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Create maps and a chart to visualize the LST changes over time! Calculated the average Land Surface Temperature (LST) for each month of 2023 in India. Loaded the India boundary map: Fetched the shapefile of India to define the area of interest. Defined a function to process each month: Grabed Landsat 9 satellite images for the month: Filtered images based on dates and cloud cover. Combined multiple images into a single image for better quality. Extracted LST data: Selected the Thermal Infrared band. Applied a formula to convert raw values to Celsius. Calculated mean LST: Average the LST values within the India boundary. Printed the mean LST value for the month. Created a map: Centered the map on India. Displayed the LST data as a colorful map. Exported the map to Google Drive: Saved the map image for sharing or further analysis. Returned the mean LST value: Used this value for creating the chart. Used My own color palette: Choosed a color scheme called "handsome Pakistani" for the maps. Processed each month from January to December 2023: Called the function defined earlier for each month. Stored the mean LST value for each month in a list. Created a time series chart: Ploted the mean LST values over time on a chart. Customized the chart title and appearance. Displayed the chart. Key points: Satellite images: The code uses Landsat 9 satellite images to measure LST. Cloud cover: It filters out images with too much cloud cover to get accurate readings. LST calculation: LST is calculated using a specific formula that converts raw image values to temperature. Maps and charts: The code creates both visual maps and a time series chart to show how LST changes over time. Google Earth Engine: It's written in a language called JavaScript and runs on Google Earth Engine, a platform for processing satellite data.