Monitoring of Farmland Soil Moisture Based on Unmanned Aerial Vehicle Multispectral Data

FENG Shanshan; LIANG Xueying; FAN Fenglei; WANG Sai; WU Jianheng

doi:10.6054/j.jscnun.2020098

Journal of South China Normal University (Natural Science Edition) > 2020 > 52(6): 74-81. > DOI: 10.6054/j.jscnun.2020098

FENG Shanshan, LIANG Xueying, FAN Fenglei, WANG Sai, WU Jianheng. Monitoring of Farmland Soil Moisture Based on Unmanned Aerial Vehicle Multispectral Data[J]. Journal of South China Normal University (Natural Science Edition), 2020, 52(6): 74-81. DOI: 10.6054/j.jscnun.2020098

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Monitoring of Farmland Soil Moisture Based on Unmanned Aerial Vehicle Multispectral Data

1.
School of Geography, South China Normal University, Guangzhou 510631, China
2.
Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental Science and Technology, Guangzhou 510650, China
3.
Joint Laboratory of Plateau Surface Remote Sensing, Tibet University, Lhasa 850000, China

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Received Date: March 31, 2020
Available Online: January 04, 2021

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Abstract

Abstract

In order to improve the efficiency of precision farmland management, a method to quickly monitor the soil moisture content of farmland is designed, based on remote sensing data transmitted and obtained in real time with unmanned aerial vehicle (UAV). Firstly, multispectral images of farmland were obtained through UAV and a representative area of farmland was selected to detect the soil moisture content of random samples. Then, the perpendicular drought index (PDI) method, combined with the soil moisture data of samples, were used to construct an inversion model of soil moisture, with which the data of the soil moisture of a wide range of farmland were finally obtained. UAV images and soil moisture data in 6 different periods were used for the inversion accuracy analysis and the method validation. The results showed that this method is of high precision in soil moisture monitoring, with the coefficient of determination (R²) being more than 0.8 in 6 periods and the root mean square error (RMSE) and the systematic error (SE) being both less than 0.1 in 5 periods. This method of soil moisture monitoring based on UAV data had its validity and feasibility proved and could provide reference for rapid monitoring of large-scale farmland soil moisture.
- soil moisture,
- unmanned aerial vehicle(UAV),
- multispectral remote sensing,
- perpendicular drought index(PDI)

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References (21)

