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Abstract
Urban surface albedo is important for investigating urban surface–atmosphere radiative heat exchanges. For modeling surface energy balance (SEB) at local and neighborhood scales, ground or unmanned aerial vehicle (UAV)-based multispectral remote sensing (RS) can be used to obtain high-spatial-resolution multispectral information for both horizontal and vertical urban surfaces. The existing narrow-to-broadband (NTB) conversion models, developed for satellite/high-altitude observation and large homogeneous rural/vegetated/snow zones, may not be suitable for downscaling to the local and neighborhood scales or the urban complex texture. We developed three NTB models following published methodologies for three common UAV-based multispectral cameras according to Sample_D, a sample group of extensive spectral albedos of artificial urban surfaces, and evaluated their performance and sensitivities to solar conditions and surface material class. The proposed models were validated with independent samples (Sample_V). A model considering albedo physics was improved by multiplying different variables with respect to the camera (termed as “Model_phy_reg”), which initially proved to be the most accurate with a root mean square error of up to 0.02 for Sample_D and approximately 0.029 for Sample_V, meeting the required accuracy of total shortwave albedo for SEB modeling. The accuracy of Model_phy_reg was not much prone to the solar conditions.
Highlights
Albedo quantifies the capacity of an urban surface to reflect solar radiation, which is a driving factor of the surface energy balance (SEB) [1,2]
As compared to the performance of another physics-based conversion model against Sentinel-2A products in urban Perugia with an Root Mean Square Error (RMSE) of approximately 0.02 [17], Model_phy’s RMSE value was higher by 0.01 (Figure 4). This may be due to the close-to-visible wavelength for NIR channel (< 1000 nm) and narrower bandwidth of spectral bands provided by unmanned aerial vehicle (UAV)-based cameras, whereas the spectral bands provided by spaceborne sensors (e.g., Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-2 Multispectral Instrument (MSI), etc.), to which the physics-based models are well-adapted to, could better capture the spectral variation of albedo
The aim of this study is to provide applicable and easy-to-use NTB conversion models for UAV-based multispectral cameras and urban textures
Summary
Albedo quantifies the capacity of an urban surface to reflect solar radiation, which is a driving factor of the surface energy balance (SEB) [1,2]. While studying the urban microclimate at local and neighborhood scales, in situ “point” measurements and satellite RS have their limitations. The former is time-consuming, labor-intensive, provides limited points of each surface, and cannot access certain surfaces (e.g., roofs), whereas the spatial resolution of the latter is not sufficient (e.g., even with Sentinel 2, which has a 10 m spatial resolution [16,17], small structures show mixed pixels [18]). Rapid development of unmanned aerial vehicles (UAVs) equipped with spectral cameras has enabled obtaining both horizontal and vertical urban surface albedo with fine spatial resolution to further bridge the gap between in situ and spaceborne/airborne observations. We focused on the third process, NTB conversion, following the same assumptions as those for isotropic reflection
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