Abstract:

The difference of canopy-air temperature can indirectly monitor the variation of tea tree heat and moisture. However, the time lag effect between the tea tree canopy temperature and atmospheric temperature of tea plantation will affect the monitoring effect. In order to explore the time lag effect and the variation law between canopy temperature and atmospheric temperature, the variation characteristics of tea tree canopy temperature and atmospheric temperature of tea plantation during different tea picking seasons and different weather types were analyzed based on the monitoring data of microclimate station in tea plantation and near national meteorological station in Wuyuan from March to September in 2020. The simulated models of daily average canopy temperature and atmospheric temperature of tea plantation according to different weather types were established through the linear regression method and tested to provide data support for tea meteorological service. The results showed that: (1) the diurnal variation of tea tree canopy temperature and atmospheric temperature of tea plantation showed an obviously single-peak trend during different tea picking seasons and different weather types, while the change intensity of canopy temperature was greater than that of atmospheric temperature of tea plantation, and the peak time of canopy temperature was generally about 1h earlier than that of atmospheric temperature of tea plantation. (2) The point temperature difference of tea tree canopy and atmospheric temperature of tea plantation within 24h a day were generally ranked as spring tea＞autumn tea＞summer tea, sunny days＞cloudy days＞rainy days. In general, the canopy temperature was above or near to the atmospheric temperature of tea plantation around noon, while the canopy temperature was lower than the atmospheric temperature of tea plantation at all times in rainy days. (3) From the aspects of daily average temperature, it showed that the canopy was generally lower (1-2℃) than which in the atmosphere, but the changing trend was the same. (4) All kinds of daily average temperature prediction models were approved by 0.01 level significant test, which meaned the simulation effect were good generally. What’s more, the simulation effect of the atmospheric temperature of tea plantation prediction models were better than that of canopy temperature. In addition, under different weather types, the optimal effect of the prediction models were demonstrated in rainy days, followed by sunny days, and relatively poor in cloudy days.

Key words: Tea tree, Canopy temperature, Atmospheric temperature, Time lag effect, Correlation model.