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The influence of land-cover type on the relationship between NDVI–LST and LST-T air

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Air temperature (T 2m or T air) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T 2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (= 0.0001) with T 2mDay (daytime air temperature) and T 2mNight (night-time air temperature). The correlation (r) between LSTDay and T Day is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T 2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T 2mNight.
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Document Type: Research Article

Affiliations: 1: Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany 2: Institut für Weltraumwissenschaften, Freie Universität Berlin, Carl-Heinrich-Becker-Weg, Berlin, Germany

Publication date: March 4, 2018

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