4.1. Comparison with SURFRAD Data
Figure 2 shows overall comparisons of the VIIRS LSTs (a) and the MODIS LSTs (b) against the SURFRAD LSTs. The number of VIIRS matchups is twice that of MODIS matchups: On the one hand, this is due to a better coverage of VIIRS compared to MODIS; on the other hand, it is a result of different cloud flag definition. In the MODIS LST product, the cloud free pixels affected by nearby clouds are excluded, which is not the case for the VIIRS LST product. It presents that accuracy and precision of the VIIRS LSTs are −0.41 K and 2.35 K, respectively, which is better than that of the MODIS LSTs (
i.e., −1.36 K and 2.50 K, respectively). Note that a better accuracy/precision of the VIIRS LSTs are at nighttime (−0.24 K/1.97 K) compared to that at daytime (−0.71 K/2.86 K). The better nighttime performance is expected because the thermal heterogeneity is usually higher during daytime and the atmospheric water vapor is less and the land surface behaves almost homogeneously at night [
7]. This result is consistent with the results of other studies [
21,
33,
47,
56].
Note also that overall the VIIRS LSTs are 0.95 K warmer than the MODIS LSTs.
A similar comparison of the VIIRS LSTs and the AATSR LSTs against the SURFRAD LSTs is shown in
Figure 3. In which, bias and of the VIIRS LSTs are −0.78 K and 2.34 K, respectively, which is comparable to that of the AATSR LSTs (
i.e., −0.20 K and 2.42 K, respectively). Note that VIIRS LST is on average 0.5 K colder than AATSR LST.
Note that circled matchups of the VIIRS LSTs in
Figure 2 and
Figure 3 are significantly lower than the ground measurements. These are suspicious cloud contaminated data since temperature of the cloud top is mostly lower than the land surface during the time. Although the VIIRS QF from the cloud mask product and additional cloud filtering have been utilized, it appears insufficient for the validation purpose. It is found that all matchups in the circle are with snow/ice cover, which suggests a degradation of the cloud detection over bright surfaces. Four types of misclassification have been found for snow/ice identification with the cloud mask including the multi-layered cloud misclassified as snow [
57]. Cloud leakage has been reported by EDR groups and snow/ice/cloud differentiation has been listed as the major issue for further improvement [
58,
59]. Besides, the snow/ice EDR only provides temporal snow for daytime, which leads to the incorrect surface type used in the VIIRS LST retrieval at night. Therefore, the VIIRS nighttime LST is likely degraded by misuse of the surface cover information. In order to solve this problem, the nighttime snow/ice detection was introduced to the operational product on 22 May 2014. However, it cannot help the analysis of past data prior to that date.
Figure 2.
Scatter plots of the VIIRS LSTs (a) and MODIS LSTs (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).
Figure 2.
Scatter plots of the VIIRS LSTs (a) and MODIS LSTs (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).
Figure 3.
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.
Figure 3.
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.
Difference between the VIIRS LSTs and SURFRAD LSTs also demonstrates a strong seasonal variation. As shown in
Table 3, the best agreement occurs in fall with a bias of −0.23 K and STD as of 1.82 K and the worst agreement in spring with a bias of −0.57 K and STD as of 2.56 K; the seasonal pattern is more significant at daytime than at nighttime. The seasonal variation is also reported in MODIS LST validation [
30]. Two most relevant error sources in LST retrieval are atmospheric water vapor absorption and the surface emissivity uncertainty. Significant decrease of the atmospheric transmittance at 11 and 12 µm with increase of the water vapor introduces significant error in the split window algorithm when the surface temperature is high [
60], which is the fundamental reason of the worsen performance in spring and summer compared to that in fall and winter. Besides, the large discrepancies in spring are also attributed to the cloud contamination over snow/ice surface (those matchups in the red circle of
Figure 2 happened in spring) and considerably warm LST retrievals about 6–10 K greater than ground observations over Bondville station in late spring to early summer. As shown in blue circle of
Figure 2, the feature is found in both VIIRS LST and MODIS LST validation results against SURFRAD observations. In their MODIS LST validation study, Li
et al. [
30] compared 10 years 16-day average NDVI and daily emissivity datasets from the MODIS observations and found that this feature might be caused by anomalous NDVI-emissivity relationship,
i.e., emissivity does not change accordingly with the NDVI change during the time period. Guillevic
et al. [
21] mentioned that validation results obtained for stations surrounded by croplands present strong seasonal dependency: station observations may be closer/deviate more from the temperature of surrounding fields, according to crop maturity. As such, VIIRS LST tends to be much lower than that of Bondville station when plants in surrounding fields (corn and soybeans) are well developed in summer and significantly higher after the harvest.
