Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

Meixuan Li, Xicun Zhu, Wei Li, Xiaoying Tang, Xinyang Yu and Yuanmao Jiang
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Meixuan Li: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Xicun Zhu: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Wei Li: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Xiaoying Tang: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Xinyang Yu: College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Yuanmao Jiang: National Apple Engineering and Technology Research Center, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an 271018, China

Sustainability, 2022, vol. 14, issue 4, 1-16

Abstract: The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R 2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R 2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data.

Keywords: arbor trees; dwarf trees; linear model; machine learning; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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