Quality Assessment of S-NPP VIIRS Land Surface Temperature Product
"> Figure 1
<p>(<b>a</b>) is Geographic landscape in Gobabeb station in Namibia and (<b>b</b>) is the instrumentation for LST measurement: two radiometers measure the surface-leaving radiance (9.6–11.5 μm) from the gravel plain, which is highly homogenous over at least 2500 km<sup>2</sup>. A third radiometer measures sky radiance.</p> "> Figure 2
<p>Scatter plots of the VIIRS LSTs (<b>a</b>) and MODIS LSTs (<b>b</b>) against the SURFRAD LSTs compared in the period from February 2012 to April 2015. Overall accuracy and precision of the satellite LSTs referring the SURFRAD LSTs are noted, as well as the daytime and nighttime cases. Some VIIRS LST plots are circled as suspicious cloud contaminated plots (red).</p> "> Figure 3
<p>Scatter plots of the VIIRS LSTs (blue) and the AATSR LSTs (red) against the SURFRAD LSTs compared in the period from 1 February 2012 to 8 April 2012. Overall accuracy and precision of the satellite LSTs referring the SURFRAD LSTs are noted. Some VIIRS LST plots are circled as suspicious cloud contaminated plots.</p> "> Figure 4
<p>Validation result against the data in Gobabeb, Namibia in 2012: VIIRS LST (<b>a</b>) and MODIS LST V5 (<b>b</b>).</p> "> Figure 5
<p>Cross-comparison results between VIIRS and AQUA for the whole period and area under analysis. (<b>a</b>) all comparison results under cloud clear condition ; (<b>b</b>) based on a, spatial variation tests are added ; (<b>c</b>) based on b, angle difference is added ; (<b>d</b>) based on c, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST.</p> "> Figure 6
<p>Cross-comparison results between VIIRS and AQUA of the case study on 28 December 2013. (<b>a</b>) Overall comparison results under cloud clear condition; (<b>b</b>) Brightness temperature comparison of VIIRS band 15 and MODIS Aqua band; (<b>c</b>) the BT difference comparison between VIIRS (BT15-BT16) and MODIS (BT31-BT32); (<b>d</b>) 31 based on a, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST</p> "> Figure 7
<p>Global BT difference distribution map for 19 December 2014 at daytime (<b>a</b>) and nighttime (<b>b</b>); 4 July 2014 at daytime (<b>c</b>) and nighttime (<b>d</b>).</p> "> Figure 8
<p>LST uncertainty associated with the uncertainty in surface type classification. These values are estimated using the simulation dataset for all surface types and day/night conditions.</p> "> Figure 9
<p>Impact of surface type accuracy (blue line, ranging from 0 to 1) on LST uncertainty (red line, in K) for daytime (<b>a</b>) and nighttime (<b>b</b>).</p> ">
Abstract
:1. Introduction
2. Data
2.1. VIIRS LST EDR
2.2. Reference Data
2.2.1. MODIS LST Product
2.2.2. AATSR LST Product
2.2.3. SURFRAD Ground Observations
No. | Site Location | Station Acronyms | Lat(N)/Lon(W) | Surface Type |
---|---|---|---|---|
1 | Bondville, IL | BON | 40.05/88.37 | Crop Land |
2 | Fort Peck, MT | FPT | 48.31/105.10 | Grass Land |
3 | Goodwin Creek, MS | GWN | 34.25/89.87 | Grassland |
4 | Table Mountain, CO | TBL | 40.13/105.24 | Grass/Crop Land |
5 | Desert Rock, NV | DRA | 36.63/116.02 | Shrub Land |
