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Open AccessArticle

Measuring Landscape Albedo Using Unmanned Aerial Vehicles

1
Yale-NUIST Center on Atmospheric Environment & Jiangsu Key Laboratory of Agriculture Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA
3
WeRobotics, 1812 Bolton Street, Baltimore, MD 21217, USA
4
Center for Earth Observation, Yale University, New Haven, CT 06511, USA
5
Key Laboratory of Transportation Meteorology, China Meteorological Administration & Jiangsu Institute of Meteorological Sciences, Nanjing 210009, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1812; https://doi.org/10.3390/rs10111812
Received: 11 September 2018 / Revised: 11 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
(This article belongs to the Special Issue Remotely Sensed Albedo)
Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation. View Full-Text
Keywords: Unmanned Aerial Vehicle (UAV); albedo; landscape; consumer-grade camera; radiometric calibration Unmanned Aerial Vehicle (UAV); albedo; landscape; consumer-grade camera; radiometric calibration
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MDPI and ACS Style

Cao, C.; Lee, X.; Muhlhausen, J.; Bonneau, L.; Xu, J. Measuring Landscape Albedo Using Unmanned Aerial Vehicles. Remote Sens. 2018, 10, 1812.

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