Inverting the water stress index of the Brassica chinensis using multiple-spectral and meteorological parameters

Wang Weixing; Yang Mingxin; Gao Peng; Xie Jiaxing; Sun Daozong; Cao Yapeng; Luo Runmei; Lan Yuyang

doi:10.11975/j.issn.1002-6819.2022.06.018

Volume 38 Issue 6

Mar. 2022

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Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2022 > 38(6): 157-164. > DOI: 10.11975/j.issn.1002-6819.2022.06.018

Wang Weixing, Yang Mingxin, Gao Peng, Xie Jiaxing, Sun Daozong, Cao Yapeng, Luo Runmei, Lan Yuyang. Inverting the water stress index of the Brassica chinensis using multiple-spectral and meteorological parameters[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(6): 157-164. DOI: 10.11975/j.issn.1002-6819.2022.06.018

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Inverting the water stress index of the Brassica chinensis using multiple-spectral and meteorological parameters

1.
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
2.
Engineering Research Center for Monitoring Agricultural Information of Guangdong Province, Guangzhou 510642, China

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Received Date: December 24, 2021
Revised Date: February 25, 2022
Published Date: March 30, 2022

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Abstract

Abstract

Abstract: Monitoring the Crop Water Stress Index (CWSI) is of great significance for the water status and irrigation in crop production. Taking the Brassica chinensis as the test object, this study aims to measure the canopy temperature under different soil moisture conditions. Some meteorological parameters were collected, including the air temperature, relative humidity, wind speed, and photosynthetic active radiation. Meanwhile, the images of spectral reflectance were also collected for the four bands (450, 650, 808, and 940nm). Four vegetation indexes were then calculated by the canopy spectral reflectance, including the Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Re-Difference Vegetation Index (RDVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Support Vector Regression (SVR) was selected to construct the inversion models of the CWSI upper/lower baseline using the meteorological parameters, and the inversion models of the canopy temperature using the vegetation index. The results showed that the canopy spectral reflectance at 450 and 650 nm for the Brassica chinensis ranged from 0 to 0.1, while the relatively higher one at 808 and 940 nm ranged from 0.4 to 0.6. The reflectance at 808 and 940 nm increased outstandingly, when the Brassica chinensis was developed gradually from the vegetative to reproductive growth stage. The vegetation index reflected the growth state and vegetation coverage of the Brassica chinensis. There was a different response of vegetation indexes to the canopy temperature. The vegetation NDVI, DVI and RDVI increased, while the vegetation OSAVI decreased with the increase of the canopy temperature of the Brassica chinensis. The vegetation index under the same water treatment was slightly different in the various growth stages. Specifically, the range of the vegetation index in the reproductive growth stage was smaller than that in the vegetative growth stage. The error analysis showed that the inversion models were feasible to monitor the air temperature, relative humidity, wind speed, and photosynthetic radiation, further invert the upper/lower baseline of CWSI with the determination coefficient greater than 0.75. In the light of the error analysis of the inversion models, the vegetation index was inverted the canopy temperature of the Brassica chinensis in the vegetative and reproductive growth stage, indicating an excellent accuracy with the determination coefficient greater than 0.7. The calculated CWSI using the inversion models presented a significant correlation with the using the measurement, while the determination coefficient was equal to 0.70. And the CWSI showed the negative relationship with the stomatal conductance with the determination coefficient equal to 0.53. The meteorological parameters were used to invert the upper/lower baseline of CWSI, where the vegetation indexes were used to invert the canopy temperature. The inverted values using the SVR model shared the better fitting performance. The finding can provide a strong support for the spectral monitoring of the crop water stress index of the Brassica chinensis.
- water,
- temperature,
- Brassica chinensis,
- meteorological parameter,
- vegetation index,
- support vector regression