6 | Pennsylvania State University, PA | PSU | 40.72/77.93 | Mixed Forest |
7 | Sioux Falls, SD | SFX | 43.73/97.49 | Cropland |
2.2.4. Ground Observation at Gobabeb, Namibia
2.3. Quality Control Procedures
- (1)
- Ground data quality control
- (2)
- Satellite data quality control
- (3)
- Match up process
3. LST Assessment Methodology
3.1. T-Based Validation Method
3.2. Cross Satellite Comparison Method
3.3. VIIRS LST Uncertainty to Input Imprecision
No. | IGBP Land Surface Type | Percentage |
---|---|---|
1 | Evergreen Needle Leaf Forests | 1.91 |
2 | Evergreen Broadleaf Forests | 9.25 |
3 | Deciduous Needle leaf Forests | 1.17 |
4 | Deciduous Broadleaf Forests | 0.78 |
5 | Mixed Forests | 5.85 |
6 | Closed Shrub Lands | 0.06 |
7 | Open Shrub Lands | 15.18 |
8 | Woody Savannahs | 8.16 |
9 | Savannahs | 8.13 |
10 | Grasslands | 8.45 |
11 | Permanent Wetlands | 0.87 |
12 | Croplands | 7.25 |
13 | Urban build-up | 0.39 |
14 | Croplands/Natural Vegetation Mosaics | 4.11 |
15 | Snow ice | 10.46 |
16 | Barren | 12.54 |
17 | Water Bodies | 5.45 |
4. Results
4.1. Comparison with SURFRAD Data
Season | Samples | Overall | Day | Night | |||
---|---|---|---|---|---|---|---|
Bias | STD | Bias | STD | Bias | STD | ||
Spring | 1549 | −0.57 | 2.55 | −0.58 | 3.16 | −0.56 | 2.13 |
Summer | 1433 | −0.12 | 2.46 | −0.90 | 3.70 | 0.26 | 1.40 |
Fall | 1734 | −0.23 | 1.82 | −0.46 | 1.97 | −0.07 | 1.70 |
Winter | 1372 | −0.72 | 2.21 | −0.85 | 1.80 | −0.63 | 2.44 |
Viirs_lst | Surfrad_lst | BT15 | BT16 | Date | Time | STZ | STAZ | SOZ | SOAZ |
---|---|---|---|---|---|---|---|---|---|
312.19 | 304.77 | 306.05 | 302.51 | 2013140 | 1905 | 33.39 | −98.15 | 26.34 | −133.20 |
313.65 | 305.98 | 308.04 | 304.88 | 2013163 | 1835 | 14.57 | 78.10 | 19.43 | −146.77 |
311.39 | 301.43 | 306.27 | 303.76 | 2013164 | 1815 | 39.15 | 74.33 | 17.73 | −159.42 |
313.91 | 304.41 | 307.21 | 303.14 | 2013169 | 1825 | 32.07 | 75.08 | 17.95 | −155.56 |
310.84 | 303.62 | 304.40 | 300.88 | 2013170 | 1805 | 50.70 | 72.09 | 16.85 | −169.68 |
313.14 | 305.44 | 308.69 | 306.60 | 2014128 | 1855 | 6.24 | −100.44 | 27.15 | −142.34 |
312.03 | 301.27 | 308.06 | 306.73 | 2014130 | 1815 | 44.90 | 73.11 | 23.26 | −161.37 |
308.33 | 300.28 | 302.04 | 299.51 | 2014130 | 2000 | 67.60 | −90.80 | 35.98 | −118.65 |
309.05 | 302.28 | 305.02 | 303.27 | 2014144 | 1855 | 6.25 | −97.24 | 24.05 | −137.94 |
311.75 | 303.35 | 307.29 | 305.41 | 2014151 | 1825 | 38.72 | 74.25 | 19.61 | −154.49 |
309.26 | 303.50 | 304.43 | 302.02 | 2014154 | 1910 | 25.26 | −99.96 | 24.39 | −130.55 |
306.46 | 299.39 | 303.54 | 302.82 | 2014165 | 1900 | 15.91 | −99.07 | 22.50 | −132.85 |
Site | Surface Types | Samples Number | Overall | Nighttime | Daytime | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Bias | Std | Rmse | Bias | Std | Rmse | Bias | Std | Rmse | |||
BON | Cropland | 768 | −0.42 | 2.92 | 2.95 | −0.48 | 2.05 | 2.10 | −0.27 | 4.33 | 4.33 |
BON | Snow/ice | 39 | 0.12 | 1.34 | 1.33 | 0.12 | 0.83 | 0.80 | 0.12 | 1.50 | 1.48 |
DRA | Closed Shrublands | 97 | −0.96 | 1.42 | 1.71 | −1.32 | 0.84 | 1.56 | −0.45 | 1.88 | 1.91 |
