A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing

Files and Descriptions: 1. TP_Lake.csv: This CSV file contains identified lakes in the Tibetan Plateau. 2. TP_River_Monthly_Statistics.csv: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders. 3. S2RiverWidth.py: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths. 4. best_model_vCloud10.pth: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL). 5. Example.tif: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model. Model Input Requirements:The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.