Land surface temperature retrieval using cross-track infrared sounder and normalized emissivity method: a case study in the United States

Land Surface Temperature (LST) retrieval is crucial for environmental and climatological research as it plays a vital role in the Earth's energy balance and climate system. This research investigates the possibility of using the Normalized Emissivity Method (NEM) algorithm and Cross-track Infrared Sounder (CrIS) Sensor Data Records (SDR) to retrieve LST. The study analyzed four scenes with varying atmospheric conditions to assess the performance of LST retrievals. The analysis results indicate a strong correlation between atmospheric water vapor content and LST retrieval accuracy, with higher vapor content leading to increased errors and biases. Despite these challenges, the approach shows promise, with an overall Root Mean Square Error (RMSE) of 1.23K and a small bias of around 0.16K. This study emphasizes the importance of taking into account atmospheric factors when retrieving LST. It highlights the potential feasibility of CrIS data for estimating LST with NEM, which is essential for environmental monitoring and meteorological applications.