Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016
Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized diff...
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doaj-cea44a568d184996812687836b5953832020-11-24T20:42:46ZengMDPI AGRemote Sensing2072-42922018-12-011012203410.3390/rs10122034rs10122034Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016Zengjing Song0Ruihai Li1Ruiyang Qiu2Siyao Liu3Chao Tan4Qiuping Li5Wei Ge6Xujun Han7Xuguang Tang8Weiyu Shi9Lisheng Song10Wenping Yu11Hong Yang12Mingguo Ma13Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaHigh School Affiliated to Southwest University, Chongqing 400700, ChinaHigh School Affiliated to Southwest University, Chongqing 400700, ChinaHigh School Affiliated to Southwest University, Chongqing 400700, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaChongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaLand surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM<sub>2.5</sub>) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM<sub>2.5</sub> increased by 0.17 K, 0.04, and 1.02 μg/m<sup>3</sup> in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM<sub>2.5</sub> on LST was more complex. On the whole, LST increased with a small increase in PM<sub>2.5</sub> concentrations but decreased with a marked increase in PM<sub>2.5</sub>. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.https://www.mdpi.com/2072-4292/10/12/2034land surface temperatureSINDVIPM<sub>2.5</sub>air pollutiontime-series analysisArcticAntarctic |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zengjing Song Ruihai Li Ruiyang Qiu Siyao Liu Chao Tan Qiuping Li Wei Ge Xujun Han Xuguang Tang Weiyu Shi Lisheng Song Wenping Yu Hong Yang Mingguo Ma |
spellingShingle |
Zengjing Song Ruihai Li Ruiyang Qiu Siyao Liu Chao Tan Qiuping Li Wei Ge Xujun Han Xuguang Tang Weiyu Shi Lisheng Song Wenping Yu Hong Yang Mingguo Ma Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 Remote Sensing land surface temperature SINDVI PM<sub>2.5</sub> air pollution time-series analysis Arctic Antarctic |
author_facet |
Zengjing Song Ruihai Li Ruiyang Qiu Siyao Liu Chao Tan Qiuping Li Wei Ge Xujun Han Xuguang Tang Weiyu Shi Lisheng Song Wenping Yu Hong Yang Mingguo Ma |
author_sort |
Zengjing Song |
title |
Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 |
title_short |
Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 |
title_full |
Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 |
title_fullStr |
Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 |
title_full_unstemmed |
Global Land Surface Temperature Influenced by Vegetation Cover and PM<sub>2.5</sub> from 2001 to 2016 |
title_sort |
global land surface temperature influenced by vegetation cover and pm<sub>2.5</sub> from 2001 to 2016 |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-12-01 |
description |
Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM<sub>2.5</sub>) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM<sub>2.5</sub> increased by 0.17 K, 0.04, and 1.02 μg/m<sup>3</sup> in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM<sub>2.5</sub> on LST was more complex. On the whole, LST increased with a small increase in PM<sub>2.5</sub> concentrations but decreased with a marked increase in PM<sub>2.5</sub>. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature. |
topic |
land surface temperature SINDVI PM<sub>2.5</sub> air pollution time-series analysis Arctic Antarctic |
url |
https://www.mdpi.com/2072-4292/10/12/2034 |
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