https://github.com/kadyb/lakes_temp
Lakes temperature analysis based on satellite images
https://github.com/kadyb/lakes_temp
dataset lake landsat poland remote-sensing temperature water
Last synced: 7 months ago
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Lakes temperature analysis based on satellite images
- Host: GitHub
- URL: https://github.com/kadyb/lakes_temp
- Owner: kadyb
- License: mit
- Created: 2019-12-26T17:24:32.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-08-11T08:17:19.000Z (about 3 years ago)
- Last Synced: 2024-01-29T21:14:06.402Z (over 1 year ago)
- Topics: dataset, lake, landsat, poland, remote-sensing, temperature, water
- Language: R
- Homepage:
- Size: 7.47 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Lakes temperature
This repository contains the data, code, and results for “[Evaluation of Methods for Estimating Lake Surface Water Temperature Using Landsat 8](https://www.mdpi.com/2072-4292/14/15/3839)” article.
## Dataset
The `data` folder contains the following files:
- `SR_processed.csv` - surface reflectance after cleaning
- `TOA_processed.csv` - top-of-atmosphere reflectance after cleaning
- `hydro_stations.csv` - list of hydrological stations (38) with name and ID
- `lakes_temp.csv` - lake water temperature in degrees Celsius
- `pointsFeatures.txt` - location of measurement points as a JavaScript object (this is required by Google Earth Engine)
- `SMW_LST_L8_Lakes_newEmiss.csv` - estimated water temperatures using the Ermida et al. (2020) model
- `coordinates` subfolder - location of measurement points as a shapefile
- `reflectance` subfolder - raw (not cleaned) SR and TOA reflectance
- `vector/lakes.gpkg` - extent of 4 sample lakes (Drawsko, Ełckie, Gopło, Łebsko)
## Reproduction
1. Open the `geomorph_clustering.Rproj` project file in [RStudio](https://rstudio.com/).
2. Create a JavaScript object with coordinates using `01_create_features.R` that will be used in [Google Earth Engine](https://earthengine.google.com/).
3. Download reflectance data from Google Earth Engine using `02_Landsat8_SR_download.js` (Surface Reflectance) and `02_Landsat8_TOA_download.js` (Top-of-Atmosphere Reflectance).
You must use the coordinates from the `pointsFeatures.txt` file.
4. Download data from hydrological stations (water temperature) using `04_hydro_process.R`.
5. The main part of the analysis was done in the `05_analysis.R` script.
It includes training of LM and RF models and validation of all LM, RF, LST and LST-L2 models.
6. `06_LST_calibration.R` was used to compare calibration methods for the LST-L2 (USGS) product using empirical data.
7. Entire satellite scenes for spatial prediction can be downloaded using script `07_download_scene.js`.
8. Prediction using LM or RF model can be done with script `08_predict.R` for individual lakes or the entire scene.
The `{terra}` package was used to process the raster data.
The algorithm to generate the LST product developed by [Ermida et al. (2020)](https://www.mdpi.com/2072-4292/12/9/1471/htm) is available in the Google Earth Engine repository: https://code.earthengine.google.com/?accept_repo=users/sofiaermida/landsat_smw_lst
## Results
The results of this research are saved in `results` folder:
- `lakes_stats.csv`- performance statistics of LM and RF models considering training and test lakes
- `month_stats.csv` - performance statistics of LM and RF models by month
- `predictions_testset.csv` - testset with actual measurements and estimated by 4 models (LM, RF, LST, LST-L2)
- `rf_model.rds` - trained RF model in *.rds* format (`{ranger}` package is required)
Additionally, in the `images/predict` folder there are 4 exemplary results of the spatial prediction by the RF model for different terms.
## Acknowledgement
The source of the hydrological data is the Institute of Meteorology and Water Management - National Research Institute (https://www.imgw.pl/).
Landsat-8 images courtesy of the U.S. Geological Survey (https://earthexplorer.usgs.gov/) and Google Earth Engine (https://earthengine.google.com/).