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.
Thermotronics: Towards Nanocircuits to Manage Radiative Heat Flux
