The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were −0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: ΔCO2 = 0.079 μmol m−2s−1). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately.

Accurate estimation of net ecosystem carbon exchange (NEE) is vital to regional carbon balance. Currently, NEE observations at the canopy scale are mainly based on the chamber method. However, the chamber method is labor intensive, time consuming, and measures only plot-scale NEE. It cannot reflect whole-field NEE with high spatial resolution. In this study, maize daytime NEE variations in four fields under different irrigation treatments in a semiarid area was measured using the chamber method, and the spectral reflectance in the maize canopy at noon was obtained using an unmanned aerial vehicle (UAV) multispectral system. We established a daytime NEE variation estimation model and up-scaled the level of NEE observations in maize canopy using UAV-based remote sensing. Twelve widely used vegetation indices were employed for NEE estimation. To obtain an optimal NEE variation estimation method, we compared the performance of several models, including simple linear regression, multiple stepwise regression, and four machine learning (ML) algorithms. Based on the comparison, the modified triangular vegetation index-2 is the best predictor for analyzing simple linear regression, with a coefficient of determination R 2 = 0.719. Compared with the simple linear regression, there is no substantial increase in the R 2 of NEE estimation based on multiple stepwise regression. However, the ML algorithms greatly improved R 2 values. In particular, the gradient boosting regression model exhibits the best performance (R 2 = 0.856). This study demonstrates that high-resolution UAV multispectral remote sensing shows great potential in improving the spatial and temporal estimating of maize daytime NEE variations.