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MEaSUREs

MEaSUREs land surface temperature and emissivity ESDR

Title

Making Earth System Data Records (ESDR) for Use in Research Environments (MEaSUREs) is a NASA initiative to build long-term, consistent data records across the globe. Land Surface Temperature and Emissivity (LST&E) data are critical variables for monitoring the long-term effects of climate change and studying a variety of Earth surface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important ESDR by NASA and many other international organizations including the Global Climate Observing System (GCOS), which recently included LST as an Environmental Climate Variable (ECV). Accurate knowledge of the LST&E at high spatial (1km) and temporal (hourly) scales is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. Currently LST&E products are produced using different retrieval methodologies resulting in discontinuities in the time-series, and no single satellite exists that is capable of providing global LST&E products at both high spatial and temporal resolution. LST&E data products are currently created from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites, as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES). Sensors in LEO orbits provide global coverages at moderate spatial resolutions (~1km) but more limited temporal coverage (twice daily), while sensors in GEO orbits provide more frequent measurements (hourly) at lower spatial resolution (~3-4km) over a geographically restricted area.

The MEaSUREs LST&E products are built through collaboration between the Jet Propulsion Laboratory, University of Wisconsin Madison, University of Maryland College Park, and the United States Department of Agriculture (USDA).

 

The following long-term data records are currently in production:

 

Product

Spatial resolution    

Coverage

Temporal resolution             

Time period

LEO LST-ESDR

1km

Global

Daily, 8-day and monthly

2000 – 2023

GEO LST-ESDR

5km

N. and S. America    

N. America – hourly

S. America – 3 hourly

2000 – 2023  

LEO LSE-ESDR*   

(CAMEL)

5km

Global

Monthly

2000 – 2023

 

* Version 1 has being uploaded to LP DAAC for distribution (time period: 2000 – 2016).

The CAMEL data is available from the LP DAAC. The algorithm theoretical basis document describes the full algorithm.

 The GEO-LST data is available from the LP DAAC. The algorithm theoretical basis document describes the full algorithm.

 

Product: LSE-ESDR

Name: Combined ASTER/MODIS Emissivity over Land (CAMEL)

Status: The CAMEL data is available from the LP DAAC.

Method: A unified high spectral resolution emissivity database has been created by merging the MODIS baseline-fit emissivity database (MODBF) produced at the University of Wisconsin-Madison and the ASTER Global Emissivity Database (ASTER GED) produced at JPL. The unified Emissivity ESDR will be produced globally at 5km in mean monthly time-steps and for 13 bands from 3.6-14.3 micron and extended to 417 bands using a PC regression approach.

Fig1

 

Product: LEO LST-ESDR

Status: To be replaced by MOD21.

Rationale: 

We assessed the accuracy of the individual MYD11 and MYD21 LST products along with the unified MYD11+MYD21 (original LEO LST) product over a suitable set of validation sites representative of wide range of atmospheric conditions and surface types (water, grass, forest, sands, rocks) using 6 full years of data (2003-2005, 2013-2015) over the Southwestern USA (Figure 1). This can be described as Stage-2 validation according to NASA CEOS-LPV definition: Product accuracy is estimated over a significant set of locations and time periods by comparison with reference in situ or other suitable reference data. We used a Temperature-based validation method at JPL’s automated Lake Tahoe and Salton Sea validation sites, and a Radiance-based method at all other sites where in situ emissivity data was available. A summary of results in terms of RMSE in LST is given in Table 1, along with a final RMSE for all sites.

The results indicate that the MxD21 LST product performs the best on average at all sites when compared to MxD11 and the unified MxD21+MxD11 product. This can be attributed to the fact that:

1. The MxD11 product in Collection 6 has systematic cold biases over barren sites and this degrades the accuracy of the unified product even when taking into account the uncertainties in the ‘combination of states of information’ model used to unify the two products. This is illustrated in Figure 2 for the Great Sands barren site.

2. Introduction of a Water Vapor Scaling (WVS) method in the MxD21 product in 2014 greatly increased accuracy over vegetated and water surfaces to where accuracies with MxD11 were comparable. For example results over vegetated surfaces in Figure 3 and 4 at Texas Grassland and Redwood forest show similar if not better accuracy in MYD21 when compared to MYD11 and the unified product.

 

Fig 2: Validation results plotted as cumulative density histograms at Texas Grasslands, TX, daytime (left) and nighttime (right) between MOD21, MOD11 and unified MOD21+11 products. 

 

Product: GEO LST-ESDR

Status: Data is available from LP DAAC.

Method: LST information is derived from the GOES satellites from 2000 and onward. Atmospheric correction is done using MERRA-2 reanalysis fields with the RTTOV radiative transfer model, adjusted for the GEO characteristics, and CAMEL is used for the emissivity. The advantage of this approach is its consistency with the retrieval approach used at JPL to generate the MOD21 product.

Fig3

Fig4

 

Relevant Publications

 

Borbas et al., 2015: A Unified and Coherent Land Surface Emissivity Data Record, (poster) 20th International TOVS Study Conference, Lake Geneva, WI, USA, Oct 28-Nov 3, 2015.

