Maqu L-Band Brightness Temperature Simulation Dataset

This dataset presents a comprehensive study on simulating L-band brightness temperature in the Maqu site, employing both random forest regression and support vector regression algorithms. It amalgamates in-situ measurements, model predictions, programming scripts, predictor variables, prediction results, feature importance metrics, evaluation criteria, and relevant visualizations. Serving as a valuable resource, this dataset offers insights into brightness temperature simulation. Researchers, reviewers, and interested parties can reference this dataset for related studies and analyses. This dataset presents a comprehensive study on simulating L-band brightness temperature in the Maqu site, employing both random forest regression and support vector regression algorithms. It amalgamates in-situ measurements, model predictions, programming scripts, predictor variables, prediction results, feature importance metrics, evaluation criteria, and relevant visualizations. Serving as a valuable resource, this dataset offers insights into brightness temperature simulation. Researchers, reviewers, and interested parties can reference this dataset for related studies and analyses.