STAR Joint Polar Satellite System Algorithms & Data Products website National Oceanographic & Atmospheric Administration website NOAA Center for Satellite Applications and Research website
VIIRS Global LST, April 8, 2014 - click to enlarge

VIIRS Global LST, April 8, 2014 - click to enlarge

Global Monthly Mean from May 2014 of diurnal range of LST from S-NPP VIIRS - click to enlarge

Global Monthly Mean from May 2014 of diurnal range of LST from S-NPP VIIRS - click to enlarge

Land Surface Temperature (LST)

Team Lead: Yunyue (Bob) Yu

Background

Land surface temperature, a key indicator of the Earth surface energy budget, is widely required in applications of hydrology, meteorology, and climatology. It is of fundamental importance to the net radiation budget at the Earth surface and to monitoring the state of crops and vegetation, as well as an important indicator of both the greenhouse effect and the energy flux between the atmosphere and ground (Norman & Becker, 1995; Li & Becker, 1993;). LST is one of the land EDRs for the JPSS mission. Maturity status of the S-NPP product generation is defined as beta, provisional and validated versions; the LST beta and provisional productions were started in December 2012 and June 2014, respectively. The validated V1 version readiness review was approved in December 2014.

Algorithm Science and Data Access

VIIRS, aboard S-NPP, provides measurements of the atmospheric, land, and oceanic parameters which are referred to as EDRs. The LST EDR is the measurement of the skin temperature over global land coverage including coastal and inland- water. Currently, The VIIRS LST EDR is derived from a baseline split-window regression algorithm (Yu et al., 2005):

Land surface temperature equation

Products and data:

EDR Long Term-Monitoring

Documentation

where (k=0 to 4) are the algorithm coefficients, which are based on 17 International Geosphere-Biosphere Programme (IGBP) land surface types (i=0 to 16) and day/night conditions (j=0 to 1); θ is the satellite viewing zenith angle. The two VIIRS thermal infrared spectral bands currently being used for this split window algorithm are the M15 band (centered at 10.76 µm) and the M16 band (12.01 µm). Note that this is a surface type-dependent algorithm; the VIIRS Surface Type EDR is used as input for the surface type information.

The official JPSS LST EDR product can be accessed from CLASS.

Users

In the US, demands of satellite LST data including the VIIRS LST EDR are from a variety of government agencies including the NOAA, Department of Agriculture (USDA), Environmental Protection Agency (EPA), Department of the Interior (DOI), Department of Defense (DOD), as well as from universities and research institutes worldwide.

Calibration and Validation

Two approaches of satellite LST product validation techniques are adapted to validate the VIIRS LST product: temperature based method (T-based) and the radiance based (R-based) methods [Wan and Li, 2008]. The T-based method is a direct comparison analysis of ground in-situ LST estimates and the satellite derived LSTs, which is based on the assumption that the ground in-situ LST estimates are good reference of the satellite LSTs at pixel level. Currently we utilized the in-situ LSTs from the U.S. SURFace RADiation budget observing network (SURFRAD) [Sun et al., 2003; Yu et al., 2009; Liu et al., 2013; Wang and Liang, 2009]. In the R-based validation, we utilized a radiative transfer simulation tool (i.e. MODTRAN) for the reference LST estimation using NCEP atmospheric profiles for the atmospheric correction computation. The team has provided a preliminary validation report which indicates that VIIRS LST product meets the mission requirements.

Ongoing Improvements

Although the VIIRS LST EDR has passed criteria of the validated V1 release, the team is still working hard on some critical improvements. First, a comprehensive validation effort is ongoing, for extending the in-situ validation over all the 17 IGBP surface types and for global of geographic regions. The team is also in collecting of high accurate atmospheric profiles for accurate R- based validation. Second, an emissivity explicit algorithm (via the current surface-type-dependent algorithm) is in evaluation, for improving impact of emissivity variation of certain surface types. A model application effort is proposed in which the VIIRS LST data will be compared to the LST output of numerical weather models. Finally, In addition, a long-term validation tool is in development for monitoring performance of the VIIRS LST production.

References

Li, Z.-L., & Becker, F. (1993). Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sensing of Environment, 43, 67-85.

Liu, Y., Yu, Y., Sun, D., Tarpley, D. and Fang, L. (2013), Effect of Different MODIS Emissivity Products on Land-Surface Temperature Retrieval From GOES Series, IEEE Geoscience and Remote Sensing Letters, 10(3):510-514. DOI: 10.1109/LGRS.2012.2211992

Norman, J. M., & Becker, F. (1995). Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology, 77, 153-166.

Sun, D., & Pinker, R. T. (2003). Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). Journal of Geophysical Research, 108, 4326.

Wan, Z., & Li, Z.-L. (2008). Radiance-based validation of the V5 MODIS land-surface temperature product. International Journal of Remote Sensing, 29, 5373-5395.

Wang, K., & Liang, S. (2009). Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 113, 1556-1565.

Yu, Y., Tarpley, D., Privette, J. L., Goldberg, M. D., Rama Varma Raja, M. K., Vinnikov, K. Y.,et al. (2009). Developing algorithm for operational GOES-R land surface temperature product. IEEE Transactions on Geoscience and Remote Sensing, 47, 936-951.

Y. Yu, J. L. Privette, and A. C. Pinheiro, Analysis of the NPOESS VIIRS land surface temperature algorithm using MODIS data, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 10, pp. 2340-2350, Oct. 2005.