DRA | open shrublands | 1128 | −0.18 | 1.57 | 1.58 | −0.58 | 0.88 | 1.05 | 0.26 | 2.00 | 2.01 |
DRA | Barren | 149 | −0.23 | 1.55 | 1.56 | −1.04 | 0.75 | 1.28 | 0.87 | 1.67 | 1.88 |
FPK | Grass | 491 | −0.19 | 1.84 | 1.85 | 0.07 | 1.63 | 1.63 | −0.70 | 2.12 | 2.23 |
FPK | Crop/vegetation Mosaic | 90 | −1.13 | 2.61 | 2.83 | −1.70 | 2.86 | 3.31 | −0.08 | 1.69 | 1.67 |
FPK | Snow/ice | 56 | −3.16 | 5.57 | 6.36 | - | - | - | −3.16 | 5.57 | 6.36 |
GWN | Woody Savannahs | 390 | 0.06 | 2.69 | 2.69 | 1.39 | 1.75 | 2.23 | −2.10 | 2.56 | 3.30 |
GWN | Crop/vegetation Mosaic | 487 | −0.18 | 2.52 | 2.52 | 1.28 | 1.61 | 2.06 | −2.20 | 2.11 | 3.05 |
PSU | Deciduous broadleaf forests | 21 | −0.85 | 2.52 | 2.60 | −0.48 | 2.55 | 2.51 | −1.77 | 2.39 | 2.80 |
PSU | Grass | 157 | −0.28 | 1.85 | 1.86 | −0.21 | 1.93 | 1.93 | −0.37 | 1.75 | 1.77 |
PSU | Cropland | 35 | −1.16 | 2.20 | 2.46 | −1.21 | 2.38 | 2.63 | −0.91 | 1.04 | 1.31 |
PSU | Crop/vegetation Mosaic | 406 | −0.15 | 2.51 | 2.51 | −0.19 | 2.56 | 2.56 | 0.00 | 2.34 | 2.32 |
PSU | Snow/ice | 105 | −1.30 | 3.10 | 3.35 | −2.29 | 3.67 | 4.29 | −0.72 | 2.56 | 2.64 |
SXF | Cropland | 762 | −0.44 | 2.33 | 2.37 | −0.13 | 2.07 | 2.07 | −1.08 | 2.69 | 2.90 |
SXF | Snow/ice | 119 | −1.91 | 3.64 | 4.10 | −1.94 | 1.94 | 2.72 | −1.90 | 4.10 | 4.50 |
TBL | Grass | 749 | −0.68 | 1.81 | 1.94 | −0.70 | 1.59 | 1.74 | −0.63 | 2.35 | 2.43 |
TBL | Snow/ice | 41 | −1.36 | 1.80 | 2.24 | −2.43 | 0.80 | 2.54 | −1.06 | 1.90 | 2.14 |
4.2. Comparison with Data from Gobabeb, Namibia
4.3. Cross Comparison with MODIS Aqua LST
Surface Type | Night | Day | ||||
---|---|---|---|---|---|---|
Bias | STD | Samples | Bias | STD | Samples | |
Evergreen Needleleaf Forest | −0.36 | 1.15 | 31 | 0.24 | 1.01 | 12 |
Evergreen broadleaf Forest | −0.06 | 0.93 | 11110 | 0.20 | 0.92 | 40085 |
Deciduous Needleleaf Forest | 1.70 | 2.09 | 104 | −1.41 | 0.28 | 2 |
Deciduous Broadleaf Forest | 0.50 | 0.93 | 1947 | −0.46 | 0.97 | 1871 |
Mixed Forest | −0.10 | 1.28 | 5666 | −0.72 | 1.19 | 218 |
Closed Shrublands | 1.60 | 0.97 | 858 | - | - | - |
Open Shrublands | 2.11 | 1.34 | 166680 | −0.37 | 1.43 | 1097 |
Woody Savannahs | 0.15 | 1.13 | 124278 | 0.46 | 1.36 | 7728 |
Savannahs | 0.76 | 1.03 | 145338 | 3.34 | 1.01 | 505 |
Grasslands | 0.46 | 1.24 | 51831 | −0.33 | 1.35 | 259 |
Wetlands | 0.61 | 1.34 | 4371 | 1.72 | 1.13 | 340 |
Croplands | 0.21 | 1.23 | 26030 | −0.32 | 1.06 | 11583 |
Urban | 0.60 | 1.22 | 769 | 0.38 | 1.40 | 52 |
Natural Vegetation Mosaics | 0.44 | 1.04 | 53593 | 1.14 | 1.49 | 2551 |
Snow/ice | - | - | - | 0.47 | 0.57 | 552550 |
Barren | 2.04 | 1.24 | 1222 | 1.08 | 1.12 | 111549 |
Water | 1.40 | 1.53 | 3073 | −0.19 | 1.00 | 553 |
5. Discussion
5.1. Impact from the Non-Linear Term
5.2. Uncertainty due to Error of Surface Type
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Yu, Y.; Tarpley, D.; Privette, J.L.; Flynn, L.E.; Xu, H.; Chen, M.; Vinnikov, K.Y.; Sun, D.; Tian, Y. Validation of GOES-R satellite land surface temperature algorithm using SURFRAD ground measurements and statistical estimates of error properties. IEEE Trans. Geosci. Remote Sens. 2012, 50, 704–713. [Google Scholar]