References

[1]	MORAN M S, HYMER D C, QI J, et al. Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions[J]. Remote Sensing of Environment, 2002, 79(2/3):243-252. http://www.sciencedirect.com/science/article/pii/S0034425701002760
[2]	KUMAR S V, DIRMEYER P A, PETERS-LIDARD C D, et al. Information theoretic evaluation of satellite soil moisture retrievals[J]. Remote Sensing of Environment, 2018, 204:392-400. http://www.sciencedirect.com/science/article/pii/S0034425717304789
[3]	伍漫春, 丁建丽, 王高峰.基于地表温度-植被指数特征空间的区域土壤水分反演[J].中国沙漠, 2012, 32(1):148-154. http://d.wanfangdata.com.cn/Periodical/zgsm201201023 WU M C, DING J L, WANG G F. Regional soil moisture inversion based on surface temperature and vegetation index characteristic spaces[J]. Journal of Desert Research, 2012, 32(1):148-154. http://d.wanfangdata.com.cn/Periodical/zgsm201201023
[4]	汝博文, 缴锡云, 王耀飞, 等.基于MODIS数据的土壤水分空间变异规律[J].中国农村水利水电, 2016(4):38-42. http://d.wanfangdata.com.cn/Periodical/zgncslsd201604010 RU B W, JIAO X Y, WANG Y F, et al. Spatial variability of soil moisture content based in MODIS data[J]. China Rural Water and Hydropower, 2016(4):38-42. http://d.wanfangdata.com.cn/Periodical/zgncslsd201604010
[5]	仝兆远, 张万昌.土壤水分遥感监测的研究进展[J].水土保持通报, 2007(4):107-113. http://www.cqvip.com/Main/Detail.aspx?id=25293937 TONG Z Y, ZHANG W C. Progress of soil moisture monitoring by remote sensing[J]. Bulletin of Soil and Water Conservation, 2007(4):107-113. http://www.cqvip.com/Main/Detail.aspx?id=25293937
[6]	吴黎, 张有智, 解文欢, 等.土壤水分的遥感监测方法概述[J].国土资源遥感, 2014, 26(2):19-26. http://www.cnki.com.cn/Article/CJFDTotal-GTYG201402004.htm WU L, ZHANG Y Z, XIE W H, et al. Summary of remote sensing methods for monitoring soil moisture[J]. Remote Sensing for Land & Resources, 2014, 26(2):19-26. http://www.cnki.com.cn/Article/CJFDTotal-GTYG201402004.htm
[7]	IDSO S B, JACKSON R D, PINTER P J, et al. Normalizing the stress-degree-day parameter for environmental variability[J]. Agricultural Meteorology, 1981, 24(1):45-55. http://www.sciencedirect.com/science/article/pii/0002157181900327
[8]	JACKSON R D, IDSO S B, REGINATO R J, et al. Canopy temperature as a crop water stress indicator[J]. Water Resources Research, 1981, 17(4):1133-1138. doi: 10.1029/WR017i004p01133/pdf
[9]	MORAN M S, CLARKE T R, INOUE Y, et al. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994, 49(3):246-263.
[10]	CARLSON T N, GILLIES R R, PERRY E M. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover[J]. Remote Sensing Reviews, 1994, 9(1):161-173. doi: 10.1080/02757259409532220
[11]	ULABY F T, ASLAM A, DOBSON M C. Effects of vegetation cover on the radar sensitivity to soil moisture[J]. IEEE Transactions on Geoscience & Remote Sensing, 1982, 20(4):476-481. http://ieeexplore.ieee.org/document/4157335
[12]	李杏朝.微波遥感监测土壤水分的研究初探[J].遥感技术与应用, 1995(4):1-8. http://www.cnki.com.cn/Article/CJFDTotal-YGJS504.000.htm LI X C. A study on soil moisture monitoring by using microwave remote sensing[J]. Remote Sensing Technology & Application, 1995(4):1-8. http://www.cnki.com.cn/Article/CJFDTotal-YGJS504.000.htm
[13]	GHULAM A, QIN Q, ZHAN Z. Designing of the perpendicular drought index[J]. Environmental Geology, 2007, 52(6):1045-1052. doi: 10.1007/s00254-006-0544-2
[14]	詹志明, 秦其明, 阿布都瓦斯提·吾拉木, 等.基于NIR-Red光谱特征空间的土壤水分监测新方法[J].中国科学:D辑, 2006(11):1020-1026. http://www.cnki.com.cn/Article/CJFDTotal-JDXK200611004.htm ZHAN Z M, QIN Q M, GHULAM A, et al. A new method of soil moisture monitoring based on NIR-Red spectral feature space[J]. Science in China:Series D, 2006(11):1020-1026. http://www.cnki.com.cn/Article/CJFDTotal-JDXK200611004.htm
[15]	陈国茜, 周秉荣, 胡爱军.垂直干旱指数在高寒农区春旱监测中的应用研究[J].遥感技术与应用, 2014, 29(6):949-953. http://d.wanfangdata.com.cn/Periodical/ygjsyyy201406008 CHEN G X, ZHOU B R, HU A J. Application research of perpendicular drought index for spring drought monitoring in high and cold agricultural area[J]. Remote Sensing Technology & Application, 2014, 29(6):949-953. http://d.wanfangdata.com.cn/Periodical/ygjsyyy201406008
[16]	鹿国武, 尚建设, 周清, 等.基于高分一号数据的干旱监测方法研究[J].能源与环保, 2020, 42(1):63-66. http://www.ixueshu.com/document/2e1c32172b661dd8ebf9dfd2fc35f29d318947a18e7f9386.html LU G W, SHANG J S, ZHOU Q, et al. Research on drought monitoring method based on data of GF-1[J]. China Energy and Environmental Protection, 2020, 42(1):63-66. http://www.ixueshu.com/document/2e1c32172b661dd8ebf9dfd2fc35f29d318947a18e7f9386.html
[17]	张智韬, 王海峰, 韩文霆, 等.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. http://d.wanfangdata.com.cn/periodical/nyjxxb201802023 ZHANG Z T, WANG H F, HAN W T, et al. Inversion of soil moisture content based on multispectral remote sen-sing of UAVs[J]. Transactions of the Chinese Society of Agricultural Machinery, 2018, 49(2):173-181. http://d.wanfangdata.com.cn/periodical/nyjxxb201802023
[18]	陈震, 马春芽, 孙浩, 等.基于无人机影像的作物土壤水分亏缺反演方法[J].中国农业信息, 2019, 31(4):23-35. http://d.wanfangdata.com.cn/periodical/nyxxts201904003 CHEN Z, MA C Y, SUN H, et al. The inversion methods for water stress of irrigation crop based on unmanned aerial vehicle remote sensing[J]. China Agricultural Informa-tics, 2019, 31(4):23-35. http://d.wanfangdata.com.cn/periodical/nyxxts201904003
[19]	葛翔宇, 丁建丽, 王敬哲, 等.一种基于无人机高光谱影像的土壤墒情检测新方法[J].光谱学与光谱分析, 2020, 40(2):602-609. http://d.wanfangdata.com.cn/periodical/gpxygpfx202002046 GE X Y, DING J L, WANG J Z, et al. A new method for predicting soil moisture based on UAV hyperspectral ima-ge[J]. Spectroscopy and Spectral Analysis, 2020, 40(2):602-609. http://d.wanfangdata.com.cn/periodical/gpxygpfx202002046
[20]	DEERING D W. Measuring "forage production" of gra-zing units from Landsat MSS data[C]//Proceedings of the 10th International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1975: 1169-1178.
[21]	赵英时.遥感应用分析原理与方法[M].北京:科学出版社, 2003.

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Monitoring of Farmland Soil Moisture Based on Unmanned Aerial Vehicle Multispectral Data

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