National Earth System Science Data Center GLASS Broadband Emissivity (BBE) Download:
Introduction:
The GLASS BBE product represents the emissivity value at 8-13.5 um since it is the optimal spectral range for estimating surface longwave net radiation (Cheng and Liang 2013).
The algorithm estimates surface broadband emissivity for land surface types include: water, snow or ice, bare soils (0 < NDVI< 0.156), vegetated surfaces (NDVI>0.156), and transition zone(0.1 < NDVI< 0.2). BBEs of water and snow or ice were set to 0.985 based on a combination of BBE calculated from the emissivity spectra in the ASTER spectral library and the MODIS UCSB spectral library, and BBE values simulated using radiative transfer model (Cheng et al. 2010).
For bare soils, we estimate BBE using MODIS spectral albedos(Cheng and Liang 2014). This algorithm takes advantage of both ASTER BBE and MODIS shortwave albedo products,as well as the established non-linear relationship between ASTER BBE and seven MDOSI spectral albedos. The rationality of the algorithm was verified by a study (Cheng et al. 2018) that demonstrated the physical linkage between land surface emissive and reflective variables over non-vegetated surfaces.
For vegetated surfaces, we estimate the BBE by constructing a look-up table (LUT) based on the 4SAIL radiative transfer model(Cheng et al. 2016). The BBE of the vegetated surfaces was derived from the LUT using three inputs: leaf BBE, soil BBE, and LAI.
The method for estimating transition BBE is similar to that for bare soils. Areas of overlapping bare soils and transition zone as well as of transition zone and vegetated surfaces are used. Their BBE is assumed to be their average values.
The algorithm for estimating BBE from AVHRR data was similar to that designed for MODIS(Cheng and Liang 2013). The difference is that we use AVHRR surface visible near-infrared reflectance to replace the MODIS spectral albedos.
References:
[1]Cheng, J., & Liang, S. (2013). Estimating global land surface broadband thermal-infrared emissivity from the Advanced Very High Resolution Radiometer optical data. International Journal of Digital Earth, DOI: 10.1080/17538947.17532013.17783129
[2]Cheng, J., Liang, S., Weng, F., Wang, J., & Li, X. (2010). Comparison of Radiative Transfer Models for Simulating Snow Surface Thermal Infrared Emissivity. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 3, 323-336
[3]Cheng, J., & Liang, S. (2014). Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product. Journal of Geophysical Research: Atmospheres, 119, 614-634
[4]Cheng, J., Liang, S.L., Nie, A.X., & Liu, Q. (2018). Is There a Physical Linkage Between Surface Emissive and Reflective Variables Over Non-Vegetated Surfaces? Journal of the Indian Society of Remote Sensing, 46, 591-596
[5]Cheng, J., Liang, S., Verhoef, W., Shi, L., & Liu, Q. (2016). Estimating the Hemispherical Broadband Longwave Emissivity of Global Vegetated Surfaces Using a Radiative Transfer Model. IEEE Transactions on Geoscience and Remote Sensing, 54, 905-917