Abstract：[Background] In order to accurately determine the linear regression relationship between leaf temperature difference and atmospheric vapor pressure difference (VPD) of Quercus variabilis seedlings under the condition of adequate water supply, and to optimize the crop water stress index (CWSI) empirical model established by predecessors, Zhengzhou city in Henan province, where plant drought is a significant problem, was selected as the main research area, aiming to analyze and understand the dynamic change of soil water and the response degree of plants in a more representative way, so as to build a more applicable CWSI model and soil water diagnosis model. [Methods] The temperature, solar radiation, leaf temperature difference and atmospheric vapor pressure difference of Q. variabilis seedlings in the pot experiment were measured by dry-wet reference surface method in research base of College of Forestry, Henan Agricultural University. The reasonable upper and lower baselines of CWSI model of Q. variabilis seedlings were determined by theoretical analysis and calculation of energy balance principle. [Results] Under dry reference plane condition, the leaf temperature difference ∆t干 of Q. variabilis seedlings was significantly positively correlated with solar radiation Q, and the upper baseline of CWSI empirical model was determined as a straight line: ∆t干 = 0.007Q + 1.621. Under the wet reference plane, there was a negative correlation between leaf temperature difference ∆t湿 and V (VPD) of atmospheric saturated water vapor pressure difference, and the linear regression relationship was significant. The optimized lower baseline of CWSI empirical model was obtained: ∆t湿 = 1.218V + 1.987. Thus, the CWSI empirical model after optimizing the lower baseline is I = tca (1.218V + 1.987)/ (0.007Q + 1.621) (1.218V + 1.987), tca is the measured leaf-air temperature difference. The CWSI value of Q. variabilis seedlings was calculated by using the optimized CWSI model with the obtained upper and lower baseline. The linear relationship between CWSI value I and soil moisture θ was significant. The diagnostic formula is θ = 24.65I + 27.91. The relationship between CWSI and soil moisture content was as follows: 12% soil moisture corresponded to mild drought, and CWSI was about 0.65. The soil moisture of 8% 10% corresponded to moderate drought, and the CWSI was 0.75 0.8. Soil moisture of 5% 8% corresponds to severe drought and CWSI is 0.8 0.95. When soil moisture was lower than 5% and CWSI was greater than 0.95, the seedling stress was fatal. [Conclusions] The relationship between leaf temperature difference and VPD can be more accurately determined by the heat budget calculation method of energy balance equation, so as to optimize the CWSI model, and then calculate the linear relationship between CWSI of different crops and soil moisture according to this model, and obtain the soil moisture diagnosis model. The soil moisture of different crops or forest plants can be diagnosed according to the solar radiation and air temperature and humidity of remote sensing leaves and weather stations.
王谦, 解英超, 李明蔚, 李强, 陈景玲, 寇渊博. 基于能量平衡原理的栓皮栎幼苗土壤水分胁迫指数下基线研究[J]. 中国水土保持科学, 2024, 22(3): 162-168.
WANG Qian, XIE Yingchao, LI Mingwei, LI Qiang, CHEN Jingling, KOU Yuanbo. Study on base line of CWSI for Quercus variabilis seedlings based on energy balance principle. SSWC, 2024, 22(3): 162-168.
陈景玲,王佩舒,刘琳奇,等.光温条件和土壤湿度对栓皮栎幼苗蒸腾潜热和叶温的影响[J]中国水土保持科学,2017,15(1):62. CHEN Jingling, WANG Peishu, LIU Linqi, et al. Impacts of radiation, temperature and soil moisture on hidden heat of transpiration and leaf temperature of Quercus variabilis seedlings[J]. Science of Soil and Water Conservation,2017,15(1):62.
[2]
王谦,李明蔚,李强,等.基于栓皮栎幼苗叶绿素荧光参数的不同质地土壤干旱胁迫指标[J].中国水土保持科学,2021,19(2):27. WANG Qian, LI Mingwei, LI Qiang, et al. Drought stress indexes of soil with different texture based on chlorophyll fluorescence parameters of Quercus variabilis seedlings[J]. Science of Soil and Water Conservation,2021, 19(2):27.
