Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data

Özelkan, Emre; Bagis, Serdar; Özelkan, Ertunga C.; Ustundag, Burak Berk; Örmeci, Cankut

doi:10.5721/eujrs20144737

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…The wind effects may differ according to different location and topography. The change of the urban boundary layer wind field reduces the LST of the urban center, especially in coastal cities, and good ventilation can better improve the effects of the urban thermal environment [76]. With long-term and multi-site data, it could be disclosed that there is a negative correlation presented between LST and precipitation [73].…”

Section: Discussionmentioning

confidence: 99%

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Yang

Zhao

et al. 2020

Remote Sensing

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Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

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Section: Discussionmentioning

confidence: 99%

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Yang

Zhao

et al. 2020

Remote Sensing

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“…A high NDVI value indicates healthy vegetation and high humidity and a low NDVI value indicates the opposite condition [11]. Thus the NDVI results can be used as a drought index [7].…”

Section: Methodsmentioning

confidence: 99%

“…Monitoring and detection drought by using NDVI is based on the health of vegetation that is correlated to moisture condition [6]. The hypothesis for a strong SPI-NDVI relationship to agricultural land is that lack of rainfall often causes vegetation to experience stress or even death, with plants showing a decrease in near-infrared reflectance and increased red reflectance [7].…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of drought levels in Bantimurung District, Maros Regency, South Sulawesi Province

2020

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Drought is an event that almost occurs every year in several regions of Indonesia. Drought events are often associated with the El Nino phenomenon, namely the lack of rainfall over a certain period of time in a reasonably long time. In 2015, Indonesia experienced drought in several parts of Indonesia, one of which was Bantimurung District in Maros Regency, South Sulawesi Province. This study aimed to map the level of drought in Bantimurung District. Standardized Precipitation Index (SPI) based meteorological and Normalized Difference Vegetation Index (NDVI) were used to determine the spatial-temporal distribution of drought. Then, the NDVI values of weak (2014) and very strong (2015) El Nino year were correlated with SPI. The results show that Bantimurung District experienced drought in 2015 with near normal (July-September) to moderately dry (October-November) drought levels. In 2014, the drought was not so severe compared to 2015 because the level of drought was near normal and moderately wet. In 2014, the moderately wet area was located in the east district including Leang-leang and Kalabbirang villages. In 2015, villages Minasa Baji, Mattoangin, Alangtae, Baruga, Tukamasea, Mangeloreng, west of Kalabbirang and Leang Leang were located in western of the district have moderately dry drought level.

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“…According to Planck's law, assuming that the content of vapor is relatively small and constant in the atmosphere (the radiation change affected by atmospheric conditions can only be ignored when there exists a homogeneous atmospheric environment), the at-satellite brightness temperature (in Kelvin) reflects the surficial temperature distribution condition on land [6,30]. Some researchers have proved that the at-satellite brightness temperature can be used to approximate the land surface brightness temperature (LSBT) instead [31][32][33], and some important parameters in [34] cannot easily be obtained in this research, so the at-satellite brightness temperature was used as the LSBT in our research.…”

Section: Land Surface Brightness Temperature Derivationmentioning

confidence: 99%

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

2017

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Abstract:The relationship between urban land surface material fractions (ULSMFs) and brightness temperature has long attracted attention in research on urban environments. In this paper, a multiple endmember spectral mixture analysis (MESMA) method was applied to extract vegetation-impervious surface-soil (V-I-S) fractions in each pixel, and the surface brightness temperature was derived by using the radiation in the upper atmosphere, on the basis of Landsat 8 images. Then, a clustering analysis, ternary triangular chart (TTC), and a multivariate statistical analysis were applied to ascertain the relationship between the fractions in each pixel and the land surface brightness temperature (LSBT). The hypsometric TTC, as well as the geographical distribution features of the LSBT, revealed that the changes in LSBT were associated with the high fractions of impervious surfaces (or vegetation), in addition to the temperature distribution differences across locations with varying land-cover types. The data fitting results showed that the comprehensive endmember fractions of V-I-S explained 98.6% of fluctuating LSBT, and the impervious surface fraction had a positive impact on the LSBT, whereas the fraction of vegetation had a negative impact on the LSBT. Keywords: ternary triangular chart (TTC); sub-pixel unmixing; multiple endmember spectral mixture analysis (MESMA); Landsat 8; land surface brightness temperature (LSBT)

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Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data

Cited by 20 publications

References 26 publications

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Spatial distribution of drought levels in Bantimurung District, Maros Regency, South Sulawesi Province

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

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