- Meng, C.L.; Li, Z.-L.; Zhan, X.; Shi, J.C.; Liu, C.Y. Land surface temperature data assimilation and its impact on evapotranspiration estimates from the common land model. Water Resour. Res. 2009, 45, W02421. [Google Scholar] [CrossRef]
- Zheng, W.; Wei, H.; Wang, Z.; Zeng, X.; Meng, J.; Ek, M.; Mitchell, K.; Derber, J. Improvement of daytime land surface skin temperature over arid regions in the NCEP GFS model and its impact on satellite data assimilation. J. Geophys. Res. Atmos. 2012, 117, D06117. [Google Scholar] [CrossRef]
- Anderson, M.C.; Kustas, W.P.; Norman, J.M.; Hain, C.R.; Mecikalski, J.R.; Schultz, L.; Gonzalez-Dugo, M.P.; Cammalleri, C.; d’Urso, G.; Pimstein, A.; et al. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydro. Earth Sys. Sci. 2011, 15, 223–239. [Google Scholar] [CrossRef] [Green Version]
- Anderson, M.C.; Allen, R.G.; Morse, A.; Kustas, W.P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 2012, 122, 50–65. [Google Scholar] [CrossRef]
- Rajasekar, U.; Weng, Q. Urban heat island monitoring and analysis by data mining of MODIS imageries. ISPRS J. Photogramm. Remote Sens. 2009, 64, 86–96. [Google Scholar] [CrossRef]
- Li, H.; Sun, D.; Yu, Y.; Wang, H.; Liu, Y.; Liu, Q.; Du, Y.; Wang, H.; Cao, B. Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sens. Environ. 2014, 142, 111–121. [Google Scholar] [CrossRef]
- Jiménez-Muñoz, J.C.; Sobrino, J.A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res. 2003, 108, 4688–4695. [Google Scholar] [CrossRef]
- Prata, A.J. Land surface temperatures derived from the AVHRR and the ATSR. 1, Theory. J. Geophys. Res. 1993, 98, 689–16702. [Google Scholar]
- Coll, C.; Caselles, V.; Sobrino, J.A.; Valor, E. On the atmospheric dependence of the split-window equation for land surface temperature. Int. J. Remote Sens. 1994, 15, 105–122. [Google Scholar] [CrossRef]
- Wan, Z.; Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 1996, 34, 892–905. [Google Scholar]
- Gillespie, A.R.; Rokugawa, S.; Hook, S.J.; Matsunaga, T.; Kahle, A.B. Temperature/Emissivity Separation Algorithm Theoretical Basis Document, version 2.4; NASA/GSFC: Greenbelt, MD, USA, 1996. [Google Scholar]
- Yu, Y.; Tarpley, D.; Privette, J.; Goldberg, M.; Raja, M.; Vinnikov, K.; Xu, H. Developing algorithm for operational GOES-R land surface temperature product. IEEE Trans. Geosci. Remote Sens. 2009, 47, 936–951. [Google Scholar]
- Sun, D.; Pinker, R.T. Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). J. Geophys. Res. 2003, 108. [Google Scholar] [CrossRef]
- Gillespie, A.R.; Rokugawa, S.; Matsunaga, T.; Cothern, J.S.; Hook, S.; Kahle, A.B. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1113–1126. [Google Scholar] [CrossRef]
- Dash, P.; Göttsche, F.-M.; Olesen, F.-S.; Fischer, H. Land surface temperature and emissivity estimation from passive sensor data: Theory and practice—Current trends. Int. J. Remote Sens. 2002, 23, 2563–2594. [Google Scholar] [CrossRef]
