## 2 Study Area and Datasets
### Data Sources  
In order to conduct a drought monitoring study of Laibin City based on multispectral remote sensing data, three data sources are selected in this paper. (1) Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 images are used as the main data sources for drought analysis in Laibin City. (2) Sentinel-2 images are used to provide data sources for the analysis of the landforms in Laibin City. Remote sensing satellite images with no cloud  
Figure 1: Geographic location of the study area and corresponding remote sensing image. Figure 3: Monthly PrecipitationChanges in Laibin City in 2021. Figure 2: Monthly Temperature Changes in Laibin City in 2021.  
cover are preferred as the data for this paper's drought monitoring and analysis study. Cloud cover leads to incomplete pixel information extraction and calculation, subsequently compromising the accuracy of subsequent drought monitoring results. (3) The RSM data is sourced from the China Meteorological Data Network (CMDN) and the near real-time product dataset of the China Meteorological Administration's Land Surface Data Assimilation System (CLDAS-V2.0). For correlation analysis, this article utilizes the relative humidity data of 0-10 cm and 0-20 cm soil layers collected during the same period as the monitoring analysis. Soil relative humidity (RSM) stands as a crucial indicator for synthesizing monthly drought inversion data and assessing the severity of land drought. RSM denotes the percentage of soil moisture content relative to the field water holding capacity or saturated water content, primarily influenced by factors like precipitation, temperature, vegetation type, and terrain conditions. Therefore, RSM is a crucial indicator for characterizing agricultural drought conditions, and systematically analyzing the changes in soil relative humidity assists in accurately forecasting agricultural drought conditions. This method has been widely applied in agricultural drought mitigation practices [25]. In this study, the 2021, 2022, and 2023 satellite data of Landsat 7, 8, and 9 obtained from the USGS website ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) were selected as the main images for drought monitoring and analysis in Laibin City. Additionally, the Sentinel-2 image with higher resolution was chosen for vegetation area classification. This image served as a template to provide support data for further analysis and research on vegetation drought monitoring in Laibin City. Data selection is shown in Table 1. Furthermore, sample data collected during a field trip in 2020 were utilized to accurately understand the distribution of feature types in Laibin City. Data preprocessing involved radiometric calibration of remote sensing images, atmospheric correction, and the calculation of NDVI and LST month-by-month data to facilitate data support for drought monitoring in the future. Among them, NDVI is one of the most representative drought indices in vegetation drought monitoring. It is derived from the reflection characteristics of the red band and the near-infrared band for green vegetation. NDVI is a quantitative index that measures the difference between the near-infrared band and the red band.The expression of NDVI is:  
\[NDVI=\frac{NIR-R}{NIR+R} \tag{1}\]  
The land surface temperature (LST) often plays a crucial role in drought monitoring. At present, there are three main methods for surface temperature inversion based on remotely sensed data: the radiative transfer equation method, the single-window algorithm, and the split-window algorithm. In this study, we choose to use the radiative transfer equation method to invert the surface temperature of Laibin City in the research area. This article generates 16 day NDVI data based on Landsat series remote sensing data, and uses the radiative transfer equation method to invert the surface temperature of Laibin City in the study area, obtaining 16 day LST data.