The solar greenhouse structure helps indoor lighting and heat storage, ensuring the normal growth of crops. However, there is variability in environmental parameters at different times and locations in the greenhouse, and they vary with weather and seasons. To reveal the spatiotemporal variation patterns of light intensity, air temperature and humidity in the canopy, an environmental monitoring system based on wireless sensor networks was built. Nodes with environmental information sensing functions such as light intensity, air temperature and humidity were deployed in the crop canopy to analyze the temporal and spatial variation of environmental parameters. Firstly, the inverse distance weighted algorithm was used to construct discrete data surface of canopy light intensity, air temperature and humidity. Secondly, the K-means clustering based on centroid coordinates of the interpolation results was carried out to calculate the position of the feature points of connected and non-connected areas in the greenhouse. Finally, the semi-variogram method was used to analyze the spatiotemporal variability of the monitoring parameters of the interpolation nodes. The experimental results showed that the solar greenhouse in summer presented high temperature and high light in the afternoon. The light intensity at 08:00 and 16:00 was 24.2% and 72.9% of that at 12:00, respectively. The air temperature at 08:00 (27.7℃) was about 6℃ lower than that at 12:00 and 16:00, and the air humidity (90%) was about 30% higher. The maximum light intensity in sunny day was 1.4 times of that in cloudy day and 4.6 times of that in rainy day. The maximum air temperature in sunny day and cloudy day was about 6℃ higher than that in rainy day (29.5℃), and the minimum air humidity were lower than that in rainy day (84%). The solar greenhouse presented high temperature and low humidity in both sunny day and cloudy day, and high humidity and low light in rainy day. The range of light intensity was 10.34m, and the spatial variability was strong. The spatial variability of air temperature and humidity was weak, and the overall distribution was relatively uniform. The temporal variability of light intensity, air temperature, and air humidity were moderate. The characteristic points and spatial and temporal variation patterns of environmental parameters contributed to the efficient deployment of solar greenhouse sensors and provided a basis for revealing the interaction between crops and the environment.