New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

Wan, Zhengming

doi:10.1016/j.rse.2006.06.026

Cited by 1,063 publications

(747 citation statements)

References 16 publications

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“…The coefficients used in the split window algorithm are given by interpolating a set of multi-dimensional lookup tables (LUT) derived by linear regression of MODIS simulation data from radiative transfer calculations over wide ranges of surface and atmospheric conditions. These look up tables have been continuously upgraded (Wan et al, 2004) and comparisons between MODIS LST's and in-situ measurements across a wide set of test sites indicate the accuracy is better than 1 K with an RMS (of differences) less than 0.5 K in most cases (Wan, 2008;Wang et al, 2008).…”

Section: Land Surface Temperature (Lst) and Ndvimentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of the urban heat island effect across biomes in the continental USA

Imhoff

Zhang

Wolfe

et al. 2010

Remote Sensing of Environment

1,218

686

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Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003)(2004)(2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the nonurban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8°C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9°C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3°C temperature difference in summer and only 1.3°C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.Published by Elsevier Inc.

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Section: Land Surface Temperature (Lst) and Ndvimentioning

confidence: 99%

“…2d). Even though the differences in the emissivities are small this further reduces the possibility of LUT induced signal in the comparison of LST's between ISA zones (Wan, 2008).…”

Section: Quantifying the Relationship Between Lst And Isamentioning

confidence: 99%

Remote sensing of the urban heat island effect across biomes in the continental USA

Imhoff

Zhang

Wolfe

et al. 2010

Remote Sensing of Environment

1,218

686

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“…1b) for which a homogeneous climate was assumed. Erroneous MODIS LST measurements due to incorrect cloud masking are a known issue (Wan, 2008). MODIS LSTs colder than 10°C from the regional mean in summer were assumed to be outliers due to the admixing of cloud top temperatures.…”

Section: Calculating Modis Lst Spatial Anomaliesmentioning

confidence: 99%

“…However, considering the low MDs during snow melt periods spatial differences in LST due to snow were also reduced for the averaging period. Ideally, Wan (2008) recommends taking in situ measurements over large homogeneous sites with dimensions of at least 5 by 5 km, using high-accuracy radiometer measurements at multiple points. Such requirements are, however, extremely difficult to fulfill at Arctic sites, where logistical constraints limit the number of field stations.…”

Section: Uncertainties and Errors Of Modis Lst Evaluationmentioning

confidence: 99%

Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes

Muster

Langer

Abnizova

et al. 2015

Remote Sensing of Environment

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The accelerated warming of the Arctic climate may alter the local and regional surface energy balances, for which changing land surface temperatures (LSTs) are a key indicator. Modeling current and anticipated changes in the surface energy balance requires an understanding of the spatio-temporal interactions between LSTs and land cover, both of which can be monitored globally by measurements from space. This paper investigates the accuracy of the MODIS LST/Emissivity Daily L3 Global 1 km V005 product and its spatio-temporal sensitivity to land surface properties in a Canadian High Arctic permafrost landscape. The land cover ranged from fully vegetated wet sedge tundra to barren rock. MODIS LSTs were compared with in situ radiometer measurements from wet tundra areas collected over a 2-year period from July 2008 to July 2010 including both summer and winter conditions. The accuracy of the MODIS LSTs was −1.1°C with a root mean square error of 3.9°C over the entire observation period. Agreement was lowest during the freeze-back periods where MODIS LST showed a cold bias likely due to the overrepresentation of clear-sky conditions. A multi-year analysis of LST spatial anomalies, i.e., the difference between MODIS LSTs and the MODIS LST regional mean, revealed a robust spatiotemporal pattern. Highest variability in LST anomalies was found during freeze-up and thaw periods as well as for open water surface in early summer due to the presence or absence of snow or ice. The summer anomaly pattern was similar for all three years despite strong differences in precipitation, air temperature and net radiation. Summer periods with regional mean LSTs above 5.0°C showed the greatest spatial diversity with four distinct 2.0°C classes. Summer anomalies ranged from −4.5°C to 2.6°C with an average standard deviation of 1.8°C. Dry ridge areas heated up the most, while wetland areas and dry areas of sparsely vegetated bedrock with a high albedo remained coolest. The observed summer LST anomalies can be used as a baseline against which to evaluate both past and future changes in land surface properties that relate to the surface energy balance. Summer anomaly classes mainly reflected a combination of albedo and surface wetness. The potential to use this tool to monitor surface drying and wetting in the Arctic should therefore be further explored. A multi-sensor approach combining thermal satellite measurements with optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme.

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“…Validation of MODIS land surface temperature products has emphasised the night-time product [11,12], which is easier to validate. The effects of relative humidity, wind speed, soil moisture, air temperature and sensor view zenith angle on night time land surface temperatures have been investigated, of which only sensor view zenith angle showed a weak influence on LST error propagation [11].…”

Section: Introductionmentioning

confidence: 99%

Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

et al. 2014

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Spatially continuous satellite infrared temperature measurements are essential for understanding the consequences and drivers of change, at local and regional scales, especially in northern and alpine environments dominated by a complex cryosphere where in situ observations are scarce. We describe two methods for producing daily temperature fields using MODIS -clear-sky‖ day-time Land Surface Temperatures (LST). The Interpolated Curve Mean Daily Surface Temperature (ICM) method, interpolates single daytime Terra LST values to daily means using the coincident diurnal air temperature curves. The second method calculates daily mean LST from daily maximum and minimum LST (MMM) values from MODIS Aqua and Terra. These ICM and MMM models were compared to daily mean air temperatures recorded between April and October at seven locations in southwest Yukon, Canada, covering characteristic alpine land cover types (tundra, barren, glacier) 947 8-day LST averages, aggregated from the MMM method, to air temperature, we found a high correlation (R 2 = 0.84) with less variability (RMSE = 1.54 K). Where the trend was less steep and the y-intercept increased by 1.6 °C compared to the daily correlations. This effect is likely a consequence of LST temperature averages being differentially affected by cloud cover over warm and cold surfaces. We conclude that satellite infrared skin temperature (e.g., MODIS LST), which is often aggregated into multi-day composites to mitigate data reductions caused by cloud cover, changes in its relationship to air temperature depending on the period of aggregation.

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New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

Cited by 1,063 publications

References 16 publications

Remote sensing of the urban heat island effect across biomes in the continental USA

Remote sensing of the urban heat island effect across biomes in the continental USA

Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes

Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

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