Variations of Canopy Temperature in a Rubber Plantation in Western Hainan Island and Their Relations with Micrometeorological Factors

doi:10.3969/j.issn.1000-2561.2020.07.028

Abstract

Abstract:

Canopy temperature is one of the most important parameters to characterize the physiological, ecological processes and energy balance of plants. In order to investigate the variation of the canopy temperature in tropical forests at different time scales and to preliminarily analyze the relationship between environmental factors and canopy temperature, an infrared temperature sensor was used to measure the canopy temperature of a rubber plantation in Western Hainan Island in 2017. Based on the annual canopy temperature data and synchronous canopy micrometeorological data, the characteristics of canopy temperature (T_c) and air temperature (T_a) of a rubber plantation in dry and wet seasons were analyzed, and the micrometeorological factors were discussed. The daily change of canopy temperature in a rubber plantation was a single-peak curve every month. Compared with T_a, T_c had obvious characteristics of phase forward and intensified change. During the day, the canopy boundary layer was unstable, and the canopy was the heat source of SPAC (Soil-Plant-Atmosphere Continuum), at night, the canopy boundary layer was inversion stratification, which was stable, and the canopy was the cold source of SPAC. The canopy temperature in wet seasons was higher than that in dry seasons. If only T_a was considered, T_c and T_a had a good linear relationship, and the linear equation was T_c= 1.03T_a-0.656. If the micro-meteorological factors such as atmospheric temperature (T_a), net radiation (R_n), relative humidity (RH) and wind speed (V) were taken into account, the multiple correlation coefficients showed that the annual correlation was very significant. Partial correlation coefficients showed that the change of canopy temperature in dry seasons was mainly affected by T_a, R_n and RH, and secondly by V. Wet seasons were mainly affected by T_a and RH, the influence of R_n and V was negligible. The results preliminarily revealed the annual variation of canopy temperature in rubber plantation and its relationship with many micro-meteorological factors, providing a scientific basis for further exploring the change of canopy temperature and its influencing mechanism.

Key words: canopy temperature, rubber plantation, micrometeorology, daily variation

CLC Number:

Q148

DENG Cong,WU Zhixiang,TAN Zhenghong,LIAO Liguo,CUI Yibin,CHENG Juan,ZHAO Linlin. Variations of Canopy Temperature in a Rubber Plantation in Western Hainan Island and Their Relations with Micrometeorological Factors[J]. Chinese Journal of Tropical Crops, 2020, 41(7): 1490-1497.

Figures/Tables 7

References 18

[1]	史长丽, 郭家选, 梅旭荣, 等. 夏玉米农田表面温度影响因素分析[J]. 中国农业科学, 2006(1):48-56.
[2]	张一平, 马友鑫, 刘玉洪, 等. 橡胶林缘热力垂直特征的初步分析[J]. 北华大学学报(自然科学版), 2000,1(3):254-258.
[3]	祁栋灵, 谢贵水, 孙瑞, 等. 热垦525橡胶树茎粗生长的季节变化规律及其与气象因子关联度分析[J]. 南方农业学报, 2015,46(8):1442-1448.
[4]	Tanner C B. Plant temperatures[J]. Agronomy Journal, 1963,55(2):210-211. DOI URL
[5]	刘云, 宇振荣, 孙丹峰. 冬小麦冠气温差及其相关影响因素关系研究[J]. 灌溉排水学报, 2004(1):30-35.
[6]	韩亚东, 张文忠, 杨梅, 等. 孕穗期水稻叶温与水分状况关系的研究[J]. 中国农学通报, 2006(3):214-216.
[7]	王纯枝, 宇振荣, 孙丹峰, 等. 夏玉米冠气温差及其影响因素关系探析[J]. 土壤通报, 2006(4):651-658.
[8]	邵光成, 张展羽, 蔡焕杰, 等. 膜下滴灌棉花缺水诊断指标的试验研究[J]. 河海大学学报(自然科学版), 2004(5):546-549.
[9]	马友鑫, 张克映, 刘玉洪. 云南橡胶林冠面温度的初步研究[J]. 林业科学, 1996(3):193-198.
[10]	Song Q H, Deng Y, Zhang Y P, et al. Comparison of infrared canopy temperature in a rubber plantation and tropical rain forest[J]. International Journal of Biometeorology, 2017,61(10):1885-1892. DOI URL PMID
[11]	吴志祥, 谢贵水, 杨川, 等. 橡胶林生态系统干季微气候特征和通量的初步观测[J]. 热带作物学报, 2010,31(12):2081-2090.
[12]	祁栋灵, 孙瑞, 陶忠良, 等. 海南儋州地区低割龄橡胶树热研7—20—59茎粗生长月动态研究初报[J]. 生态科学, 2017,36(5):80-84.
[13]	Blonquist J M, Norman J M, Bugbee B. Automated measurement of canopy stomatal conductance based on infrared temperature[J]. Agricultural and Forest Meteorology, 2009,149(12):2183-2197. DOI URL
[14]	魏丹丹, 张劲松, 孟平, 等. 栓皮栎人工林冠层温度变化特征及其与微气象因子的关系[J]. 应用生态学报, 2012,23(7):1767-1773.
[15]	Nelson J A, Bugbee B. Analysis of environmental effects on leaf temperature under sunlight, high pressure sodium and light emitting diodes[J]. PLoS One, 2015,10(10):e0138930. URL PMID
[16]	段永红, 李本纲, 陶澍. 冬小麦田午时冠层温度与气温和地温的关系[J]. 应用气象学报, 2003(3):356-362.
[17]	Geiser K M, Slack D C, Allred E R, et al. Irrigation scheduling using crop canopy-air temperature difference[J]. Transactions of the Asae, 1982,25(3):689-694. DOI URL
[18]	杨振, 张一平, 于贵瑞, 等. 西双版纳热带季节雨林林冠层温度与大气温度特征[J]. 生态学杂志, 2009,28(5):845-849.

