基于无人机热红外遥感的冬小麦水分胁迫研究
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摘要
为探究水分胁迫对冬小麦生长的影响,以不同水分处理的冬小麦为试验对象,利用无人机搭载热红外传感器,通过采集其不同生育期中一天不同时刻(11∶00,13∶00)的冠层热红外图像,提取其冠层温度信息,同时测定小麦叶片的气孔导度(Gs)、蒸腾速率(Tr)和田间土壤体积含水率(SWC)等信息。分别研究不同水分胁迫指数(CWSI、I_G、ICWSI)与各参数之间的关系,同时使用一元线性模型和多元线性回归模型进行建模并验证。结果表明:CWSI、I_G和ICWSI与Gs、Tr和SWC之间存在着显著的相关关系,在一元模型中,SWC对不同水分胁迫指数的预测效果更好,验证R~2均在0.800以上,相对分析误差均在2.0以上,在多元模型中,CWSI的预测效果最好,验证R~2为0.928,相对分析误差为3.041,同时多元模型的预测效果均优于一元模型。该研究可快速获取大量作物信息,为利用无人机热红外遥感探究冬小麦的水分胁迫状况提供了一条新途径。
To explore the effects of water stress on winter wheat growth, canopy thermal infrared images of the wheat with different water treatments were collected by thermal infrared sensor loaded in UAV at certain moments(11∶00, 13∶00) a day in different growth periods and the canopy temperature information are extracted. Meanwhile, this test collected the information of wheat leaf stomatal conductance(Gs), transpiration rate(Tr) and the field soil volumetric moisture content(SWC). The relationship between different water stress indexes(CWSI, I_G, ICWSI) and each parameter was analyzed, and the unary linear model and multiple linear regression model were used for modeling and verification. The result shows that there exists a significant relationship between the CWSI, I_G, ICWSI and Gs, Tr, SWC; SWC is the best index to predict the effect of different water stress and the R~2(determination coefficient) is above 0.800, with the prediction of RPD( residual predictive deviation) up to 2.0 in a single model; the prediction effect of CWSI is the best in the multivariate model, the R~2(determination coefficient) is 0.928, the prediction of RPD( residual predictive deviation) is 3.041, and the prediction result of the multiple model is superior to a single model. This study can obtain a large amount of crop information quickly and provide a new approach to explore the water stress status of winter wheat by using thermal infrared remote sensing of UAV.
To explore the effects of water stress on winter wheat growth, canopy thermal infrared images of the wheat with different water treatments were collected by thermal infrared sensor loaded in UAV at certain moments(11∶00, 13∶00) a day in different growth periods and the canopy temperature information are extracted. Meanwhile, this test collected the information of wheat leaf stomatal conductance(Gs), transpiration rate(Tr) and the field soil volumetric moisture content(SWC). The relationship between different water stress indexes(CWSI, I_G, ICWSI) and each parameter was analyzed, and the unary linear model and multiple linear regression model were used for modeling and verification. The result shows that there exists a significant relationship between the CWSI, I_G, ICWSI and Gs, Tr, SWC; SWC is the best index to predict the effect of different water stress and the R~2(determination coefficient) is above 0.800, with the prediction of RPD( residual predictive deviation) up to 2.0 in a single model; the prediction effect of CWSI is the best in the multivariate model, the R~2(determination coefficient) is 0.928, the prediction of RPD( residual predictive deviation) is 3.041, and the prediction result of the multiple model is superior to a single model. This study can obtain a large amount of crop information quickly and provide a new approach to explore the water stress status of winter wheat by using thermal infrared remote sensing of UAV.
引文
[1] Liu Xianfeng,Zhu Xiufang,Pan Yaozhong, et al. Agricultural drought monitoring:Progress, challenges, and prospect[J].ACTA Geographical Sinica,2016,26(6):750-767.
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[4] Jackson R D,Idso S B,Reginato R J.Canopy temperature as a crop water stress indicator[J]. Water Resource Research,1981,17(4):1 133-1 138.
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[13] 张立伟,张智郡 ,刘海军,等. 基于冠层温度的玉米缺水诊断研究[J].干旱地区农业研究,2017,35(3):94-98.
[14] 赵焕,徐宗学,赵捷. 基于CWSI及干旱稀遇程度的农业干旱指数构建及应用[J].农业工程学报,2017,33(9):116-125.
[15] Junzeng Xu, Yuping Lv, Xiaoyin Liu, et al. Diagnosing Crop Water Stress of Rice using Infra-red Thermal Imager under Water Deficit Condition[J].International Journal of Agriculture and Biology,2016,18(3):565-572.
[16] 杨永辉,吴普特,武继承,等. 复水前后冬小麦光合生理特征对保水剂用量的响应[J].农业机械学报,2011,42(7):116-123.
