A study that compared the spatial occurrence of droughts, detected by the combination of satellite drought indices over the desert and arid area in Mongolia, proved that the areas of drought occurrence were better identified with FAPAR than using the Palmer Drought Severity Index (PDSI) and that it is rather difficult to point out the most valuable drought index [22]. While analyzing the performance of various drought indices based on remotely sensed data, it was found that their capability for drought detection varies with spatial and temporal patterns, implying that the application of a specific approach is necessary for different regions of the world [23; 24; 25]. Some authors have examined the models for drought detection applied to regions that are site-specific, adjusted to local environments [26; 27; 28]. VHI is based on the NDVI and the brightness temperature, derived from the NOAA AVHRR satellite. NDVI and LST, supposed to have an inverse correlation with vegetation vigor, detect stress with low NDVI and high temperature values. However, [PERSON] et al. [29] found that northern ecosystems are characterized by positive correlations, which implies that a higher temperature has a favorable impact on vegetation activity. Thus, the conclusion is that VHI should be used with prudence, especially in high-latitude regions [30; 31]. Furthermore, it is important to examine the reaction of vegetation to water deficit. For instance, the reaction of vegetation to precipitation anomalies has been studied for Inner Mongolia [32], and it was found that vegetation has a 1-2 month lag response to precipitation anomalies and is significantly correlated with 2-6 months of cumulative precipitation anomalies. Different drought indices provide different judgments of drought severity because of difficulties in determining the best method of evaluation. Most often, the indices based on meteorological data are applied for verification of satellite-derived indices to consider the climatic/environmental variability of the region [33]. The authors indicated that different indices reflect the drought differently. The SPEI has a good correlation with yield loss at a 6 month scale and in the middle growth season, while the VegDRI (Vegetation Drought Response Index) and PADI (Process-based Accumulated Drought Index) demonstrated the highest correlation in the late growth season, which indicates that they are complementary and should be used together. There are advantages and limitations of drought indices often used jointly that are complementary for different growth seasons to indicate the yield loss. The Enhanced Combined Drought Index (ECDI) based on the weighted approach of using four independently derived satellite data: rainfall, soil moisture, land surface temperature, and vegetation vigor, has been introduced by [PERSON] et al. [34]. The index was developed in direct collaboration with \"Doctors without Borders.\" As stated by the authors, a dedicated approach has to be chosen for different needs of the users. It was pointed out that the drought impact is often neglected. Prediction of vegetation conditions and drought effects is also very important as they relate to the economic losses and food risk. [PERSON] et al. [35] predicted drought conditions up to three months ahead from the developed prediction model using artificial neural networks. In this study, it was decided to develop a model for drought assessment and monitoring for Poland, which is based on the hydrothermal coefficient HTC characterizing climatic variability of the entire country and satellite-derived Temperature Condition Index (TCI). The HTC index has been used for estimates of drought for agriculture in the Central-Eastern part of Europe. The advantage of this indicator over others is that only readily available information is needed to determine the index, i.e., the amount of rainfall and average daily temperature. Strings of any length can be considered; however, those most frequently analyzed are 15 day and 30 day observation strings [4].