Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

Nusseiba Noureldeen,Tongren Xu,Xinyi Shen,Zijin Yuan,Kebiao Mao,Zhihao Qin

doi:10.3390/rs12030488

Abstract

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, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 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, p < 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, p < 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.

Highlights

Land surface temperature (LST) is an important parameter related to surface–atmosphere interactions [1,2] and plays a key role in different scientific studies, such as monitoring drought [3] and ecological, agricultural [4], and meteorological processes on the Earth’s surface [5]
To understand the overall LST trend we calculate the average of each year, Mann-Kendall test performed to verify a significant upward or downward trend, and used the Pettitt test to demonstrate change occurred in Africa, over the period from 2003–2017
Analyzing the spatiotemporal changes in LST is important for understanding the distribution of warming trends

Summary

Introduction

Land surface temperature (LST) is an important parameter related to surface–atmosphere interactions [1,2] and plays a key role in different scientific studies, such as monitoring drought [3] and ecological, agricultural [4], and meteorological processes on the Earth’s surface [5]. LST data can be used as an input for many models at both regional and global scales to improve and refine global hydroclimatic and meteorological prediction models [6]. Developing countries, including those in underdeveloped regions such as Africa, are highly vulnerable to climate change [7]. The driving forces behind the increasing LST include heat release from anthropogenic activities, the loss of vegetation cover, solar radiation, and drought and climate change at the local, regional and global scales [10,11]. LST data in most remote areas are conventionally collected by meteorological stations. To maintain the spatial continuity of LST data from these stations, various geostatistical interpolation approaches, such as kriging interpolation and inverse distance weighting (IDW) modified by digital elevation model (DEM) data, have been applied [13]

Methods

Results

Discussion

Conclusion