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References

[1]	卢宇鹏，夏岩石，温少波，等. 不同熟性菜心品质性状的多样性分析[J]. 广东农业科学，2020，47(5)：161-164.Lu Yupeng, Xia Yanshi, Wen Shaobo, et al. Diversity of quality traits of Chinese flowering cabbage varieties with different maturity[J]. Guangdong Agricultural Sciences, 2020, 47(5): 161-164. (in Chinese with English abstract)
[2]	徐燕. 土壤水分胁迫对菜心生理生化指标及气孔发育的影响[D]. 广州：暨南大学，2010.Xu Yan. Effects of Soil Water Stress on Physiology and Biochemistry and Stomatal Development in Brassica Chinensis[D]. Guangzhou: Jinan University, 2010. (in Chinese with English abstract)
[3]	李中赫，占车生，胡实，等. 气候变化条件下中国灌溉面积变化的产量效应[J]. 农业工程学报，2021，37(19)：94-104.Li Zhonghe, Zhan Chesheng, Hu Shi, et al. Yield effects of irrigated acreage change under climate change in China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(19): 94-104. (in Chinese with English abstract)
[4]	杨恒山，薛新伟，张瑞富，等. 灌溉方式对西辽河平原玉米产量及水分利用效率的影响[J]. 农业工程学报，2019，35(21)：69-77.Yang Hengshan, Xue Xinwei, Zhang Ruifu, et al. Effects of irrigation methods on yield and water use efficiency of maize in the West Liaohe Plain[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(21): 69-77. (in Chinese with English abstract)
[5]	Wu X, Shi J C, Zuo Q, et al. Parameterization of the water stress reduction function based on soil-plant water relations[J]. Irrigation Science, 2021, 39: 101-122.
[6]	Mukherjee S, Nandi R, Kundu A, et al. Soil water stress and physiological responses of chickpea (Cicer arietinum L. ) subject to tillage and irrigation management in lower Gangetic plain[J]. Agricultural Water Management, 2022, 263: 1-17.
[7]	Yang M X, Gao P, Zhou P, et al. Simulating Canopy Temperature using a random forest model to calculate the crop water stress index of Chinese Brassica[J]. Agronomy, 2021, 11: 2244.
[8]	Jamshidi S, Zand-Parsa S, Niyogi D. Assessing crop water stress index of citrus using in-situ measurements, landsat, and sentinel-2 data[J]. International Journal of Remote Sensing, 2021, 42(5): 1893-1916.
[9]	毋海梅，闫浩芳，张川，等. 温室滴灌黄瓜产量和水分利用效率对水分胁迫的响应[J]. 农业工程学报，2020，36(9)：84-93.Wu Haimei, Yan Haofang, Zhang Chuan, et al. Responses of yield and water use efficiency of drip-irrigated cucumber in greenhouse to water stress[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(9): 84-93. (in Chinese with English abstract)
[10]	García L, Parra L, Jimenez J M, et al. Deployment strategies of soil monitoring WSN for precision agriculture irrigation scheduling in rural areas[J]. Sensors, 2021, 21(5): 1-27.
[11]	张芮，王旺田，吴玉霞，等. 水分胁迫度及时期对设施延迟栽培葡萄耗水和产量的影响[J]. 农业工程学报，2017，33(1)：155-161.Zhang Rui, Wang Wangtian, Wu Yuxia, et al. Effect of moisture stress level and stage on evapotranspiration and yield of grape under protected and delayed cultivation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(1): 155-161. (in Chinese with English abstract)
[12]	Kumar N, Rustum R, Shankar V, et al. Self-organizing map estimator for the crop water stress index[J]. Computers and Electronics in Agriculture. 2021, 187, 106232.
[13]	Kumar N, Adeloye A J, Shankar V, et al. Neural computing modelling of the crop water stress index[J]. Agricultural Water Management. 2020, 239: 106259.
[14]	King B A, Tarkalson D D, Sharma V, et al. Thermal crop water stress index base line temperatures for sugarbeet in arid western U. S[J]. Agricultural Water Management, 2021, 243: 1-12.
[15]	王伟，彭彦昆，马伟，等. 冬小麦叶绿素含量高光谱检测技术[J]. 农业机械学报，2010，41(5)：172-177.Wang Wei, Peng Yankun, Ma Wei, et al. Prediction of chlorophyll content of winter wheat using leaf-level hyperspectral data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(5): 172-177. (in Chinese with English abstract)
[16]	陈鹏，冯海宽，李长春，等. 无人机影像光谱和纹理融合信息估算马铃薯叶片叶绿素含量[J]. 农业工程学报，2019，35(11)：63-74.Chen Peng, Feng Haikuan, Li Changchun, et al. Estimation of chlorophyll content in potato using fusion of texture and spectral features derived from UAV multispectral image[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(11): 63-74. (in Chinese with English abstract)