[3]
张立元.基于无人机遥感的大田玉米水分胁迫监测研究[D].陕西杨凌:西北农林科技大学,2022:128. ZHANG Liyuan. Monitoring maize water stress based on UAV remote sensing data[D]. Yangling,Shaanxi:Northwest A&F University,2022:128.
[4]
刘婵,范兴科.基于冠层叶气温差的温室土壤水分诊断[J].干旱地区农业研究,2012,30(1):90. LIU Chan, FAN Xingke. Diagnosis of soil moisture in greenhouse based on canopy leaf-air temperature difference[J]. Agricultural Research in the Arid Areas,2012, 30(1):90.
[5]
IDSO S B, JACKSON R D, PINTER P J, et al. Normalizing the stress-degree-day parameter for environmental variability[J]. Agricultural Meteorology,1981,24:45.
[6]
JACKSON R D, KUSTAS W P, CHOUDHURY B J. A reexamination of the crop water stress index[J]. Irrigation Science,1988,9(4):309.
[7]
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.
[8]
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,2018,49(5):233. ZHANG Liyuan, NIU Yaxiao, HAN Wenting, et al. Establishing method of crop water stress index empirical model of field maize[J]. Transactions of the CSAM. 2018,49(5):233.
[9]
赵福年,张虹,陈家宙,等.玉米作物水分胁迫指数(CWSI)基线差异原因初探[J].中国农学通报,2013, 29(6):46. ZHAO Funian, ZHANG Hong, CHEN Jiazhou, et al. Preliminary investigation on difference of crop water stress index baseline for maize[J]. Chinese Agricultural Science Bulletin, 2013,29(6):46.
[10]
赵福年,王瑞君,张虹,等.基于冠气温差的作物水分胁迫指数经验模型研究进展[J].干旱气象,2012, 30(4):522. ZHAO Funian, WANG Ruijun, ZHANG Hong, et al. Advances in crop water stress index empirical model research based on canopy and atmosphere temperature difference[J]. Journal of Arid Meteorology, 2012, 30(4):522.
[11]
PAYERO J O, NEALE C M U,WRIGHT J L. Non-waterstressed baselines for calculating crop water stress index (cwsi) for alfalfa and tall fescue grass[J]. Transactions of the ASAE,2005,48(2):653.
[12]
赵福年,陈家宙,张虹.施氮水平对红壤区夏玉米水分胁迫指数下基线的影响[J].中国农业气象,2012, 33(2):215. ZHAO Funian, CHEN Jiazhou, ZHANG Hong. Effect of nitrogen fertilization level on the low baseline of crop water stress index for summer maize in red soil[J]. Chinese Journal of Agrometeorology,2012,33(2):215.
[13]
TESTI L, GOLDHAMER D A, INIESTA F, et al. Crop water stress index is a sensitive water stress indicator in pistachio trees[J]. Irrigation Science,2008, 26(5):395.
[14]
YAZAR A,HOWELL T A, DUSEK D A, et al. Evaluation of crop water stress index for LEPA irrigated corn[J]. Irrigation Science,1999,18(4):171.
[15]
JENSEN H E, SVENDSEN H, JENSEN S E, et al. Canopy-air temperature of crops grown under different irrigation regimes in a temperate humid climate[J]. Irrigation Science,1991,12(2):181.
[16]
IDSO S B. Non-water-stressed baselines:A key to measuring and interpreting plant water stress[J]. Agricultural Meteorology,1982,27(12):59.
[17]
TAGHVAEIAN S, CHÁVEZ J L, HANSEN N C. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in northeastern Colorado[J]. Remote Sensing,2012,4(11):3619.
[18]
ALDERFASI A A, NIELSEN D C. Use of crop water stress index for monitoring water status and scheduling irrigation in wheat[J]. Agricultural Water Management, 2001,47(1):69.
[19]
HOWELL T A, MUSICK J T, TOLK JA. Canopy temperature of irrigated winter wheat[J]. Transactions of the Asae,1986, 29(6):1692.
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