Table 3.
Seasonal variation from the validation using SURFRAD measurements.
Table 3.
Seasonal variation from the validation using SURFRAD measurements.
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 |
In our dataset, however, we mainly observed higher LST at early growth stage from May to June but not after the harvest; and this feature is not obvious from other sites with cropland cover such as over the SXF site. Therefore, other impacts should be investigated. Some matchups with higher LST estimation are listed in
Table 4. It mainly happens at local time 1–3 p.m. When crops are short, they will have little shade on ground. From the angles shown in columns of STZ (Satellite Zenith Angle), STAZ (Satellite Azimuth Angle), SOZ (Solar Zenith Angle) and SOAZ (Solar Azimuth Angle), the sun illuminates crops with a certain angle from 17 deg to 36 deg centering at about 20 deg, and the satellite views the soil surface and vegetation stem rather than the canopy.
Radiometer of the station, however, is always looking down at nadir measuring the surface upwelling radiance from the vegetation canopy. Such observation difference might be another reason leading to lower ground observations than satellite LST estimation.
Table 4.
Details about the match ups over Bondville site with higher satellite LST retrievals than ground measurements.
Table 4.
Details about the match ups over Bondville site with higher satellite LST retrievals than ground measurements.
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 |
Table 5.
Discrepancies between VIIRS LST and Ground LST over site and associated surface types.
Table 5.
Discrepancies between VIIRS LST and Ground LST over site and associated surface types.
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 |
To characterize the spatial representativeness of the ground site LST, ASTER LST product is used by aggregating 90 m ASTER pixels to form 1 km pixels centered at each station [
7,
21]. In this study, google earth image is used for visual check of the surface heterogeneity. The SURFRAD sites DRA and FPK appear more homogeneous than other sites. The quality of validation results over relatively heterogeneous sites depend on the satellite footprint, geolocation accuracy, surface type accuracy as well as the emissivity settings of ground LST calculation. The discrepancies between VIIRS LST and ground measurements are analyzed over each site and associated surface types (
Table 5). All sites except DRA and GWN present seasonal snow cover. The validation results are strongly impacted by cloud contamination in FPK and SXF sites for snow cover. The analysis result suggests land cover discrepancies between sites and satellite footprints. For example, FPK site and surroundings are located within grassland areas; however 90 out of 637 matchup pixels are classified as crop/vegetation mosaic, which results in a relatively large error. Similarly PSU site is with cropland cover on site; however, 17 matchups pixels are classified as deciduous broadleaf forests, which also causes a significant error. For the DRA site, although there were 149 matchups misclassified as barren surface, a good agreement between the satellite observations and ground measurements is obtained. This is possibly because that the emissivity setting for barren surface (emissivity pair of 0.965 and 0.97 for VIIRS band 15 and 16, respectively) is close to the bushy surface of the site. Furthermore, there would also be considerable bush shading at the site so that no obvious underestimation observed. It is also noted that the remaining 97 matchups are classified as closed shrubland at DRA site. According to the IGBP surface type definitions, shrub canopy cover is greater than 60% and 10%–60% for closed shrubland type and open shrubland type, respectively. Therefore surface type over the DRA site might change depending on the green and dry season.
4.2. Comparison with Data from Gobabeb, Namibia
The same QC control procedure as for the SURFRAD sites is implemented and the validation results are shown in
Figure 4. Gobabeb
in situ LST are also used to validate MODIS Aqua LST (collection 5), which are used as a reference.
Figure 4.
