# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 1 Introduction  
Droughs are complex and multifaceted natural disasters that can lead to a decline in agricultural productivity [1], land desertification [2, 3], and forest degradation [4, 5, 6]. Compared with other natural disasters, such as floods and earthquakes, droughts have a wider impact, longer duration, and cause more damage [7; 8; 9]. Therefore, it has become increasingly important to effectively monitor droughts as a means to deal with extreme drought events [10]. Currently, drought monitoring is typically based on ground-based meteorological stations and remote sensing data [11]. Although the data observed by ground-based meteorological stations are relatively more accurate, they have the drawbacks of a high cost, small coverage, and spatially variable distribution [12]. In contrast, remote sensing data have wider coverage, higher spatial resolution, and greater timeliness [13]. So, remote sensing has been applied for drought monitoring on a regional scale [14]. Drought indices play a crucial role in monitoring drought events, and many drought indices have been developed [15; 16]. The most common meteorological drought indices are the Standardized Precipitation Index (SPI) [17], the Palmer Drought Severity Index (PDSI) [18], and the Standardized Precipitation Evapotranspiration Index (SPEI) [19], which have been extensively validated in numerous studies [20; 21; 22]. Remote sensing-based drought indices are usually classified into vegetation indices, such as the Normalized Vegetation Index (NDVI) [23] and the Vegetation Condition Index (VCI) [24]; vegetation temperature indices, such as the Vegetation Temperature Conditional Index (VTCI) [25] and the Temperature Vegetation Drought Index (TVDI) [26]; and soil moisture indices, such as the Soil-adjusted Vegetation Index (SAVI) [27] and the Soil Moisture Monitoring Index (SMMI) [28]. Vegetation drought indices have exhibited a time lag in drought monitoring, but adding surface temperature as a moisture stress factor to construct a drought index model can improve timeliness [29; 30]. In particular, the TVDI is a simplified land surface dryness index [31], which was proven to be effective in simulating soil moisture changes and has been widely applied in drought monitoring [32; 33]. The drought indices have usually been calculated by MODIS, Landsat 8, and Sentinel-2, etc., and were well validated in studies of different scales [34; 35; 36]. However, these remote sensing data of earth observation satellites exhibit limitations in terms of revisiting frequency [37; 38; 39]. The rapid dynamics of meteorological processes, such as flash droughts and heavy rainfall, might be ignored [40]. For example, MODIS generally achieves global coverage in a minimum of one day (but mostly two or more days), but flash droughts that have higher temporal and spatial variability are not fully captured [41]. With the advantages of a wide observation range, high sampling frequency, and high spatial resolution, geostationary meteorological satellites are widely used for monitoring rapid processes such as forest fires, dust storms, and air pollution [42]. Research on drought monitoring using geostationary meteorological satellites is seldom reported. [PERSON] et al. [43] utilized brightness temperature data from Himawari-8 to calculate the Temperature Rise Index (TRI) and demonstrated its efficacy in drought warnings. [PERSON] et al. [44] used daily rainfall data from weather stations and daily Meteosat Second Generation (MSG) geostationary meteorological satellite NDVI data to examine the growth status of vegetation under drought conditions. [PERSON] et al. [45] calculated the Perpendicular Drought Index (PDI) based on FY-4 AGRI and Himawari-8 AHI data and verified the effectiveness of FY-4A in drought monitoring. However, they selected a single image with the smallest cloud cover within one day in this paper, ignoring the high sampling frequency characteristic of FY-4A.