How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

Zhang, Hongbo; Zhang, Fan; Zhang, Guoqing; Che, Tao; Yan, Wei

doi:10.1002/2017jd028243

Cited by 29 publications

(21 citation statements)

References 80 publications

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“…A comparison of lapse rates in Finnish Lapland for LST versus T air showed very little correspondence between the two, because of the elevational gradient in surface types (Pepin et al, 2018). In the Tibetan Plateau in contrast, lapse rates estimated for daily mean temperature from LST were found to show similar seasonal patterns to those based on T air (Zhang, Zhang, Zhang, Che, et al, 2018). There have been relatively few detailed studies of the difference between T air and LST at extreme high elevations above 5,000 m, but those that have been performed tend to show increased variance in the difference, a lower correlation between T air and LST, and larger mean differences (Pepin et al, 2016).…”

Section: Past Studiesmentioning

confidence: 85%

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

et al. 2019

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Research has revealed systematic changes in warming rates with elevation (EDW) in mountain regions. However, weather stations on the Tibetan plateau are mostly located at lower elevations (3,000‐4,000 m) and are nonexistent above 5,000 m, leaving critical temperature changes unknown. Satellite LST (Land Surface Temperature) can fill this gap but needs calibrating against in situ air temperatures (Tair). We develop a novel statistical model to convert LST to Tair, developed at 87 high‐elevation Chinese Meteorological Administration stations. Tair (daily maximum/minimum temperatures) is compared with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua LST (1330 and 0130 local time) for 8‐day composites during 2002‐2017. Typically, 80‐95% of the difference between LST and Tair (ΔT) is explained using predictors including LST diurnal range, morning heating/nighttime cooling rates, the number of cloud free days/nights, and season (solar angle). LST is corrected to more closely represent Tair by subtracting modeled ΔT. We validate the model using an AWS on Zhadang Glacier (5800 m). Trend analysis at the 87 stations (2002‐2017) shows corrected LST trends to be similar to original Tair trends. To examine regional contrasts in EDW patterns, elevation profiles of corrected LST trends are derived for three ranges (Qilian Mountains, NyenchenTanglha, and Himalaya). There is limited EDW in the Qilian mountains. Maximum warming is observed around 4,500‐5,500 m in NyenchenTanglha, consistent with snowline retreat. In common with other studies, there is stabilization of warming at very high elevations in the Himalaya, including absolute cooling above 6,000 m, but data there are compromised by frequent cloud.

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Section: Past Studiesmentioning

confidence: 85%

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

et al. 2019

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“…Some studies have found that there is a strong relationship between the mean LST and mean near-surface air temperature [13][14][15][16], and the seasonal pattern of monthly mean land-surface TLR is very similar to that of the near-surface air TLR [17]. However, as the LST is influenced by the surface regime [18,19], the relationship between them is not always stable.…”

Section: Introductionmentioning

confidence: 99%

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

et al. 2018

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Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

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“…The relationship between DEM and LST is inverse due to the effect of NSTLR. The LST decreased with the increase of elevation (He et al 2018;Jain et al 2008;Lakshmi et al 2001;Zhang et al 2018). The results for different dates are shown in Table 1 The NSTLR values of the study area varies with time (Table 4).…”

Section: Resultsmentioning

confidence: 95%

“…Therefore, in mountainous and many natural environments, the LST-DEM feature space will be influenced by other parameters. For example, a decrease of LST was reported with the increase in elevation (Jain et al 2008;Verhoest et al 2012;Zhang et al 2018). Similarly, environmental parameters including topographic conditions, solar local incident angle, and surface biophysical properties influence LST based on the LST-DEM feature space.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is not always possible to model TLR using the traditional methods in physically inaccessible areas including many mountainous regions. Regression relationships based on the feature space between the LST and the Digital Elevation Model (DEM) can be used to model the Surface Temperature Lapse Rate (NSTLR) and this can be used as an alternative to TLR in different applications (Qin et al 2018;Romshoo et al 2018;Zhang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Impact of Surface Characteristics on the Near Surface Temperature Lapse Rate

Firozjaei

Fathololuomi

Alavipanah

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.

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How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

Cited by 29 publications

References 80 publications

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation‐Dependent Warming

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

Modeling the Impact of Surface Characteristics on the Near Surface Temperature Lapse Rate

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