Goes-16 Level 2 Land Surface Temperature Product– Filling for Cloud Masked Data
The GOES-16 satellite ground segment team develops different levels of environmental productsand makes them readily available to the public. The level 2 Land Surface Temperature (LST)product aims to provide a remote measurement of the Earth’s surface temperature every hour.LST is of fundamental importance to many aspects of the geosciences, for example, to study theUrban Heat Islands (UHI) effect. The product however, does not provide values for pixels wherethe satellite imager’s view is obstructed by clouds, as determined by a cloud mask intermediateproduct. The objective of this thesis is to estimate LST in the cloud masked pixels by takingadvantage of the temporal resolution of the GOES-16 level two product and other remote sensingproducts related to the temperature of the area of interest. This is done by using two differentmethods; Long-Short Term Memory (LSTM) Neural Network and an Interpolation Method.LSTM is well-suited to make prediction based on time series data and the interpolation methoduses neighbor pixels without cloud cover for the estimation of LST. The accuracy of theestimation was measured by reconstructing the images with the estimated values of LST andusing R and RMSE as performance evaluation metrics. The results show that the interpolationmethod has better performance in the estimation of LST since the reconstructed images showbetter values in the performance evaluation metrics ranging between 0.3460 to 0.9880 for R and0.2929 K to 0.8111 K for RMSE. The performance evaluation metrics of the images that werereconstructed using LSTM range between -0.0859 to 0.5548 for R and 0.546 K to 1.9844 K forRMSE.
Electrical engineering|Environmental Studies
Ortega, Guadalupe, "Goes-16 Level 2 Land Surface Temperature Product– Filling for Cloud Masked Data" (2021). ETD Collection for University of Texas, El Paso. AAI28869303.