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Open AccessArticle

Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions

1
College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China
2
Department of Agronomy, University of Agriculture Faisalabad, Burewala 61010, Pakistan
3
Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 397; https://doi.org/10.3390/rs12030397
Received: 7 January 2020 / Revised: 21 January 2020 / Accepted: 22 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue Remote Sensing of Biophysical Parameters)
Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables. View Full-Text
Keywords: unmanned aircraft vehicle; multispectral sensor; vegetation indices; rapeseed crop; site-specific farming unmanned aircraft vehicle; multispectral sensor; vegetation indices; rapeseed crop; site-specific farming
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Hussain, S.; Gao, K.; Din, M.; Gao, Y.; Shi, Z.; Wang, S. Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions. Remote Sens. 2020, 12, 397.

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