Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)
It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly...
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doaj-0a607ab68743470a91e46f5e72fc08cb2020-11-25T02:18:24ZengMDPI AGRemote Sensing2072-42922020-02-0112348810.3390/rs12030488rs12030488Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)Nusseiba NourEldeen0Kebiao Mao1Zijin Yuan2Xinyi Shen3Tongren Xu4Zhihao Qin5Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaCivil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USAState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100101, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaIt is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003−2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er−ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003−2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, <i>p</i> < 0.05) and the central (slope = 0.08, R = 0.89, <i>p</i> < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, <i>p</i> < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, <i>p</i> < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.https://www.mdpi.com/2072-4292/12/3/488land surface temperaturewarming trenddroughtmodisafrica |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nusseiba NourEldeen Kebiao Mao Zijin Yuan Xinyi Shen Tongren Xu Zhihao Qin |
spellingShingle |
Nusseiba NourEldeen Kebiao Mao Zijin Yuan Xinyi Shen Tongren Xu Zhihao Qin Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) Remote Sensing land surface temperature warming trend drought modis africa |
author_facet |
Nusseiba NourEldeen Kebiao Mao Zijin Yuan Xinyi Shen Tongren Xu Zhihao Qin |
author_sort |
Nusseiba NourEldeen |
title |
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) |
title_short |
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) |
title_full |
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) |
title_fullStr |
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) |
title_full_unstemmed |
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017) |
title_sort |
analysis of the spatiotemporal change in land surface temperature for a long-term sequence in africa (2003–2017) |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-02-01 |
description |
It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003−2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er−ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003−2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, <i>p</i> < 0.05) and the central (slope = 0.08, R = 0.89, <i>p</i> < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, <i>p</i> < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, <i>p</i> < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa. |
topic |
land surface temperature warming trend drought modis africa |
url |
https://www.mdpi.com/2072-4292/12/3/488 |
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