- Coll, C.; Valor, E.; Galve, J.M.; Mira, M.; Bisquert, M.; García-Santos, V.; Caselles, E.; Caselles, V. Long-term accuracy assessment of land surface temperatures derived from the advanced along-track scanning radiometer. Remote Sens. Environ. 2012, 116, 211–225. [Google Scholar] [CrossRef]
- Freitas, S.C.; Trigo, I.F.; Bioucas-Dias, J.M.; Goettsche, F.-M. Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat. IEEE Trans. Geosci. Remote Sens. 2010, 48, 523–534. [Google Scholar] [CrossRef]
- Niclòs, R.; Galve, J.M.; Valiente, J.A.; Estrela, M.J.; Coll, C. Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sens. Environ. 2011, 2011, 2126–2140. [Google Scholar] [CrossRef]
- Hulley, G.C.; Hook, S.J. Intercomparison of versions 4, 4.1 and 5 of the MODIS land surface temperature and emissivity products and validation with laboratory measurements of sand samples from the Namib Desert, Namibia. Remote Sens. Environ. 2009, 133, 1313–1318. [Google Scholar] [CrossRef]
- Guillevic, P.C.; Biard, C.J.; Hulley, G.C.; Privette, J.L.; Hook, S.J.; Olioso, A.; Göttsche, F.M.; Radocinski, R.; Román, M.O.; Yu, Y.; Csiszar, I. Validation of land surface temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sens. Environ. 2014, 154, 19–37. [Google Scholar] [CrossRef]
- Justice, C.O.; Román, M.O.; Csiszar, I.; Vermote, E.F.; Wolfe, R.; Hook, S.J.; Friedl, M.; Wang, Z.; Schaaf, C.; Miura, T.; et al. Land and cryosphere products from Suomi NPP VIIRS: Overview and status. J. Geophys. Res. 2013, 118, 9753–9765. [Google Scholar] [CrossRef] [PubMed]
- CLASS. Available online: http://www.nsof.class.noaa.gov (accessed on 14 September 2015).
- Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 2014, 140, 36–45. [Google Scholar] [CrossRef]
- Prata, A.J. Land Surface Temperature Measurement from Space: AATSR Algorithm Theoretical Basis Document. Available online: https://earth.esa.int/c/document_library/et_file?folderId=13019&name=DLFE-660.pdf (accessed on 15 December 2014).
- Coll, C.; Caselles, V.; Galve, J.M.; Valor, E.; Niclòs, R.; Sánchez, J.M.; Rivas, R. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sens. Environ. 2005, 97, 288–300. [Google Scholar] [CrossRef]
- Augustine, J.A.; DeLuisi, J.J.; Long, C.N. SURFRAD—A national surface radiation budget network for atmospheric research. Bull. Am. Meteorol. Soc. 2000, 81, 2341–2357. [Google Scholar] [CrossRef]
- Augustine, J.A.; Hodges, G.B.; Cornwall, C.R.; Michalsky, J.J.; Medina, C.I. An update on SURFRAD—The GCOS surface radiation budget network for the continental United States. J. Atmos. Ocean. Technol. 2005, 22, 1460–1472. [Google Scholar] [CrossRef]
- Hulley, G.C.; Simon, J.H. The North American ASTER land surface emissivity database (NAALSED), version 2.0. Remote Sens. Environ. 2009, 13, 1967–1975. [Google Scholar] [CrossRef]
- Li, S.; Yu, Y.; Sun, D.; Tarpley, D.; Zhan, X.; Chiu, L. Evaluation of 10 year AQUA/MODIS land surface temperature with SURFRAD observations. Int. J. Remote Sens. 2014, 35, 830–856. [Google Scholar] [CrossRef]
- Liu, Y.; Yu, Y.; Tarpley, D.; Wang, X.; Wang, Z. Initial assessment of Suomi NPP VIIRS Land Surface Temperature (LST) algorithms ballroom G. In Presented at the AMS 93rd Annual Meeting, Austin, TX, USA, 5–10 January 2013.
