Characterization of maximum land surface temperatures in 16 years from MODIS in Iran

Khandan, Reza; Gholamnia, Mehdi; Duan, Si‐Bo; Ghadimi, Mehrnoosh; Alavipanah, Seyed Kazem

doi:10.1007/s12665-018-7623-z

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…To estimate LST with hourly sequence, a model consisting of a harmonic and an exponential term is fitted to the Earth's DTC model, which describe the effect of the sun and the reduction of LST at night, respectively [66]- [68]. Modeling estimates the parameters that describe DTC and can also be useful for interpolating missing data due to technical or cloud problems [69], [70].…”

Section: Diurnal Temperature Cycle Modelsmentioning

confidence: 99%

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

Aliabad,

Ghaderpour,

Zare

et al. 2024

IEEE Access

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Thermal monitoring of different regions is usually limited to meteorological data in ground stations. Meteorological networks are limited in arid and semi-arid areas, where monitoring climatic conditions is not possible. The aim of this study is to estimate the land surface temperature (LST) hourly for Yazd-Ardakan plain by modeling the diurnal temperature cycle (DTC) using LST imagery of moderate resolution imaging spectroradiometer (MODIS). First, MODIS imagery are reconstructed using the multichannel singular spectrum analysis, and the complete time series without missing values are created. Then, six DTC models are compared. The accuracy of DTC models is examined by ground LST measurements, air temperature, humidity, and wind speed. In addition, the results of examining the root mean square error (RMSE) images obtained from cross-validation based on MODIS LST imagery show that DTC2 has the highest error, where 73% of the area has RMSE greater than 3 • C. In DTC1 and DTC2, 64% and 5.8% of the study region has RMSE less than 2 • C. In general, DTC1, DTC6, DTC5, DTC4, DTC3, and DTC2 models have shown the highest to lowest accuracy in modeling the LST diurnal cycle. In addition, the difference between LST in mountain and plain lands is greater at the time of maximum temperature than at other hours of the day and night. The findings of this research are crucial in studies concerning climate change and land environmental monitoring in arid/semi-arid regions.

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Section: Diurnal Temperature Cycle Modelsmentioning

confidence: 99%

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

Aliabad,

Ghaderpour,

Zare

et al. 2024

IEEE Access

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“…As daytime LST is closely related to the land cover and NDVI (Parida et al, 2008;Mildrexler et al, 2011;Khandan et al, 2018), the seasonal cycle of domain-mean daytime LST for different land cover types are illustrated in Figure 3.…”

Section: Mean Characteristics Of Lst Across Indiamentioning

confidence: 99%

“…Elevation and normalized difference vegetation index (NDVI) have shown to be closely related with the maximum LST (Khandan et al, 2018). Nonetheless, the changes in LST are also controlled by the vegetation types (Parida et al, 2008;Mildrexler et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Land surface temperature variability across India: a remote sensing satellite perspective

Prakash

Norouzi

2019

Theor Appl Climatol

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Land surface temperature (LST) plays a key role in the surface energy budget computation and land surface process studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua and Terra satellites provide comprehensive global LST estimates at a fine spatial resolution. The MODIS products were recently upgraded to Collection 6, and shown to be more accurate than its predecessor Collection 5 products. In this study, LST and its variability have been examined across India from Collection 6 of the Aqua MODIS data at 0.05°s patial resolution for the period of 2003 to 2017. All-India mean LST characteristics show distinctive features as compared to the well-documented mean characteristics of near-surface air temperature. All land cover types except permanent snow & ice, and cold desert areas exhibit bimodal peaks in seasonal variations of daytime LST. The daytime LST over the coldest and high-altitude regions of northern India shows anomalous positive linear relationship with NDVI at a monthly scale. However, monthly domain-mean daytime LST of cropland regions is largely negatively correlated with NDVI as compared to other land cover types. Results reveal that about 17% of the Indian landmass received its hottest LST during 2010 followed by 2016. Linear trend analysis for the 15-year period of mean annual LST shows a decrease in diurnal temperature range over most parts of the country due to rather rapid increase in nighttime LST than daytime LST, similar as changes in near-surface air temperature across the country.

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A new method to model diurnal air temperature cycle

Gholamnia

Alavipanah

Boloorani

et al. 2018

Theor Appl Climatol

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Characterization of maximum land surface temperatures in 16 years from MODIS in Iran

Cited by 12 publications

References 28 publications

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

Land surface temperature variability across India: a remote sensing satellite perspective

A new method to model diurnal air temperature cycle

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