Radiometric calibration of Landsat Thematic Mapper thermal band

Wukelic, G. E.; Gibbons, D.E.; Martucci, L.M.; Foote, H.P.

doi:10.1016/0034-4257(89)90125-9

Cited by 175 publications

(100 citation statements)

References 7 publications

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“…LST was derived following the method proposed by Bastiaanssen (2000), Bastiaanssen et al (1998a), Coll et al (2010) and Wukelic et al (1989) for computing the surface temperature from the TIR band (band 6) of Landsat (Supplement S1). The TIR band was first converted to thermal radiance (L6, W m −2 sr −1 µm −1 ) and then to atmospherically corrected thermal radiance (R c , W m −2 sr −1 µm −1 ), as described by Wukelic et al (1989) and Coll et al (2010), and with the atmospheric parameters obtained on NASA's online Atmospheric Correction Calculator (Barsi et al, 2003(Barsi et al, , 2005 (Supplement S2).…”

Section: Land Surface Temperaturementioning

confidence: 99%

“…The TIR band was first converted to thermal radiance (L6, W m −2 sr −1 µm −1 ) and then to atmospherically corrected thermal radiance (R c , W m −2 sr −1 µm −1 ), as described by Wukelic et al (1989) and Coll et al (2010), and with the atmospheric parameters obtained on NASA's online Atmospheric Correction Calculator (Barsi et al, 2003(Barsi et al, , 2005 (Supplement S2). The LST (K) was computed with the following equation similar to the Planck equation, as in Coll et al (2010) and Wukelic et al (1989):…”

Section: Land Surface Temperaturementioning

confidence: 99%

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Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

et al. 2017

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Abstract. Indonesia is currently one of the regions with the highest transformation rate of land surface worldwide related to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impacts of land transformation on the LST in Indonesia. We analyze LST from the thermal band of a Landsat image and produce a highresolution surface temperature map (30 m) for the lowlands of the Jambi province in Sumatra (Indonesia), a region which suffered large land transformation towards oil palm and other cash crops over the past decades. The comparison of LST, albedo, normalized differenced vegetation index (NDVI) and evapotranspiration (ET) between seven different land cover types (forest, urban areas, clear-cut land, young and mature oil palm plantations, acacia and rubber plantations) shows that forests have lower surface temperatures than the other land cover types, indicating a local warming effect after forest conversion. LST differences were up to 10.1 ± 2.6 • C (mean ± SD) between forest and clear-cut land. The differences in surface temperatures are explained by an evaporative cooling effect, which offsets the albedo warming effect.Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature in the Jambi province increased by 1.05 • C, which followed the trend of observed land cover changes and exceeded the effects of climate warming. This study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase of the air temperature because of climate change.

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Section: Land Surface Temperaturementioning

confidence: 99%

Section: Land Surface Temperaturementioning

confidence: 99%

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

et al. 2017

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“…The thermal band DN was then converted to radiance using the coefficients provided in the imagery metadata. A correction to the radiance was further performed using the method of Wukelic et al [45], and the Plank equation was then applied to compute the land surface temperature (LST). Cloud fraction (CF) mask for each Landsat image, provided by USGS (http://earthexplorer.…”

Section: Landsat 5 Tm and 7 Etm+ Data Preprocessingmentioning

confidence: 99%

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ET o ) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ET r ). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009-2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R 2 ) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R 2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009-2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (R n ), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

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“…The brightness temperatures derived from the Landsat thermal band data could obtain good approximate (within 1-3 • C) ground temperature values on clear days [35,36]. Further calibration was required to estimate emissivity using local soil and vegetation information [37,38], and may obtain greater inversion results after correcting the process using the available approaches.…”

Section: Brightness Temperaturementioning

confidence: 99%

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

et al. 2017

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Abstract:Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

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Radiometric calibration of Landsat Thematic Mapper thermal band

Cited by 175 publications

References 7 publications

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

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