Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

Abstract

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

“…However, Tair and LST comparisons between Landsat and MODIS demonstrate a good correlation, particularly highlighting the UHI effect, with MODIS exhibiting a slightly stronger correlation with Tair, possibly due to the larger dataset [12,15,30,38,39,76]. Studies reveal a positive correlation between LST and climatic variables like relative humidity, precipitation, and altitude as well as solar radiation and incoming surface longwave radiation [25,40,80]. It is therefore possible to estimate global temperature trends using Tair data, by leveraging LST trends obtained regional studies in comprehending the nuances of global climate dynamics [1-6,98].…”

“…The main statistical approaches and methods commonly used in the study of LST and especially LULC interdependence are supervised and unsupervised techniques [9,20,25,[67][68][69][70]; Mann-Kendall statistics [13,71]; principal component analysis end ordinary least squares [72,73]; cellular-automata [21,33,48,74] and most widely used linear and multiple linear regression analyses [9,11,15,28,[33][34][35][36][37][38][39]49,57,76]. Particular attention is merited by studies focused on establishing linear and nonlinear dependencies between LST, UHI effects, and various vegetation indices [31,67,[77][78][79][80][81][82][83].…”

“…The exploration of UHI effects within various global cities, notably Munich, Germany, Shanghai, China, and Tokyo, Japan, reveals a consistent mitigation impact provided by urban vegetation and water bodies [7,13,32]. A comprehensive analysis demonstrate an increase in LST as areas transition from dense vegetation to sparser vegetation or bare land, with moisture content being a pivotal factor in this proces [8,9,37,80,89,91]. The reduction of urban greenery directly correlates with the expansion of bare land and built-up surfaces, further exacerbating urban temperatures [67,71].…”

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

“…According to Refs. [ 49 , 50 ], there is a gradual increase in the daily mean annual maximum temperature when moving from the northeastern part of the Hindukush Mountains toward the southeastern plateau regions. The lowest areas in the southeastern zone experience the highest mean annual maximum temperatures.…”

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

“…The ecological environmental factors that affect LST mainly include precipitation (PREP), relative humidity (RH), and sunshine hours (SSH) [17][18][19]. The impact of precipitation on LST can be distinctly perceived in the real world, as demonstrated by numerous studies [20][21][22].…”