[17]	张鑫磊，刘连涛，孙红春，等. 不同施氮水平下棉花叶片最大羧化速率的高光谱估测[J]. 农业工程学报，2020，36(11)：166-173.Zhang Xinlei, Liu Liantao, Sun Hongchun, et al. Hyperspectral estimation of the maximum carboxylation rate of cotton leaves under different nitrogen levels[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(11): 166-173. (in Chinese with English abstract)
[18]	Gokhan C, Kursad D, Levent G. Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper[J]. Quantitative InfraRed Thermography Journal, 2018, 15(1): 81-94.
[19]	Neiff N, Dhliwayo T, Suarez A E, et al. Using an airborne platform to measure canopy temperature and NDVI under heat stress in maize[J]. Journal of Crop Improvement, 2015, 29(6): 669-690.
[20]	Huang S, Tang L N, Joseph P H, et al. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing[J]. Journal of Forestry Research, 2021, 32: 1-6.
[21]	Mohammed A N, Raj K, Louis L, et al. Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions[J]. Remote sensing, 2020, 12(5): 1-17.
[22]	许改平，吴兴波，刘芳，等. 高温胁迫下毛竹叶片色素含量与反射光谱的相关性[J]. 林业科学，2014，50(5)：41-48.Xu Gaiping, Wu Xingbo, Liu Fang, et al. The correlation between the pigment content and reflectance spectrum in phyllostachys edulis leaves subjected to high temperature[J]. Scientia Silvae Sinicae, 2014, 50(5): 41-48. (in Chinese with English abstract)
[23]	程高峰. 高温胁迫下水稻生理及光谱特性的研究[D]. 镇江：江苏大学，2009.Cheng Gaofeng. Study on the Physiological and Hyperspectral Characteristics of Rice Under High Temperature Stress[D]. Zhenjiang: Jiangsu University, 2009. (in Chinese with English abstract)
[24]	Sagan V, Maimaitijiang M, Sidike P, et al. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras[J]. Remote Sensing, 2019, 11(3): 1-29.
[25]	Ihuoma S O, Madramootoo C A. Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants[J]. Computers and Electronics in Agriculture, 2019, 163: 1-10.
[26]	梁金晨，江晓东，杨沈斌，等. 基于高光谱遥感数据的水稻叶温反演[J]. 南方农业学报，2020，51(1)：230-236.Liang Jinchen, Jiang Xiaodong, Yang Shenbin, et al. Rice leaf temperature inversion based on hyperspectral remote sensing data[J]. Journal of Southern Agriculture, 2020, 51(1): 230-236. (in Chinese with English abstract)
[27]	Gyamerah S A, Ngare P, Ikpe D. Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function[J]. Agriculture and Forest Meteorology, 2020, 280: 107808.
[28]	Virnodkar S S, Pachghare V K, Patil V C, et al. Remote sensing and machine learning for crop water stress determination in various crops: a critical review[J]. Precision Agriculture, 2020, 21(5): 1121-1155.
[29]	徐敏，赵艳霞，张顾，等. 基于机器学习算法的冬小麦始花期预报方法[J]. 农业工程学报，2021，37(11)：162-171.Xu Min, Zhao Yanxia, Zhang Gu, et al. Method for forecasting winter wheat first flowering stage based on machine learning algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(11): 162-171. (in Chinese with English abstract)
[30]	谭丞轩. 基于无人机多光谱遥感的大田玉米土壤含水率估算模型研究[D]. 杨凌：西北农林科技大学，2020.Tan Chengxuan. Research on the Estimation Model of Field Maize Soil Moisture Content Based on UAV Multispectral Remote Sensing[D]. Yangling: Northwest A&F University, 2020. (in Chinese with English abstract)
[31]	Li X J, Du H Q, Mao F J, et al. Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms[J]. Agriculture and Forest Meteorology, 2018, 256: 445-457.
[32]	黄春燕，王登伟，肖莉娟，等. 不同水分条件下棉花光谱数据对冠层叶片温度的响应特征[J]. 棉花学报，2014，26(3)：244-251.Huang Chunyan, Wang Dengwei, Xiao Lijuan, et al. The responsive characteristics between cotton canopy leaves temperature from infrared thermography and hyperspectral data under different water conditions[J]. Cotton Science, 2014, 26(3): 244-251. (in Chinese with English abstract)
[33]	Corinna C, Vladimir V. Support vector networks[J]. Machine Learning, 1995, 20(3)：273-297.
[34]	Osroosh Y, Troy Peters R, Campbell C S, et al. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold[J]. Computer and Electronic in Agriculture, 2015, 118: 193-203.

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