The Relationship between the Seasonal Dynamics of Surface Temperature and NDVI in Urbanized Areas of an Arid Zone. The Case of the Volgograd Agglomeration

Shinkarenko, S.S.; Kosheleva, O. Yu.; Гордиенко, О. А.; Dubacheva, A.A.; Omarov, R. S.

doi:10.1134/s0001433821120197

Cited by 2 publications

(4 citation statements)

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“…The degrees of correlations would increase with the enlargement of scales with the same positive or negative attributes [121]. In addition, the relationships betweenLU/LC indices and LSTs should be further studied in different land uses or covers [28,34,38,122]. Finally, the detailed mechanism can be analyzed by using several methods comprehensively, such as remote sensing, experimental observation, numerical simulation, artificial intelligence, etc.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…Many previous studies have focused on analysing the effects of LU/LC indices on LSTs at the city scale. The LSTs were mainly derived by the Landsat thermal infrared data [15,[24][25][26][27], Moderate-resolution Imaging Spectroradiometer (MODIS) data of Aqua or Terra [28,29] and Terra/Aster data [30][31][32][33]. These indices included the normalized difference building index (NDBI) [26,27,34,35], normalized difference impervious index (NDISI) [14], index based built-up index (IBI) [7,27,36,37], new built-up index (NBI) [16], urban index (UI) [14,16], dry built-up index (DBI) [14], normalized difference vegetation index (NDVI) [24,26,38,39], soil-regulating vegetation index (SAVI) [16,36,40,41], enhanced vegetation index (EVI) [42], normalized difference moisture index (NDMI) [43,44], normalized difference water index (NDWI) [14,15,24,26], modified normalized difference water index (MNDWI) [14,39,40,43], normalized difference bareness index (NDB...…”

Section: Introductionmentioning

confidence: 99%

“…These indices included the normalized difference building index (NDBI) [26,27,34,35], normalized difference impervious index (NDISI) [14], index based built-up index (IBI) [7,27,36,37], new built-up index (NBI) [16], urban index (UI) [14,16], dry built-up index (DBI) [14], normalized difference vegetation index (NDVI) [24,26,38,39], soil-regulating vegetation index (SAVI) [16,36,40,41], enhanced vegetation index (EVI) [42], normalized difference moisture index (NDMI) [43,44], normalized difference water index (NDWI) [14,15,24,26], modified normalized difference water index (MNDWI) [14,39,40,43], normalized difference bareness index (NDBaI) [24,26,35,45], bare soil index (BSI) [14,16], dry bare soil index (DBI) [14], etc. Most studies focused on the relationships between LSTs and the above-mentioned LU/LC indices during the daytime [24,26,46,47] and only a few during the nighttime …”

Section: Introductionmentioning

confidence: 99%

“…The relationships between LU/LC indices and LSTs were analyzed in the summer [25,27,47,48], winter [27,[49][50][51], spring or autumn [24,25,39,52] in a single year [14,24,50] or several years [49,51,53,54]. The study areas were widely located in North America [27,55], western Europe [16,44,56], eastern Europe [28], sub-Saharan Africa [49,50,57,58], western Asia [30,39,46,48], southern Asia [27,47,59,60], eastern Asia [14,[24][25][26], southeastern Asia [61], etc. The analysis methods included a correlation analysis [27,34,47,51], linear fitting [24,25,…”

Section: Introductionmentioning

confidence: 99%

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How Are Land-Use/Land-Cover Indices and Daytime and Nighttime Land Surface Temperatures Related in Eleven Urban Centres in Different Global Climatic Zones?

Zhao

Xin

et al. 2022

Land

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Improving the urban thermal environment can enhance humans’ well-being. Nevertheless, it was not clear which land-use/land-cover (LU/LC) indices were optimal for explaining land surface temperatures (LSTs) and how they affected LSTs in cities in different climatic zones, especially during the nighttime. Thus, the Aqua/MODIS and Landsat/OLI data were mainly used to explore the optimal indices of building, vegetation, water and bare soil and to analyze their effects on LSTs in eleven urban centers in global distinct climatic regions. Results showed several LU/LC indices had high probabilities of being optimal indices to explain LSTs under different conditions. The daytime LSTs were usually significantly negatively correlated with vegetation indices and positively correlated with building and bare soil indices (p < 0.05). These relationships were stronger in the summer than winter. The nighttime LSTs were usually significantly positively and negatively correlated with building and vegetation indices in the summer, respectively (p < 0.05). These correlations were generally weaker during the nighttime than daytime. The nighttime LSTs were significantly positively and negatively correlated with water and bare soil indices, respectively (p < 0.05). Significant linear multiple regressions commonly existed between daytime and nighttime LSTs and four kinds of LU/LC indices (p < 0.05). These findings helped optimize urban thermal comfort, downscale city LSTs, etc.

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