soil erosion using RUSLE model, Schemes and Mind Maps of Environmental Science

Download soil erosion using RUSLE model and more Schemes and Mind Maps Environmental Science in PDF only on Docsity! The RUSLE model on Google Earth Engine First steps ● The RUSLE model calculates yearly average soil erosion rates using data on precipitation, landcover, soil, topography and management ● Objective: Implement RUSLE on GEE at a high spatial resolution, and analyze the effects of terracing on soil erosion reduction using the new high-resolution terracing map of China ● Output: Paper on terracing ● The model is coded in python ● Only the erosivity.f90 is written in fortran code and then transformed into a python module with: f2py -c -c erosivity erosivity.f90; this needs to be done on the local machine, not sure how this can work on GEE ● Also in the current setup the reprojection of the RUSLE factors (C, K and R) from degrees to meters (see Output_File.py) is done on the local machine. This needs to be modified when model runs on GEE ● All the model code files are available on github: https://github.com/wieka29/RUSLE_GEE Model structure and files (2) ● The test region is located in the North of China (between 36 and 38 degrees north and 106 and 108 degrees East) where one can find a lot of terraces ● The data to test the model has been uploaded to google drive: https://drive.google.com/drive/folders/1cCqCj8HUgIK5VWqNJwhrMYWeutaQ TBSD?usp=sharing ● The data on topography, precipitation, landcover and ndvi has been extracted from satellite imagery using the GEE. Date on terracing has been provided by the group of Le Test data and test region Next steps: 1. Treat each RUSLe factor separately 2. Find a way to do array calculations directly on the extracted satellite images (writing and executing functions on GEE using Python API) 3. Find a way to use f2py and functions like reprojection on GEE 4. Display results as an image on GEE How to proceed