Measuring Landscape Albedo Using Unmanned Aerial Vehicles

Chang Cao,Xuhui Lee,Jiaping Xu,Laurent Bonneau,Joseph Muhlhausen

doi:10.3390/rs10111812

Abstract

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.

Highlights

Surface albedo is a key parameter in the surface energy balance, and it plays an important role in land–climate interactions
These results indicate that the spectrometer calibration experiment should be conducted under the sky conditions that match those of the unmanned aerial vehicles (UAVs) experiment
In this paper we tested a workflow for landscape albedo determination using images acquired by drone cameras

Summary

Introduction

Surface albedo is a key parameter in the surface energy balance, and it plays an important role in land–climate interactions. As a key biophysical property of land ecosystems, surface albedo can change throughout the season, due to changes in the vegetation morphology, and it can be affected by sky conditions [1]. Quantification of the surface albedo at the landscape scale is still subject to many sources of uncertainty, especially over urban land [1,2]. UAVs can cover areas ranging from 0.01 km to 100 km, depending on battery life and type of UAV [7] They provide measurements at sub-decimeter spatial resolutions, and they can be used to obtain data under both clear sky and cloudy conditions [8,9]. UAVs can measure albedos at locations that are not accessible by ground-based instruments, such as steep rooftops in cities

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Discussion

Conclusion