Validation result against the data in Gobabeb, Namibia in 2012: VIIRS LST (a) and MODIS LST V5 (b).
Figure 4.
Validation result against the data in Gobabeb, Namibia in 2012: VIIRS LST (a) and MODIS LST V5 (b).
The results show that the VIIRS and MODIS algorithms underestimate
in situ LST with a bias of 1.57 K and 2.97 K, respectively, whereas they achieve similar precisions of 2.06 K for VIIRS and 1.92 K for MODIS. As shown in
Figure 1, the location used for the comparison is about 13 km east of the validation station, where the gravel plain is highly homogeneous over large areas [
37]. Using additional
in situ measurements across the gravel plains, Göttsche
et al. [
36] demonstrated that the surface conditions at Gobabeb station are highly representative of the gravel plains and that there is an excellent match between the operational SEVIRI LST retrieved by EUMETSAT’s Land Surface Analysis-Satellite Application Facility (LSA-SAF) and Gobabeb station LST [
18], with typical monthly biases of less than 1.0 K and rms errors of about 1.0 K to 1.5 K.
Wan [
54] clearly described the underestimation of MODIS v5 LST over bare soil sites. Three possible sources are considered for the large LST error: (1) The original split window algorithm does not well cover the wide range of LSTs; (2) the large errors in surface emissivity values in bands 31 and 32 estimated from land-cover types; (3) effect of dust aerosols that has not been considered in the R-based validation. Emissivity adjustment model for bare soil pixel as well as a new set of split window algorithm coefficients is incorporated into the day/night algorithm, resulting C6 level 2 LST products. Above reasons (1) and (2) are also applicable for the large error in VIIRS LST. Reason 3 is not investigated in this study.
Besides, the misclassification of surface type over Gobabeb site is observed, with 4 matchup pixels being classified as evergreen broadleaf forest. These lead to large LST errors and were therefore removed from the validation results. Again this demonstrates the impact of surface type misclassification on LST estimates. It is necessary to understand the LST uncertainty associated with the surface type input.
Section 5 presents this analysis in detail.
4.3. Cross Comparison with MODIS Aqua LST
The cross comparison of the VIIRS and MODIS LST product is conducted at granule level using the SNO service. As described in the quality control section, the temporal difference is restricted to 10 min. Over 100 scenes are chosen covering each month of one year in continental US, low latitude and polar area representing low, middle to high latitudes climate. The overall comparison results as shown in
Figure 5, indicate that MODIS LST and VIIRS LST produce a consistent measurement with a bias of 0.77 K and STD of 1.97 K (VIIRS minus MODIS).
Considering that the cloud residue and surface cover difference within two satellite footprints have strong impact on the cross comparison, the spatial variation test is applied to both MODIS and VIIRS LSTs, which results in the exclusion of two third of match-up pixels as shown in
Figure 5b. The viewing angle difference screening is further applied based on
Figure 5b. Therefore
Figure 5c, representing the cross comparison results with cloud and VZA screening, shows a bias of 0.7 K and STD of 1.13 K. In order to check if the discrepancy is due to the LST retrieval algorithm, a proxy like method is used for VIIRS LST calculation,
i.e., using MODIS sensor data records (BT and geometry information) as input for VIIRS LST retrieval. This way the impact of difference in the sensor data records can be excluded. The result shows a bias of 0. 5K and STD of 0.7 K (
Figure 5d).
We also apply the above proxy like procedure to generate multiple daily global data, which leads to similar results. For example, a bias of 0.13 K and STD of 0.72 K is obtained for the global proxy comparison on 22 April 2014; a bias of 0.5 K and STD of 0.55 K is obtained on 19 December 2014. This exercise demonstrates that the algorithm difference is rather small in terms of uncertainty; more significant uncertainties are due to the sensor characterization, thermal heterogeneity of the land surface, temporal difference, and the angular anisotropy of land surface emissivity and temperature. A positive bias of the order of 0.5 K is found between VIIRS and MODIS LST. The comparison of VIIRS and MODIS LST over surface types is summarized in
Table 6. It is noted that the comparison result does not cover all surface types, e.g., snow/ice in nighttime and closed shrubland in daytime, and the number of match-ups in each surface type varies significantly from 2 to 552,550. The surface types with less than 500 samples are excluded from the following discussion due to lack of statistical representativeness. In the daytime, the LST difference ranges from 0.2 K to 3.3 K for absolute bias and from 0.57 K to 1.49 K for STD; in the nighttime, the LST difference ranges from 0.06 K to 2.11 K for absolute bias and from 0.93 K to 1.53 K for STD. Large discrepancies are found over open shrubland, savannahs and barren soil, for which the difference is up to 3.3 K. The comparison results are restrained by availability of SNOs and accuracy of the VIIRS LST surface type information.
Figure 5.
Cross-comparison results between VIIRS and AQUA for the whole period and area under analysis. (a) all comparison results under cloud clear condition ; (b) based on a, spatial variation tests are added ; (c) based on b, angle difference is added ; (d) based on c, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST.
Figure 5.
Cross-comparison results between VIIRS and AQUA for the whole period and area under analysis. (a) all comparison results under cloud clear condition ; (b) based on a, spatial variation tests are added ; (c) based on b, angle difference is added ; (d) based on c, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST.
However, we also observed some very large discrepancies in the cross satellite comparison of VIIRS and MODIS LST products. For example, difference over 10 K was observed on 28 December 2013 over Australia, where VIIRS overpasses at UTC 04:17 and MODIS Aqua overpasses at UTC 04:45. Therefore, the temporal separation is about 30 min. The corresponding granules are used as a case study to investigate the problem.
Table 6.
Cross comparison of VIIRS LST and MODIS LST over surface type. This result is corresponding to
Figure 5c.
i.e., the data has been filtered to include only LST with the angle difference within 10 degrees and possible cloud contamination excluded. The overpass includes areas in low latitude, high latitude and US.
Table 6.
Cross comparison of VIIRS LST and MODIS LST over surface type. This result is corresponding to Figure 5c. i.e., the data has been filtered to include only LST with the angle difference within 10 degrees and possible cloud contamination excluded. The overpass includes areas in low latitude, high latitude and US.
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 |
The overall granule comparison of VIIRS and MODIS LST (
Figure 6a) shows that VIIRS LST is statistically 2 K warmer than MODIS LST and the maximum difference is over 10 K. To examine whether these highest discrepancies are caused by the sensor input, we examine the brightness temperature at 11 µm and the BT difference of the two split windows; the results are displayed in
Figure 6b,c, respectively.
It is found that the VIIRS BT
15 is on average 1 K colder than MODIS BT
31 (
Figure 6b) but the VIIRS BT difference between two split windows is statistically 1 K higher than that in MODIS (
Figure 6c), which is considered as the main cause for the large LST discrepancy as the impact of BT difference on VIIRS LST is not linear but quadratic growth. For verification, we calculate VIIRS LST using the same proxy method in generating
Figure 5d and then compare with MODIS LST. The LST discrepancy becomes much smaller as shown in (
Figure 6d) with a bias of 0.01 K. Therefore, it once again suggests that the algorithm difference is not the main cause for the large discrepancies. However, VIIRS LST might overcorrect the atmospheric absorption under very high BT difference condition, which results in the LST degradation for the particular case.
Figure 6.
Cross-comparison results between VIIRS and AQUA of the case study on 28 December 2013. (a) Overall comparison results under cloud clear condition; (b) Brightness temperature comparison of VIIRS band 15 and MODIS Aqua band; (c) the BT difference comparison between VIIRS (BT15-BT16) and MODIS (BT31-BT32); (d) 31 based on a, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST
Figure 6.
Cross-comparison results between VIIRS and AQUA of the case study on 28 December 2013. (a) Overall comparison results under cloud clear condition; (b) Brightness temperature comparison of VIIRS band 15 and MODIS Aqua band; (c) the BT difference comparison between VIIRS (BT15-BT16) and MODIS (BT31-BT32); (d) 31 based on a, VIIRS LST is calculated using MODIS data as input and then compare to MODIS LST