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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #379370

Research Project: Improved Aerial Application Technologies for Precise and Effective Delivery of Crop Production Products

Location: Aerial Application Technology Research

Title: Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring

item ZHANG, JIAN - Huazhong Agricultural University
item WANG, CHUFENG - Huazhong Agricultural University
item Yang, Chenghai
item XIE, TIANJIN - Huazhong Agricultural University
item JIANG, ZHAO - Huazhong Agricultural University
item HU, TAO - Huazhong Agricultural University
item LUO, ZHIBANG - Huazhong Agricultural University
item ZHOU, GUANGSHENG - Huazhong Agricultural University
item XIE, JING - Huazhong Agricultural University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2020
Publication Date: 11/18/2020
Citation: Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., Xie, J. 2020. Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring. Remote Sensing. 12:1207.

Interpretive Summary: The spatial resolution of aerial imagery from unmanned aerial vehicles (UAV) has a crucial effect on crop growth monitoring performance and image acquisition efficiency. In this study, a UAV-based imaging system was flown at five altitudes to obtain imagery with spatial resolutions from 1.4 cm to 11.6 cm. Vegetation indices derived from the aerial images at the five resolutions were related to ground-measured plant growth parameters for rapeseed growth monitoring. Results showed images with a spatial resolution of 2.6 cm or finer performed better for estimating plant growth parameters. As finer spatial resolution required long image acquisition and processing time, a spatial resolution around 2.6 cm could be a preferred selection for seedling rapeseed growth monitoring. The findings and methodologies from this study provide useful and practical information for crop growth monitoring using UAV remote sensing technology.

Technical Abstract: The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22 m, 29 m, 44 m, 88 m, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35 cm, 1.69 cm, 2.61 cm, 5.73 cm, and 11.61 cm, respectively. Meanwhile, the normalized di'erence vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD ('5.73 cm), imprecise PH information and large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about 3 times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have better performance for low spatial resolution images.