Modeling Spatial Distribution of Humidity and Near-surface Soil Temperature in Common Greenhouses

Abstract

To overcome the limited adaptability of current greenhouse environmental models, this study develops targeted models for predicting the spatial distribution of near-surface soil temperature and air humidity. The soil temperature model utilizes a one-dimensional unsteady-state heat transfer approach that accounts for solar radiation, convection, thermal radiation, and evaporation. Concurrently, the air humidity model applies water vapor mass conservation to integrate the effects of soil evaporation, ventilation, and humidification.

Experimental validation showed that the soil temperature model effectively captured diurnal variations at different soil depths. The relative root mean square error (rRMSE) was 5% at 0.25 m depth, demonstrating high accuracy, whereas predictions at the more dynamic surface layer (0.05 m) showed a higher error of 10%. The air humidity model produced trends consistent with measured data, albeit with a general underestimation, yielding a mean bias error (MBE) of 4.027 and an RMSE of 8.09%.

The proposed models exhibit strong adaptability for simulating humidity and near-surface soil temperature distributions in common greenhouses. This work provides valuable insights and a practical methodological framework for advancing precise environmental control and promoting the development of standardized models in facility agriculture.