An improved model for emissivity retrieval of complex urban surfaces based on spectral indices from UAV

作者：Zhong, Xue; Zhao, Lihua; Zheng, Haichao; Li, Yingtan; Zhang, Yang; Ren, Peng^*

来源：Sustainable Cities and Society, 2022, 84: 104032.

DOI：10.1016/j.scs.2022.104032

• Emissivity
• Spectral indices
• Hyperspectral images
• UAV
• Retrieval

The estimation of land surface energy budgets and land surface temperature (LST) require accurate information on land surface emissivity (LSE). Despite the use of remote sensing or low-altitude sensing data in numerous models for the computation of LSE, it is still highly challenging to precisely predict the LSE of highheterogeneous surfaces on a micro-scale. This paper thus proposes several individual models to retrieve each surface's emissivity on the pixel level. Various emissivity and reflectance values were obtained upon spectral resampling to found individual models between emissivity and spectral indices (NDVI, DVI, MSAVI, OSAVI, RRI) for classified surfaces. Based on the smallest root mean square error (RMSE), DVI models accurately indicated the emissivity of concrete floors (0.0012), asphalt roads (0.0012), grasses (0.0062), parking lots (0.0035), pavements (0.0057), and red manhole covers (0.0089). RRI models better predicted the emissivity of tiles (0.0039), grey manhole covers (0.0092), and shrubs (0.0005). OSAVI was also the ideal index for retrieving the emissivity of rooftops (0.0009). Validation results showed their effectiveness, with accuracy within 0.001. Finally, temperature errors caused by the deviation between traditional or default emissivity and measured emissivity fluctuated from 0.0 K to 2.9 K or 0.2 K to 3.7 K, respectively.