月份 Month	平均气温 Mean temperature/℃	平均相对湿度 Mean relative humidity/%	平均风速 Mean wind speed/ (m·s^-1)	平均风向 Mean wind direction/ degree
1	19.1	86.24	3.61	92.60
2	18.2	83.51	3.25	112.73
3	21.9	84.11	2.69	141.04
4	24.8	76.87	2.86	148.75
5	26.1	82.56	2.46	141.02
6	27.8	76.07	2.64	177.13
7	26.0	87.54	3.39	134.60
8	27.2	78.69	2.69	180.07
9	27.0	82.65	2.63	163.44
10	23.9	84.18	3.07	117.70
11	21.2	89.41	3.00	83.94
12	18.1	81.43	3.45	90.71

月份 Month	平均气温 Mean temperature/℃	平均相对湿度 Mean relative humidity/%	平均风速 Mean wind speed/ (m·s^-1)	平均风向 Mean wind direction/ degree
1	19.1	86.24	3.61	92.60
2	18.2	83.51	3.25	112.73
3	21.9	84.11	2.69	141.04
4	24.8	76.87	2.86	148.75
5	26.1	82.56	2.46	141.02
6	27.8	76.07	2.64	177.13
7	26.0	87.54	3.39	134.60
8	27.2	78.69	2.69	180.07
9	27.0	82.65	2.63	163.44
10	23.9	84.18	3.07	117.70
11	21.2	89.41	3.00	83.94
12	18.1	81.43	3.45	90.71

