Estimation of chlorophyll content of summer maize canopy based on UAV multispectral remote sensing
CHEN Hao1,2, FENG Hao2,3,4 *, YANG Zhenting1,2, WANG Naijiang1,2, LI Yue1,2, WANG Qingsong1,2
1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; 2. Institute of Water-saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling, Shaanxi 712100, China; 3. Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, China; 4. Institute of Soil and Water Conservation, CAS & MWR, Yangling, Shaanxi 712100, China
Abstract：The remote sensing of unmanned aerial vehicle(UAV)has the advantages of monitoring crop nutritional status accurately and flexibly. Modeling crop canopy chlorophyll content is of great significance for efficient agricultural management. In the present study, the multispectral remote sensing data of UAV at different levels of nitrogen fertilizer rate were used to estimate the chlorophyll content of summer maize canopy in 2019. Firstly, 10 spectral vegetation indexes were selected to determine the vegetation indices that were significantly related to the chlorophyll content of summer maize canopy. Secondly, the estimation models of chlorophyll content were established by linear regression and stepwise regression analyses. The results showed that the chlorophyll content of summer maize canopy increased first and then decreased with the nitrogen fertilizer rate increasing. There is no significant difference in chlorophyll content between different topdressing treatments at the same level of nitrogen fertilizer rate. 9 out of 10 spectral vegetation indexes were significantly correlated with chlorophyll content, especially GNDVI whose correlation coefficient with chlorophyll content was the highest and reached 0.892. In comparison with linear regression models, the stepwise regression model showed the best performance in modeling chlorophyll content with coefficient of determination of 0.87, root mean square error of 0.15, and relative error of 2.68%. Therefore, the real-time monitoring of chlorophyll content for summer maize canopy can be achieved by UAV multispectral remote sensing combined with stepwise regression model at the field scale.
