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Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters

Author

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  • Umut Hasan

    (College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, China)

  • Mamat Sawut

    (College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, China
    Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, China)

  • Shuisen Chen

    (Guangzhou Institute of Geography, Guangzhou 510070, China)

Abstract

The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R 2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management.

Suggested Citation

  • Umut Hasan & Mamat Sawut & Shuisen Chen, 2019. "Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters," Sustainability, MDPI, vol. 11(23), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6829-:d:293039
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    Citations

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    Cited by:

    1. Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.
    2. Meixuan Li & Xicun Zhu & Wei Li & Xiaoying Tang & Xinyang Yu & Yuanmao Jiang, 2022. "Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    3. Min Yan & Yonghua Xia & Xiangying Yang & Xuequn Wu & Minglong Yang & Chong Wang & Yunhua Hou & Dandan Wang, 2023. "Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    4. Maria Victoria Bascon & Tomohiro Nakata & Satoshi Shibata & Itsuki Takata & Nanami Kobayashi & Yusuke Kato & Shun Inoue & Kazuyuki Doi & Jun Murase & Shunsaku Nishiuchi, 2022. "Estimating Yield-Related Traits Using UAV-Derived Multispectral Images to Improve Rice Grain Yield Prediction," Agriculture, MDPI, vol. 12(8), pages 1-28, August.
    5. Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
    6. Yingying Xing & Xiaoli Niu & Ning Wang & Wenting Jiang & Yaguang Gao & Xiukang Wang, 2020. "The Correlation between Soil Nutrient and Potato Quality in Loess Plateau of China Based on PLSR," Sustainability, MDPI, vol. 12(4), pages 1-17, February.

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