| Estimation of maize leaf chlorophyll contents based on UAV hyperspectral drone image |
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| DOI：10.7606/j.issn.1000-7601.2019.01.09 |
| Key Words: UAV hyperspectral image maize chlorophyll estimate model |
| Author Name | Affiliation | | CHANG Xiao-yue | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | CHANG Qing-rui | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | WANG Xiao-fan | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | CHU Dong | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | GUO Run-xiu | College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| UAV remote sensing system can quickly acquire high-resolution remote sensing images on farmland scale, which is significant for crop growth monitoring and agricultural production management. In this research, the hyperspectral images of maize field were acquired with a UHD185 camera mounted on a drone. The spectral parameters were extracted from the hyperspectral images to construct models for estimating chlorophyll content in maize leaves. Chlorophyll distribution maps of maize leaf were inversely estimated using these models. The results showed that the simple regression model separately built with red edge area (SDr), maximum first derivative values within red edge (Dr), or difference vegetation index (DVI) had higher modeling accuracy. These inversion models were used to make SPAD value distribution map of maize leaves, then, they were validated against observed results for the accuracy of the map, it was found that SPAD-Dr model was the best one in estimating the chlorophyll of maize leaves (R2=0.89, RMSE=1.28, and RE=2.31). Therefore, this new method is feasible to be used for estimating the chlorophyll content of maize leaves. |
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