Borbas et al., 2015:  A Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR) for Earth Science Research (poster). AGU Fall Meeting, San Francisco, CA, USA, Dec 14-18 2015

Borbas, E.E., Hulley, G., Feltz, M., Knuteson, R., Hook, S. (2018). The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application, Remote Sensing, 10(4), 643. https://doi.org/10.3390/rs10040643

Guillevic, P. C., Hulley, G., Hook, S., Hain, C., Anderson, M., Pinker, R., Borbas, E., Knuteson, R. (2014). A Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR) for Earth Science Research. American Meteorological Society (AMS) annual meeting. Phoenix, AZ, 4-8 January 2015.

Hain, C. R., M. C. Anderson, J. Otkin, K. Semmens, X. Zhan, L. Fang and Z. Li, 2014: Use of Land Surface Temperature Observations in a Two-Source Energy Balance Model Towards Improved Monitoring of Evapotranspiration and Drought, 2014 AGU Fall Meeting, San Francisco, CA, 15-19 December 2014.

Hain, C. R., M. C. Anderson, J. Otkin, K. Semmens, X. Zhan, L. Fang and Z. Li, 2014: ALEXI and Use of S-NPP for Vegetation, Evapotranspiration and Drought, Secon Suomi NPP Applications Workshop, Huntsville, AL, 18-20 November 2014.

Hulley, G., Hook S., and C. Hughes (2012), A unified MODIS Land Surface Temperature (LST) Earth System Data Record, GlobTemp Meeting, Edinburgh, Scotland, June 2012

Hulley, G., Malakar, N., Hook S., and P. Guillevic (2014), A unified MODIS Land Surface Temperature (LST) Earth System Data Record, RAQRS 2014, Valencia, Spain, 22-26 September 2014

Hulley, G., Hook S., Guillevic P., Sanchez, J-M, (2014), A unified MODIS Land Surface Temperature (LST) and Emissivity Earth System Data Record, AGU Fall Meeting 2014, San Francisco, CA, 14-19 Dec 2014

Hulley, G., Hook, S., and E. Abbott (2015), The ASTER Global Emissivity Dataset (GED): Mapping Earth's emissivity at 100 meter spatial resolution, Geophysical Research Letters. Geophys. Res. Lett., 42, doi:10.1002/2015GL065564.

Hulley et al., 2015:  MEASURES High Spectral Resolution MODIS/ASTER Emissivity Database, (talk), 20th International TOVS Study Conference, Lake Geneva, WI, USA, Oct 28-Nov 3, 2015.

Hulley, G., Hook S., Guillevic P., Sanchez, J-M, (2014), A unified MODIS Land Surface Temperature (LST) and Emissivity Earth System Data Record, AGU Fall Meeting 2014, San Francisco, CA, 14-19 Dec 2014

Hulley, G. C., T. Hughes, and S. J. Hook (2012), Quantifying Uncertainties in Land Surface Temperature (LST) and Emissivity Retrievals from ASTER and MODIS Thermal Infrared Data, Geophysical Research Letters, 117, D23113, doi:10.1029/2012JD018506.

Knuteson, R, E. Borbas, G. Hulley, S. Hook, M Anderson, R. Pinker, C. Hain and P. Guillevic, 2014: A Unified and Coherent Land Surface Emissivity Earth System Data Record, Poster, AGU Fall Meeting, San Francisco, 15-19 December 2014. https://wiki.earthdata.nasa.gov/download/attachments/43321891/AGU2014_Borbas_MEaSUREs_poster.pdf?api=v2

Knuteson et al., 2015:  Uncertainty Estimates of NASA Satellite LST over the Greenland and Antarctic Plateau: 2003-2015.  (talk) AGU Fall Meeting, San Francisco, CA, USA, Dec 14-18 2015.

Sharifi, A., Sun, L., Anderson, M., McCarty, G., Crow, W., Gao, F., & Sadeghi, A.M. Calibration of a Complex Watershed Model using High Resolution Remotely Sensed Evapotranspiration Retrievals. Water Resources Res. 2017., in preparation

Sun, L., Anderson, M.C., Gao, F., Hain, C.R., Alfieri, J.G., Sharifi, A., McCarty, G.W., Yang, Y., Yang, Y., Kustas, W.P., McKee, L. (2017). Investigating water use over the Choptank River watershed using a multi-satellite data fusion approach, Water Resources Research, 53 (7), 5298-5319. doi:10.1002/2017WR020700.

Yang, Y., Anderson, M.C., Gao, F., Hain, C.R., Semmens, K.A., Kustas, W.P., Normeets, A., Wynne, R.H., Thomas, V.A., & Sun, G. (2017). Daily Landsat-scale evapotranspiration estimation over a managed pine plantation in North Carolina, USA using multi-satellite data fusion. Hydrol. Earth Syst. Sci., 21, 1017-1037. doi:10.5194/hess-21-1017-2017

Yang, Y., Anderson, M.C., Gao, F., Wardlow, B. Hain, C.R., Otkin, J.A., Alfieri, J., Yang, Y., Sun, L., Dulaney, W. (2018). Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA, Remote Sensing of Environment, 210 387-402. doi:10.1016/j.rse.2018.02.020

Yang, Y., Anderson, M.C., Gao, F., Hain, C., Kustas, W.P., Meyers, T., Crow, W., Finocchiaro, R.G., Otkin, J.A., Sun, L., & Yang, Y. (2017) Impact of tile drainage on evapotranspiration (ET) in South Dakota, USA based on high spatiotemporal resolution ET timeseries from a multi-satellite data fusion system. J. Selected Topics in Applied Earth Obs. and Remote Sensing., 10 (6) 2550-2564. doi:10.1109/JSTARS.2017.2680411