气候划分 Climate division	月份 Month	回归方程 Regression equation	偏相关系数Partial correlation coefficient				复相关系数 Correlation coefficient	样本数 Sample number
气候划分 Climate division	月份 Month	回归方程 Regression equation	大气温度 Air temperature (T_a)	净辐射 Net radiation (R_n)	相对湿度 Relative humidity (RH)	风速 Wind speed (V)	复相关系数 Correlation coefficient	样本数 Sample number
干季	1	T_c= 6.188 + 0.872T_a + 0.002R_n- 0.035RH -0.208V	0.882^**	0.930^**	-0.345	-0.636^**	0.9998^**	1200
	2	T_c= 6.877 + 0.883T_a + 0.003R_n- 0.044RH -0.330V	0.959^**	0.969^**	-0.608^**	-0.626^**	0.9998^**	1344
	3	T_c= 22.942 + 0.356T_a + 0.009R_n- 0.129RH + 0.621V	0.262	0.868^**	-0.464^*	0.367	0.9981^**	1104
	4	T_c= 23.936 + 0.502T_a + 0.004R_n- 0.152RH	0.302	0.682^**	-0.292	—	0.9938^**	1440
	11	T_c= 0.657 + 0.970T_a + 0.001R_n- 0.027V	0.995^**	0.970^**	—	-0.213	0.9999^**	1440
	12	T_c=-2.453 + 1.058T_a + 0.001R_n + 0.019RH -0.051V	0.969^**	0.887^**	0.318	-0.302	0.9998^**	1488
	1—4与11—12	T_c= 23.085 + 0.435T_a + 0.004R_n- 0.138RH	0.508^**	0.925^**	-0.660^**	—	0.9994^**	8016
湿季	5	T_c= 27.327 + 0.606T_a -0.192RH- 0.322V	0.549^**	—	-0.607^**	-0.270	0.9934^**	1488
	6	T_c= 26.894 + 0.838T_a -0.004R_n- 0.281RH	0.730^**	-0.382^*	-0.500^**	—	0.9901^**	1248
	7	T_c=-4.510 + 1.178T_a	0.697^**	—	—	—	0.9923^**	384
	8	T_c=-0.587 + 1.020T_a + 0.002R_n	0.692^**	0.309	—	—	0.9868^**	1104
	9	T_c= 1.636 + 0.933T_a + 0.002R_n	0.700^**	0.383^*	—	—	0.9962^**	1440
	10	T_c= 4.748 + 0.907T_a + 0.001R_n- 0.029RH -0.083V	0.956^**	0.914^**	-0.473^*	-0.335	0.9998^**	1488
	5—10	T_c= 28.398 + 0.560T_a -0.190RH- 0.368V	0.476^**	—	-0.549^**	-0.281	0.9955^**	7152

气候划分 Climate division	月份 Month	回归方程 Regression equation	偏相关系数Partial correlation coefficient				复相关系数 Correlation coefficient	样本数 Sample number
气候划分 Climate division	月份 Month	回归方程 Regression equation	大气温度 Air temperature (T_a)	净辐射 Net radiation (R_n)	相对湿度 Relative humidity (RH)	风速 Wind speed (V)	复相关系数 Correlation coefficient	样本数 Sample number
干季	1	T_c= 6.188 + 0.872T_a + 0.002R_n- 0.035RH -0.208V	0.882^**	0.930^**	-0.345	-0.636^**	0.9998^**	1200
	2	T_c= 6.877 + 0.883T_a + 0.003R_n- 0.044RH -0.330V	0.959^**	0.969^**	-0.608^**	-0.626^**	0.9998^**	1344
	3	T_c= 22.942 + 0.356T_a + 0.009R_n- 0.129RH + 0.621V	0.262	0.868^**	-0.464^*	0.367	0.9981^**	1104
	4	T_c= 23.936 + 0.502T_a + 0.004R_n- 0.152RH	0.302	0.682^**	-0.292	—	0.9938^**	1440
	11	T_c= 0.657 + 0.970T_a + 0.001R_n- 0.027V	0.995^**	0.970^**	—	-0.213	0.9999^**	1440
	12	T_c=-2.453 + 1.058T_a + 0.001R_n + 0.019RH -0.051V	0.969^**	0.887^**	0.318	-0.302	0.9998^**	1488
	1—4与11—12	T_c= 23.085 + 0.435T_a + 0.004R_n- 0.138RH	0.508^**	0.925^**	-0.660^**	—	0.9994^**	8016
湿季	5	T_c= 27.327 + 0.606T_a -0.192RH- 0.322V	0.549^**	—	-0.607^**	-0.270	0.9934^**	1488
	6	T_c= 26.894 + 0.838T_a -0.004R_n- 0.281RH	0.730^**	-0.382^*	-0.500^**	—	0.9901^**	1248
	7	T_c=-4.510 + 1.178T_a	0.697^**	—	—	—	0.9923^**	384
	8	T_c=-0.587 + 1.020T_a + 0.002R_n	0.692^**	0.309	—	—	0.9868^**	1104
	9	T_c= 1.636 + 0.933T_a + 0.002R_n	0.700^**	0.383^*	—	—	0.9962^**	1440
	10	T_c= 4.748 + 0.907T_a + 0.001R_n- 0.029RH -0.083V	0.956^**	0.914^**	-0.473^*	-0.335	0.9998^**	1488
	5—10	T_c= 28.398 + 0.560T_a -0.190RH- 0.368V	0.476^**	—	-0.549^**	-0.281	0.9955^**	7152