Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle

doi:10.3969/j.issn.1004-1524.2022.06.20

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (6): 1297-1305.DOI: 10.3969/j.issn.1004-1524.2022.06.20

• Biosystms Engineening • Previous Articles Next Articles

Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle

WANG Jun¹^,²(), LU Zhou², LUO Ming², XU Feifei², ZHANG Xu¹^,^*()

1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China
2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2021-07-15 Online:2022-06-25 Published:2022-06-30
Contact: ZHANG Xu

Abstract

Abstract:

Accurate and quick determination of soil moisture of winter wheat plot could provide references for efficient water utilization and precision irrigation. In this study, the multispectral remote sensing data of winter wheat at turning green period were obtained by unmanned aerial vehicle in Zhangjiagang City, Jiangsu Province. The soil moisture at two depths (10 cm and 20 cm) was measured simultaneously. Spectral reflectance was extracted from remote sensing images, and normalized difference vegetation index, enhanced vegetation index, perpendicular drought index were calculated. After collinearity analysis, stepwise regression, ridge regression and partial least squares regression methods were used to construct soil moisture content inversion models at different soil depths, then the optimal inversion model was adopted to draw soil moisture inversion maps. The results showed that the determination coefficient of models constructed by the stepwise regression method for 10, 20 cm soil depths were 0.885 and 0.782, respectively, of which the inversion precision was the highest. The inversion performance for 10 cm soil depth of all the constructed models were better than that of the 20 cm soil depth. The present study could provide references for the selection of soil moisture monitoring of winter wheat at turning green period.

Key words: soil moisture, unmanned aerial vehicle, multispectral remote sensing, inversion

CLC Number:

S152.7

WANG Jun, LU Zhou, LUO Ming, XU Feifei, ZHANG Xu. Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle[J]. Acta Agriculturae Zhejiangensis, 2022, 34(6): 1297-1305.