[17] 冯晓钰,周广胜. 夏玉米叶片水分变化与光合作用和土壤水分的关系[J].生态学报,2018,38(1):177-185.
[18] 程麒,黄春燕,王登伟,等. 基于红外热图像的棉花冠层水分胁迫指数与光合特性的关系[J].棉花学报,2012,24,(4):341-347.
[19] BALUJA J, DIAGO M P, BALDA P, et al. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) [J]. Irrigation Science, 2012, 30(6): 511-522.
[20] RUD R, COHEN Y, ALCHANATIS V, et al. Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status [J]. Precision Agriculture, 2014, 15(3): 273-289.
[21] 张智韬,边江, 韩文霆,等. 无人机热红外图像计算冠层温度特征数诊断棉花水分胁迫[J].农业工程学报,2018,34(15):77-84.
[22] 张智韬,边江, 韩文霆,等. 剔除土壤背景的无人机热红外遥感诊断棉花水分胁迫[J].农业机械学报,2018,62(10).
[23] 张立元,牛亚晓,韩文霆,等. 大田玉米水分胁迫指数经验模型建立方法[J].农业机械学报,2018,62(5).
[2] TANNER C. Plant temperatures [J]. Agronomy Journal, 1963,55(2):210-211.
[3] Idso S B,Jackson R D,Pinter Jr,et al.Normalizing the stress-degree-day parameter for environmental variability[J].Agricultural Meteorology,1981,24(1):45-55.
[4] Jackson R D,Idso S B,Reginato R J.Canopy temperature as a crop water stress indicator[J]. Water Resource Research,1981,17(4):1 133-1 138.
[5] Jones HG. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling [J].Agr Forest Meteorol, 1999,95(3):139-149.
[6] Jones H G,Stoll M,Santos T,et al. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine[J].Journal of Experimental Botany,2002,53(378):2249-2260.
[7] Jones H G, SERRAJ R, LOVEYS B R, et al. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field [J]. Functional Plant Biology, 2009,36(11): 978-989.
[8] Azar Khorsandi, Abbas Hemmat, Seyed Ahmad Mireei,et al. Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions[J]. Agricultural Water Management,2018,204(2):222-233.
[9] 张小雨,孙宏勇,王艳哲,等.应用基于红外热画像技术的CWSI简化计算方法判断作物水分状态[J].中国农业气象,2013,34(5):569-575.
[10] 张仁华.以红外辐射信息为基础的估算作物缺水状况的新模式[J].中国科学,1986,22(7):1 702-1 716.
[11] 蔡焕杰,康绍忠,熊运章. 用冠层温度计算作物缺水指标的一种简化模式[J].水利学报,1996,41(7):44-49.
[12] 袁国富,罗毅,孙晓敏,等. 作物冠层表面温度诊断冬小麦水分胁迫的试验研究[J]. 农业工程学报,2002,18(6):13-17.
[13] 张立伟,张智郡 ,刘海军,等. 基于冠层温度的玉米缺水诊断研究[J].干旱地区农业研究,2017,35(3):94-98.
[14] 赵焕,徐宗学,赵捷. 基于CWSI及干旱稀遇程度的农业干旱指数构建及应用[J].农业工程学报,2017,33(9):116-125.
[15] Junzeng Xu, Yuping Lv, Xiaoyin Liu, et al. Diagnosing Crop Water Stress of Rice using Infra-red Thermal Imager under Water Deficit Condition[J].International Journal of Agriculture and Biology,2016,18(3):565-572.
[16] 杨永辉,吴普特,武继承,等. 复水前后冬小麦光合生理特征对保水剂用量的响应[J].农业机械学报,2011,42(7):116-123.
[17] 冯晓钰,周广胜. 夏玉米叶片水分变化与光合作用和土壤水分的关系[J].生态学报,2018,38(1):177-185.
[18] 程麒,黄春燕,王登伟,等. 基于红外热图像的棉花冠层水分胁迫指数与光合特性的关系[J].棉花学报,2012,24,(4):341-347.
[19] BALUJA J, DIAGO M P, BALDA P, et al. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) [J]. Irrigation Science, 2012, 30(6): 511-522.
[20] RUD R, COHEN Y, ALCHANATIS V, et al. Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status [J]. Precision Agriculture, 2014, 15(3): 273-289.
[21] 张智韬,边江, 韩文霆,等. 无人机热红外图像计算冠层温度特征数诊断棉花水分胁迫[J].农业工程学报,2018,34(15):77-84.
[22] 张智韬,边江, 韩文霆,等. 剔除土壤背景的无人机热红外遥感诊断棉花水分胁迫[J].农业机械学报,2018,62(10).
[23] 张立元,牛亚晓,韩文霆,等. 大田玉米水分胁迫指数经验模型建立方法[J].农业机械学报,2018,62(5).