- Salibury, J.W.; D’Aria, D.M. Emissivity of Terrestrial Materials in the 8–14 µm atmospheric window. Remote Sens. Environ. 1992, 42, 83–106. [Google Scholar] [CrossRef]
- Wang, K.; Liang, S. Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sens. Environ. 2009, 113, 1556–1565. [Google Scholar] [CrossRef]
- Wang, K.; Wan, Z.; Wang, P.; Sparrow, M.; Liu, J.; Zhou, X.; Haginoya, S. Estimation of surface long wave radiation and broadband emissivity using MODIS land surface temperature/emissivity product. J. Geophys. Res. 2005, 110, D11109. [Google Scholar] [CrossRef]
- Seemann, S.W.; Borbas, E.E.; Knuteson, R.O.; Stephenson, G.R.; Huang, H.-L. Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multi-spectral satellite radiance measurements. J. Appl. Meteor. Climatol. 2008, 47, 108–123. [Google Scholar] [CrossRef]
- Göttsche, F.-M.; Olesen, F.-S.; Bork-Unkelbach, A. Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb, Namibia. Int. J. Remote Sens. 2013, 34, 3069–3083. [Google Scholar] [CrossRef]
- Göttsche, F.-M.; Hulley, G.C. Validation of six satellite-retrieved land surface emissivity products over two land cover types in a hyper-arid region. Remote Sens. Environ. 2012, 124, 149–158. [Google Scholar] [CrossRef]
- Kabsch, E.; Olesen, F.; Prata, F. Initial results of the land surface temperature (lST) validation with the Evora, Portugal ground-truth station measurements. Int. J. Remote Sens. 2008, 29, 5329–5345. [Google Scholar] [CrossRef]
- Kondratyev, K.Y. Radiation in the Atmosphere; Academic Press: New York, NY, USA, 1969. [Google Scholar]
- Simultaneous Nadir Overpasses (SNOs) Tool. Available online: http://ncc.nesdis.noaa.gov/SNOPredictions (accessed on 14 September 2015).
- Theocharous, E.; Usadi, E.; Fox, N.P. CEOS Comparison of IR Brightness Temperature Measurements in Support of Satellite Validation. Part I: Laboratory and Ocean Surface Temperature Comparison of Radiation Thermometers; National Physical Laboratory: Teddington, UK, 2010. [Google Scholar]
- Wan, Z.; Zhang, Y.; Zhang, Q.; Li, Z. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sens. Environ. 2002, 83, 163–180. [Google Scholar] [CrossRef]
- Wan, Z.; Zhang, Y.; Zhang, Q.; Li, Z. Quality assessment and validation of the MODIS global land surface temperature. Int. J. Remote Sens. 2004, 25, 261–274. [Google Scholar] [CrossRef]
- Coll, C.; Wan, Z.; Galve, J.M. Temperature-based and radiance-based validations of the V5 MODIS land surface temperature product. J. Geophys. Res. 2009, 114, D20102. [Google Scholar] [CrossRef]
- Hook, S.J.; Vaughan, R.G.; Tonooka, H.; Schladow, S.G. Absolute radiometric in-flight validation of mid infrared and thermal infrared data from ASTER and MODIS on the Terra spacecraft using the Lake Tahoe, CA/NV, USA, automated validation site. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1798–1807. [Google Scholar] [CrossRef]
- Guillevic, P.; Privette, J.; Coudert, B.; Palecki, M.A.; Demarty, J.; Ottlé, C.; Augustine, J.A. Land Surface Temperature product validation using NOAA’s surface climate observation networks-scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS). Remote Sens. Environ. 2012, 124, 282–298. [Google Scholar] [CrossRef]
- Hale, R.C.; Gallo, K.P.; Tarpley, D.; Yu, Y. Characterization of in situ locations for calibration/validation of satellite-derived land surface temperature data. Remote Sens. Lett. 2011, 2, 41–50. [Google Scholar] [CrossRef]
