A&F University,Northwest A&F University,Northwest A&F University,Northwest A&F University,Northwest A&F University and Northwest A&F University
Key Words:soil moisture content  multispectral remote sensing  UAV  regression analysis  stepwise regression
Abstract:To get the soil moisture of the large scale rapidly and the best monitoring depth in bare soil by UAV multispectral remote sensing technology, the clay loam soil was prepared into two different depths (5cm and 10cm) and the soil moisture ranged from 3% to 30% of the different samples. The UAV was equipped with a Micro-MCA multispectral camera to monitor the soil samples at 3 p.m. for three consecutive days. The soil spectral reflectance values of six bands (490nm, 550nm, 680nm, 720nm, 800nm and 900nm) were collected. The surface moisture content (about 1cm) and overall moisture content of soil samples of two different depths were also measured. The regression models between soil moisture and the reflectance of different bands were established by the regression methods of partial least squares regression, stepwise regression and ridge regression. Quantitative relationship was analyzed of the regression modes and the methods. The results showed that the three regression models had statistical significance (P<0.001) for predicting soil moisture content. The accuracy evaluation of the model through the validation set showed that the stepwise regression model had good prediction ability (R2 were 0.775, 0.764, 0.798 and 0.694, RMSE were 0.028, 0.042, 0.037 and 0.038 and RPD were 2.22, 2.04, 2.20 and 1.75), followed by ridge regression method and partial least squares method. The regression models of the surface soil had good inversion effect in monitoring depth. The inversion effect was decreased as the increase of monitoring depth. The relationship between the soil moisture and the wavelength of 720nm, 680nm and 550nm band was better among the six bands. The results showed that the best regression method was stepwise regression method, and the best monitoring depth was the surface layer (about 1cm) of the soil samples. The research result can provide reference for the rapid monitoring of soil moisture in the area by using multispectral remote sensing of UAVs, and promote the further development of precision agriculture.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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