Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

Muhammad Awais, Wei Li, Sajjad Hussain, Muhammad Jehanzeb Masud Cheema, Weiguo Li, Rui Song and Chenchen Liu
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Muhammad Awais: Research Center of Fluid Machinery Engineering & Technology, Jiangsu University, Zhenjiang 212013, China
Wei Li: Research Center of Fluid Machinery Engineering & Technology, Jiangsu University, Zhenjiang 212013, China
Sajjad Hussain: Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Muhammad Jehanzeb Masud Cheema: Faculty of Agricultural Engineering and Technology, PMAS-Arid Agricultural University, Rawalpindi 46000, Pakistan
Weiguo Li: Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
Rui Song: Research Center of Fluid Machinery Engineering & Technology, Jiangsu University, Zhenjiang 212013, China
Chenchen Liu: Research Center of Fluid Machinery Engineering & Technology, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 2, 1-19

Abstract: Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy ( R 2 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy ( R 2 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment.

Keywords: unmanned aerial vehicle; ArcGIS; thermal sensor; LST; orthomosaic; Landsat 8 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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