- Ermida, S.L.; Trigo, I.F.; DaCamara, C.C.; Göttsche, F.M.; Olesen, F.S.; Hulley, G. Validation of remotely sensed surface temperature over an oakwood landscape-The problem of viewing and illumination geometries. Remote Sens. Environ. 2014, 148, 16–27. [Google Scholar] [CrossRef]
- Sun, D.; Yu, Y.; Fang, L.; Liu, Y. Toward an operational land surface temperature algorithm for GOES. J. Appl. Meteor. Clim. 2013, 52, 1974–1986. [Google Scholar] [CrossRef]
- Wan, Z.; Li, Z.-L. Radiance-based validation of the V5 MODIS land-surface temperature product. Int. J. Remote Sens. 2008, 29, 5373–5395. [Google Scholar] [CrossRef]
- Jacob, F.; Petitcolin, F.; Schmugge, T.; Vermote, E.; French, A.; Ogawa, K. Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors. Remote Sens. Environ. 2004, 90, 137–152. [Google Scholar] [CrossRef]
- Trigo, I.F.; Monteiro, I.T.; Olesen, F.; Kabsch, E. An assessment of remotely sensed land surface temperature. J. Geophys. Res. 2008, 113, D17108. [Google Scholar] [CrossRef]
- Wan, Z.; Li, Z. A physics-based algorithm for land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens. 1997, 35, 980–996. [Google Scholar]
- Soliman, A.; Duguay, C.; Saunders, W.; Hachem, S.P.-A. Land surface temperature from MODIS and AATSR: Product development and intercomparison. Remote Sens. 2012, 4, 3833–3856. [Google Scholar] [CrossRef]
- VIIRS Surface Type EDR ATBD. Available online: http://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-024_JPSS_ATBD_VIIRS-Surface-Type_A.pdf (accessed on 15 September 2015).
- Yu, Y.; Privette, J.L.; Pinheiro, A.C. Analysis of the NPOESS VIIRS land surface temperature algorithm using MODIS data. IEEE Trans. Geosci. Remote Sens. 2005, 43, 2340–2350. [Google Scholar]
- Hutchison, K.D.; Iisager, B.D.; Mahoney, R.L. Enhanced snow and ice identification with the VIIRS cloud mask algorithm. Remote Sens. Lett. 2013, 4, 929–936. [Google Scholar] [CrossRef]
- Kopp, T.; Heidinger, A.; Thomas, W. VIIRS Cloud Mask (VCM) Provisional Status. Available online: http://www.star.nesdis.noaa.gov/jpss/documents/meetings/2013/EDRProvReview/VCM_provisional_brief_Jan2013.pdf (accessed on 10 June 2015).
- Kopp, T.J.; Thomas, W.; Heidinger, A.K.; Botambekov, D.; Frey, R.A.; Hutchison, K.D.; Iisager, B.D.; Brueske, K.; Reed, B. The VIIRS Cloud Mask: Progress in the first year of S-NPP toward a common cloud detection scheme. J. Geophys. Res. Atmos. 2014, 119, 2441–2456. [Google Scholar] [CrossRef]
- VIIRS LST ATBD. Available online: http://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-022_JPSS_ATBD_VIIRS-LST_A.pdf (accessed on 15 September 2015).
- Li, Z.; Tang, B.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef]
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, Y.; Yu, Y.; Yu, P.; Göttsche, F.M.; Trigo, I.F. Quality Assessment of S-NPP VIIRS Land Surface Temperature Product. Remote Sens. 2015, 7, 12215-12241. https://doi.org/10.3390/rs70912215
Liu Y, Yu Y, Yu P, Göttsche FM, Trigo IF. Quality Assessment of S-NPP VIIRS Land Surface Temperature Product. Remote Sensing. 2015; 7(9):12215-12241. https://doi.org/10.3390/rs70912215
Chicago/Turabian StyleLiu, Yuling, Yunyue Yu, Peng Yu, Frank M. Göttsche, and Isabel F. Trigo. 2015. "Quality Assessment of S-NPP VIIRS Land Surface Temperature Product" Remote Sensing 7, no. 9: 12215-12241. https://doi.org/10.3390/rs70912215