Figures/Tables 8

References 25

[1]	潘宁, 王帅, 刘焱序, 等. 土壤水分遥感反演研究进展[J]. 生态学报, 2019, 39(13): 4615-4626.
	PAN N, WANG S, LIU Y X, et al. Advances in soil moisture retrieval from remote sensing[J]. Acta Ecologica Sinica, 2019, 39(13): 4615-4626. (in Chinese with English abstract)
[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. DOI URL
[3]	吴巍, 王高旭, 吴永祥, 等. 农田灌溉用水量客观测算模型数据库研究[J]. 水利信息化, 2021(2): 24-28.
	WU W, WANG G X, WU Y X, et al. Research on objective calculating model database of farmland irrigation water consumption[J]. Water Resources Informatization, 2021(2): 24-28. (in Chinese with English abstract)
[4]	杨珺博, 王斌, 黄嘉亮, 等. 无人机多光谱遥感监测冬小麦拔节期根域土壤含水率[J]. 节水灌溉, 2019(10): 6-10.
	YANG J B, WANG B, HUANG J L, et al. Monitoring soil moisture content in root zone of winter wheat at jointing stage by multispectral remote sensing of UAV[J]. Water Saving Irrigation, 2019(10): 6-10. (in Chinese with English abstract)
[5]	孙越君, 郑小坡, 秦其明, 等. 不同质量含水量的土壤反射率光谱模拟模型[J]. 光谱学与光谱分析, 2015, 35(8): 2236-2240.
	SUN Y J, ZHENG X P, QIN Q M, et al. Modeling soil spectral reflectance with different mass moisture content[J]. Spectroscopy and Spectral Analysis, 2015, 35(8): 2236-2240. (in Chinese with English abstract)
[6]	吕友, 陈能成, 陈泽强. GF-1垂直干旱指数的土壤湿度空间格局分析: 以秭归县为例[J]. 测绘科学, 2018, 43(4): 94-99.
	LÜ Y, CHEN N C, CHEN Z Q. Spatial pattern analysis of soil moisture based on PDI of GF-1: taking Zigui County as an example[J]. Science of Surveying and Mapping, 2018, 43(4): 94-99. (in Chinese with English abstract)
[7]	DONG J Z, STEELE-DUNNE S C, OCHSNER T E, et al. Estimating soil moisture and soil thermal and hydraulic properties by assimilating soil temperatures using a particle batch smoother[J]. Advances in Water Resources, 2016, 91: 104-116. DOI URL
[8]	LIU C Z, SHI J C. Estimation of vegetation parameters of water cloud model for global soil moisture retrieval using time-series L-band aquarius observations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12): 5621-5633. DOI URL
[9]	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.
	ZHANG Z T, WANG H F, HAN W T, et al. 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. (in Chinese with English abstract)
[10]	李鑫星, 朱晨光, 傅泽田, 等. 基于无人机多光谱图像的土壤水分检测方法研究[J]. 光谱学与光谱分析, 2020, 40(4): 1238-1242.
	LI X X, ZHU C G, FU Z T, et al. Rapid detection of soil moisture content based on UAV multispectral image[J]. Spectroscopy and Spectral Analysis, 2020, 40(4): 1238-1242. (in Chinese with English abstract)
[11]	冯珊珊, 梁雪映, 樊风雷, 等. 基于无人机多光谱数据的农田土壤水分遥感监测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 74-81.
	FENG S S, LIANG X Y, FAN F L, et al. 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. (in Chinese with English abstract)
[12]	ROMERO M, LUO Y C, SU B F, et al. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management[J]. Computers and Electronics in Agriculture, 2018, 147: 109-117. DOI URL
[13]	VERRELST J, SCHAEPMAN M E, KOETZ B, et al. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data[J]. Remote Sensing of Environment, 2008, 112(5): 2341-2353. DOI URL
[14]	周晓红, 张飞, 张海威, 等. 艾比湖湿地自然保护区土壤盐分多光谱遥感反演模型[J]. 光谱学与光谱分析, 2019, 39(4): 1229-1235.
	ZHOU X H, ZHANG F, ZHANG H W, et al. A study of soil salinity inversion based on multispectral remote sensing index in Ebinur Lake wetland nature reserve[J]. Spectroscopy and Spectral Analysis, 2019, 39(4): 1229-1235. (in Chinese with English abstract)
[15]	王海峰, 张智韬, 付秋萍, 等. 低空无人机多光谱遥感数据的土壤含水率反演[J]. 节水灌溉, 2018(1): 90-94.
	WANG H F, ZHANG Z T, FU Q P, et al. Inversion of soil moisture content based on multispectral remote sensing data of low altitude UAV[J]. Water Saving Irrigation, 2018(1): 90-94. (in Chinese with English abstract)
[16]	刘国旗. 多重共线性的产生原因及其诊断处理[J]. 合肥工业大学学报(自然科学版), 2001, 24(4): 607-610.
	LIU G Q. Cause of multi-collinearity and its diagnosis and treatment[J]. Journal of Hefei University of Technology (Natural Science), 2001, 24(4): 607-610. (in Chinese with English abstract)
[17]	钱家炜, 刘晓青, 张静静, 等. 张家港市农田土壤重金属含量高光谱遥感监测模型构建[J]. 浙江农业学报, 2020, 32(8): 1437-1445.
	QIAN J W, LIU X Q, ZHANG J J, et al. Constructions of hyperspectral remote sensing monitoring models for heavy metal contents in farmland soil in Zhangjiagang City[J]. Acta Agriculturae Zhejiangensis, 2020, 32(8): 1437-1445. (in Chinese with English abstract)
[18]	IMANI M, GHASSEMIAN H. Ridge regression-based feature extraction for hyperspectral data[J]. International Journal of Remote Sensing, 2015, 36(6): 1728-1742. DOI URL
[19]	NANNI M R, CEZAR E, DA SILVA JUNIOR C A, et al. Partial least squares regression (PLSR) associated with spectral response to predict soil attributes in transitional lithologies[J]. Archives of Agronomy and Soil Science, 2018, 64(5): 682-695. DOI URL
[20]	王国华, 张虎, 魏岳嵩. 偏最小二乘回归在SPSS软件中的实现[J]. 统计与决策, 2017(7): 67-71.
	WANG G H, ZHANG H, WEI Y S. Implementation of partial least squares regression in SPSS software[J]. Statistics & Decision, 2017(7): 67-71. (in Chinese with English abstract)
[21]	谭丞轩, 张智韬, 许崇豪, 等. 无人机多光谱遥感反演各生育期玉米根域土壤含水率[J]. 农业工程学报, 2020, 36(10): 63-74.
	TAN C X, ZHANG Z T, XU C H, et al. Soil water content inversion model in field maize root zone based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(10): 63-74. (in Chinese with English abstract)
[22]	缪闯和, 吕贻忠. 黑土、潮土和红壤可溶性有机质的光谱特征及结构差异[J]. 土壤, 2021, 53(1): 168-172.
	MIAO C H, LÜ Y Z. Spectral characteristics and structural differences of DOM in black soil, fluvo-aquic soil and red soil[J]. Soils, 2021, 53(1): 168-172. (in Chinese with English abstract)
[23]	郭晗, 张序, 陆洲, 等. 基于机载高光谱影像的南方水稻土有机质含量估算[J]. 中国农业科技导报, 2020, 22(6): 60-71.
	GUO H, ZHANG X, LU Z, et al. Estimation of organic matter content in southern paddy soil based on airborne hyperspectral images[J]. Journal of Agricultural Science and Technology, 2020, 22(6): 60-71. (in Chinese with English abstract)
[24]	MUSTAFA A, XU M G, ALI SHAH S A, et al. Soil aggregation and soil aggregate stability regulate organic carbon and nitrogen storage in a red soil of Southern China[J]. Journal of Environmental Management, 2020, 270: 110894. DOI URL
[25]	韩晓增, 邹文秀. 东北黑土地保护利用研究足迹与科技研发展望[J]. 土壤学报, 2021, 58(6): 1341-1358.
	HAN X Z, ZOU W X. Research perspectives and footprint of utilization and protection of black soil in northeast China[J]. Acta Pedologica Sinica, 2021, 58(6): 1341-1358. (in Chinese with English abstract)

