A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

Galve, Joan M.; Sánchez, Juan Manuel; Coll, César; Villodre, Julio

doi:10.3390/rs10060826

Cited by 13 publications

(13 citation statements)

References 50 publications

(55 reference statements)

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“…We believe that such a variability is responsible for the variable difference between the temperature recorded at the probes and that calculated from satellite data. A pixel-by-pixel atmospheric correction method may reduce such effect [62]; however, high spatial resolution ancillary data are needed in order to correctly calculate (or simulate) the spatial variability of the atmospheric parameters. Such ancillary data may be retrieved from other sources, as suggested in the method proposed by Galve et al [62] that uses the National Centers of Environmental Prediction (NCEP) profiles.…”

Section: Discussionmentioning

confidence: 99%

“…A pixel-by-pixel atmospheric correction method may reduce such effect [62]; however, high spatial resolution ancillary data are needed in order to correctly calculate (or simulate) the spatial variability of the atmospheric parameters. Such ancillary data may be retrieved from other sources, as suggested in the method proposed by Galve et al [62] that uses the National Centers of Environmental Prediction (NCEP) profiles. Such an approach may improve the results obtained with our study; however, we speculate that, in our case, the efficacy of the method is limited by the very low spatial resolution of the NCEP profiles that are provided on a 1 • × 1 • longitude/latitude grid every 6 h. Based on all these considerations, we believe that the post-correction of the temperatures retrieved from satellite using the measurements performed by the ten probes spread across the Venice lagoon is the most accurate method for our case study.…”

Section: Discussionmentioning

confidence: 99%

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Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

et al. 2019

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Given the increasing anthropogenic pressures on lagoons, estuaries, and lakes and considering the highly dynamic behavior of these systems, methods for the continuous and spatially distributed retrieval of water quality are becoming vital for their correct monitoring and management. Water temperature is certainly one of the most important drivers that influence the overall state of coastal systems. Traditionally, lake, estuarine, and lagoon temperatures are observed through point measurements carried out during field campaigns or through a network of sensors. However, sporadic measuring campaigns or probe networks rarely attain a density sufficient for process understanding, model development/validation, or integrated assessment. Here, we develop and apply an integrated approach for water temperature monitoring in a shallow lagoon which incorporates satellite and in-situ data into a mathematical model. Specifically, we use remote sensing information to constrain large-scale patterns of water temperature and high-frequency in situ observations to provide proper time constraints. A coupled hydrodynamic circulation-heat transport model is then used to propagate the state of the system forward in time between subsequent remote sensing observations. Exploiting the satellite data high spatial resolution and the in situ measurements high temporal resolution, the model may act a physical interpolator filling the gap intrinsically characterizing the two monitoring techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

et al. 2019

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“…The radiance value derived from the thermal infrared channel of the Landsat sensor is composed of three parts: (1) upwelling atmospheric radiance, (2) downwelling atmospheric radiance, and (3) atmospheric transmissivity between the actual land surface and the Landsat sensor. The apparent radiance value measured by Landsat (L at−sensor, λ ), namely the RTE, can be described in Equation 1 [55][56][57][58][59]:…”

Section: Land Surface Temperature (Lst) Retrievalmentioning

confidence: 99%

Impacts of Land Cover/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China

et al. 2020

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Satellite-derived land surface temperature (LST) reveals the variations and impacts on the terrestrial thermal environment on a broad spatial scale. The drastic growth of urbanization-induced impervious surfaces and the urban population has generated a remarkably increasing influence on the urban thermal environment in China. This research was aimed to investigate land surface temperature (LST) intensity response to urban land cover/use by examining the thermal impact on urban settings in ten Chinese megacities (i.e., Beijing, Dongguan, Guangzhou, Hangzhou, Harbin, Nanjing, Shenyang, Suzhou, Tianjin, and Wuhan). Surface urban heat island (SUHI) footprints were scrutinized and compared by magnitude and extent. The causal mechanism among land cover composition (LCC), population, and SUHI was also identified. Spatial patterns of the thermal environments were identical to those of land cover/use. In addition, most impervious surface materials (greater than 81%) were labeled as heat sources, on the other hand, water and vegetation were functioned as heat sinks. More than 85% of heat budgets in Beijing and Guangzhou were generated from impervious surfaces. SUHI for all megacities showed spatially gradient decays between urban and surrounding rural areas; further, temperature peaks are not always dominant in the urban core, despite extremely dense impervious surfaces. The composition ratio of land cover (LCC%) negatively correlates with SUHI intensity (SUHII), whereas the population positively associates with SUHII. For all targeted megacities, land cover composition and population account for more than 63.9% of SUHI formation using geographically weighted regression. The findings can help optimize land cover/use to relieve pressure from rapid urbanization, maintain urban ecological balance, and meet the demands of sustainable urban growth.

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“…Coll et al [61] have validated this web-based tool against ground measurements for Landsat 7 and reported that the atmospheric correction from this tool is comparable with correction from local radiosonde profiles. The tool was also used to validate a newly proposed pixel-by-pixel atmospheric correction method called SBAC for Landsat 7 in Reference [62]. The online atmospheric correction tool requires the user to input some mandatory data including location (longitude and latitude), date, and time for which the atmospheric parameters are to calculate.…”

Section: Radiative Transfer Equation and Atmospheric Parameters To Rementioning

confidence: 99%

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Sajib

Tao

2020

Sensors

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The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

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A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+

Cited by 13 publications

References 50 publications

Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

Impacts of Land Cover/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

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