## 3 Results
### Comparative Analyses of Soil Moisture Anomalies Versus Land Surface Temperature Anomalies  
#### 3.3.1 Site-Scale Correlation Analyses  
Pearson's correlation coefficient is used as a measure of the correspondence between SMAI anomalies and LST anomalies. Based on the method of covariance, this test statistic measures the association between the two continuous variables and gives information about the magnitude of the association, or correlation, as well as the direction of the relationship. Pearson's r of mean monthly anomaly values (regarding the 2007-2018 period) are calculated for the region of each synoptic station from the NIMH operational network. Table 3 shows the Pearson correlation between the anomalies of SMAI and LST during the growing season (March-October). Selected stations representative for Nomenclature of Territorial Units for Statistics (NUTS) classification of Bulgaria according to the European Union ([[https://ec.europa.eu/eurostat/documents/345175/7451602/2016-NUTS-2-map-BG.pdf](https://ec.europa.eu/eurostat/documents/345175/7451602/2016-NUTS-2-map-BG.pdf)]([https://ec.europa.eu/eurostat/documents/345175/7451602/2016-NUTS-2-map-BG.pdf](https://ec.europa.eu/eurostat/documents/345175/7451602/2016-NUTS-2-map-BG.pdf))) are shown. The NUTS classification was set up by Eurostat, the Statistical Office of the European Union, to provide a comprehensive and consistent breakdown of territorial units necessary for collecting, developing and harmonizing regional statistics in the European Union ([[http://www.europarl.europa.eu/factsheets/er/sheet/99/common-classification-of-territorial-units-or-statistics-nuts-](http://www.europarl.europa.eu/factsheets/er/sheet/99/common-classification-of-territorial-units-or-statistics-nuts-)]([http://www.europarl.europa.eu/factsheets/er/sheet/99/common-classification-of-territorial-units-or-statistics-nuts-](http://www.europarl.europa.eu/factsheets/er/sheet/99/common-classification-of-territorial-units-or-statistics-nuts-))). In this context, Bulgaria is divided into six NUTS--northeastern, NE; notthentral, NC; northwest, NW; southeastern, SE; southcentral, SC; southwest, SW. Calculations are performed for each one of the soil depths, 20, 50, and 100 cm along the root zone. Analyses reveal a common trend in the degree of association between anomalies of SMAI and LST for the NUTS regions. SMAI and LST appear to be highly negatively correlated during the growing season (Table 3) for all NUTS of Bulgaria. There is a high degree of association between the dry SMAI and warm LST anomalies, exceeding \(-0.50\) most of the time and for most NUTS during the active growing season. The correlation is strong (\(-0.60\) to \(-0.79\)) and very strong (above \(-0.79\)) in July and August. The strong negative relationship during dry periods highlights the role of LST geostationary retrievals in providing long-term information for drought occurrence and its severity because of the high temporal resolution of MSG. In spring months there is a second peak in correlation between SMAI and LST anomalies (at some stations) but this is not the case of a 'dry' land surface anomaly because in this period SMAI indicates optimum moistening and LST is lower (see Figure 2a). In June, when the vegetation cover is fully developed and SMA for that time of year is optimum (precipitation peak for the climate of Bulgaria), the correlation in some regions (NW, SC, SW) becomes weak/very weak due to the cooling effect of non-disturbed evapotranspiration. In October, when the growing season has ended, the relationship between the two indices is low because of the lack of vegetation cover and the periodical precipitation in the autumn. The SW region, Blagoevgrad is an exception to this trend which is related to the longer growing season due to the southern flow of Mediterranean Sea air masses. SMAI anomalies for all soil depths correlate to a high degree with LST anomalies, and for most of them, the correlation is highest for the 50 cm soil depth.