Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions  
[PERSON]  
1 Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland; [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (Z.B. ); [PERSON]] (M.B. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (K.P. ); [EMAIL_ADDRESS] (M.L.) 1 Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland; [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (Z.B. ); maciej. air temperature. The most popular drought indices based on these data include the Palmer Drought Severity Index (PDSI) [1], the Standardized Precipitation Index (SPI) [2], the Standardized Precipitation Evapotranspiration Index (SPE) [3], and the Selyaninov hydrothermal indicator (i.e., the hydrothermal coefficient (HTC)) [4]. However, information on the extent of drought based on these indices is not spatially precise, due to the insufficient spatial distribution of meteorological stations in many regions of the world. Therefore, new methods of assessing drought phenomena based on satellite remote sensing data have been developed. Technology based on satellite images, collected continuously over land areas, has proved to be effective in drought estimation and mitigation [5; 6; 7]. [PERSON] et al. [6] evaluated the Moderate-resolution Imaging Spectroradiometer (MODIS) indices to detect agricultural drought in relation to the anomaly of precipitation and moisture deficiency characterized by meteorological data in agricultural areas. The complexity of drought recovery analysis based on remotely sensed data has been studied in many research works. Among others, this problem has been addressed in publications, which emphasize the impact of drought on ecosystem water-use efficiency (WUE) and its components--gross primary productivity (GPP) and evapotranspiration (ET) [8; 9]. Numerous approaches to developing remote sensing-based methods for drought monitoring have been applied, including methods based on vegetation indices, canopy temperature, thermal inertia, and microwaves. As vegetation growth is mainly related to soil moisture and temperature conditions, methods based on vegetation and temperature indices proved to be the most promising for drought assessment [10]. The first works based on the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) derived from NOAA AVHRR data revealed that these indices are capable of delivering information on the extent of drought over large areas [11; 12; 13; 14]. Subsequently, more complex indices, which combine information derived from optical and thermal parts of the spectrum, have been developed. The Vegetation Health Index (VHI) takes into account both the reflectance and temperature of the vegetation canopy, characterizing better drought conditions [15]. The Temperature Vegetation Dryness Index (TVDI) incorporates the relationship between the vegetation index and the land surface temperature (LST) to enhance drought detection [16]. The Vegetation Supply Water Index (VSWI), which is the ratio of the Normalized Difference Vegetation Index (NDVI) and LST, when modified to the Normalized Vegetation Supply Water Index (NVSWI), proved to be effective for the analysis of time series, thus improving drought monitoring [17; 18]. The EVI index of the Moderate-resolution Imaging Spectroradiometer (MODIS) was used in Germany in 2018 to identify potential regional differences in order to examine drought effects in various land cover types [19]. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) can reflect the greenness and health conditions of vegetation and has been used to monitor and assess the impact of drought [20]. In particular, FAPAR has been chosen and operationally used in the formula of the Combined Drought Indicator (CDI) for monitoring drought in the European Drought Observatory (EDO) within the Copernicus Emergency Management Service [21].