Next Article in Journal
Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image
Next Article in Special Issue
The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation
Previous Article in Journal
Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia
Previous Article in Special Issue
The Role of Climate and Land Use in the Changes in Surface Albedo Prior to Snow Melt and the Timing of Melt Season of Seasonal Snow in Northern Land Areas of 40°N–80°N during 1982–2015
Open AccessArticle

Measuring Landscape Albedo Using Unmanned Aerial Vehicles

Yale-NUIST Center on Atmospheric Environment & Jiangsu Key Laboratory of Agriculture Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA
WeRobotics, 1812 Bolton Street, Baltimore, MD 21217, USA
Center for Earth Observation, Yale University, New Haven, CT 06511, USA
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;
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop