Improving the Spatial and Temporal Estimation of Maize Daytime Net Ecosystem Carbon Exchange Variation Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Abstract

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.

Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions

“…Suitable mathematical models quantitatively reflect the relationship between VIs and the SPAD value with a stable model structure and parameters. In previous research, both the traditional algorithm of fitting model parameters (Peng et al, 2018) and the machine learning algorithm (Peng et al, 2021), which has developed rapidly in recent years, have been applied in nutrient estimation. It is meaningful to explore which algorithm is most suitable for monitoring the SPAD value of H. hainanensis seedlings.…”

Spatio-Temporal Variation and Future Sustainability of Net Primary Productivity from 2001 to 2021 in Hetao Irrigation District, Inner Mongolia

“…The 8-day variation of the Hetao Irrigation District in Inner Mongolia from 2001 to 2021 reveals outliers in August of both 2019 and 2021 (Figure 2a), which may be attributed to data attenuation and distortion caused by weather conditions or aging of satellite sensors [39]. The mean NPP value in July 2018 reached its maximum at 17.726 gCm −2 (Figure 2b).…”

Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms

“…high monitoring efficiency, and an absence of impact on field operations [22], thereby making it easy to obtain high-resolution remote sensing images at the farmland scale [23,24]. Now, the UAV remote sensing method has been widely used in crop species identification, yield estimation, water stress diagnosis, lodging information extraction, and other agricultural information applications [25][26][27][28].…”

“…Compared with the disadvantages of satellite remote sensing, such as vulnerability to weather, a long revisit cycle, and low temporal and spatial resolution, UAV remote sensing, as an emerging remote sensing form, has the advantages of a low operating cost, high monitoring efficiency, and an absence of impact on field operations [22], thereby making it easy to obtain high-resolution remote sensing images at the farmland scale [23,24]. Now, the UAV remote sensing method has been widely used in crop species identification, yield estimation, water stress diagnosis, lodging information extraction, and other agricultural information applications [25][26][27][28].…”