深度 Depth/cm	样本集 Dataset	样本数量 Sample size	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	变异系数 Coefficient of variation
10	总集Whole set	39	0.347	0.456	0.393	0.024	0.060
	建模集Modeling set	30	0.347	0.456	0.394	0.026	0.065
	检验集Verification set	9	0.363	0.412	0.392	0.016	0.041
20	总集Whole set	39	0.368	0.507	0.476	0.048	0.101
	建模集Modeling set	30	0.403	0.507	0.489	0.044	0.091
	检验集Verification set	9	0.368	0.504	0.440	0.038	0.087

深度 Depth/cm	样本集 Dataset	样本数量 Sample size	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	变异系数 Coefficient of variation
10	总集Whole set	39	0.347	0.456	0.393	0.024	0.060
	建模集Modeling set	30	0.347	0.456	0.394	0.026	0.065
	检验集Verification set	9	0.363	0.412	0.392	0.016	0.041
20	总集Whole set	39	0.368	0.507	0.476	0.048	0.101
	建模集Modeling set	30	0.403	0.507	0.489	0.044	0.091
	检验集Verification set	9	0.368	0.504	0.440	0.038	0.087

指标Index	VIF	指标Index	VIF
蓝Blue	35.935	近红外Near-infrared	46.548
绿Green	27.135	NDVI	101.345
红Red	24.573	EVI	99.657
红边Red edge	48.147	PDI	34.640

指标Index	VIF	指标Index	VIF
蓝Blue	35.935	近红外Near-infrared	46.548
绿Green	27.135	NDVI	101.345
红Red	24.573	EVI	99.657
红边Red edge	48.147	PDI	34.640

建模方法 Modeling method	深度 Depth/cm	建模结果 Modeling result	R²	RMSE
逐步回归法 Stepwise regression	10	Y=-0.396B₁-0.232B₂-0.068N+0.508	0.885	0.009
逐步回归法 Stepwise regression	20	Y=-2.695B₂+0.960B₅+1.019E-1.072P+1.298	0.782	0.016
岭回归法 Ridge regression	10	Y=0.021B₁-0.084B₂-0.301B₃-0.252B₄-0.086B₅-0.063N+0.064E-0.255P+0.428	0.762	0.013
岭回归法 Ridge regression	20	Y=-0.125B₁-0.244B₂+0.068B₃-0.330B₄-0.009B₅+0.075N-0.101E-0.304P +0.871	0.668	0.023
偏最小二乘法 Partial least squares regression	10	Y=0.076B₁-0.293B₂-0.255B₃-0.179B₄+0.204B₅-0.755N+0.983E-0.151P +0.643	0.838	0.009
偏最小二乘法 Partial least squares regression	20	Y=-1.085B₁-0.101B₂-0.400B₃-0.514B₄+0.803B₅-0.557N-0.034E-0.339P +0.938	0.737	0.020

Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 25

Related Articles 12

Recommended Articles

Metrics

Comments

建模方法 Modeling method	深度 Depth/cm	R²	RMSE	RPD
逐步回归法 Stepwise regression	10	0.899	0.005	3.091
逐步回归法 Stepwise regression	20	0.865	0.016	2.220
岭回归法 Ridge regression	10	0.843	0.008	2.020
岭回归法 Ridge regression	20	0.814	0.020	1.095
偏最小二乘法 Partial least squares regression	10	0.856	0.008	2.142
偏最小二乘法 Partial least squares regression	20	0.831	0.017	2.136

[1]	WU Ningshan, WANG Jiaxi, ZHANG Yan, YUAN Mutian, ZHANG Qi, GAO Chiyu. Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: a case study from Caijiachuan watershed [J]. Acta Agriculturae Zhejiangensis, 2021, 33(8): 1505-1518.
[2]	CHENG Jing, LIU Jiming, WANG Shu, WANG Deng, LI Lixia, XU Guorui, CHEN Meng, HUANG Luting. Plasticity of a karst endemic plant Juglans regia L. f. luodianense Liu et Xu in response to soil moisture [J]. Acta Agriculturae Zhejiangensis, 2021, 33(2): 259-269.
[3]	YIN Linjiang, ZHOU Zhongfa, HUANG Denghong, SHANG Mengjia. Extraction of individual plant of pitaya in Karst Canyon Area based on point cloud data of UAV image matching [J]. , 2020, 32(6): 1092-1102.
[4]	LIANG Changjiang, WU Xuemei, WANG Fang, SONG Zhujun, ZHANG Fugui. Research on recognition algorithm of field mulch film based on unmanned aerial vehicle [J]. , 2019, 31(6): 1005-1011.
[5]	WANG Chunmei, GU Xingfa, YU Tao, ZHOU Xiang, ZHAN Yulin, HAN Leran, XIE Qiuxia. Recent advances of validation methods in passive microwave remote sensing of soil moisture products [J]. , 2019, 31(5): 846-854.
[6]	MA Chi. Comparison on inversion accuracy of soil organic matter in surface layer based on GF-1 and Landsat 8 remote sensing data [J]. , 2017, 29(1): 137-143.
[7]	YUAN Huifang1， XIAO Rongcai1， HUANG Jing1， LI Yueying2， YUE Hai1， TIAN Yaohua1，*. Physiological responses of Citrus maxima cv. ‘Dongshizao’ to different waterretention measures and comprehensive evaluation [J]. , 2016, 28(4): 586-.
[8]	RAO Yuan, XU Wen-jun, JIANG Zhao-hui, LAZAROVITCH Naftali, LI Shao-wen. Parametric optimization of unsaturated soil hydraulic movement model [J]. , 2016, 28(12): 2082-2089.
[9]	YANG Hong, XU Chang-chang, SAI Man, CAO Jian-ting, CAO Li-hua, ZHANG Ai-qin, LIU He-man. Effects of land use on soil moisture, pH and electrical conductivity [J]. , 2016, 28(11): 1922-1927.
[10]	ZHENG Zhi1，2，LIU Chen2，FU Qing\\|lin2，*，GUO Bin2，LIN Yi\\|cheng2，DING Neng\\|fei2. Effects of salinity and soil moisture on microbial composition and community diversity in a coastal saline soil [J]. , 2015, 27(2): 240-.
[11]	HU Yong\\|guang;MAO Kang\\|qian;ZHU Xiao\\|lan. Temperature variation of underlying surface of tea plantation under late frost [J]. , 2013, 25(6): 0-1242.
[12]	LU Xing-hua;WU Liang-huan;;PANG Lin-jiang. Effects of soil moisture under plastic film mulching on the biological and yield characteristics of rice （Oryza sativa* L.） [J]. , 2009, 21(05): 0-467.