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This system fetches, processes, and predicts soil moisture using real-time data.
Link github: https://github.com/tuananhdao/soil-moisture
The validators query the contributors during specific execution windows defined in the get_request_windows method. These windows are:
Maximize the final score, given any day
final_score = await scoring_mechanism.score(pred_data)
The input to run_inference is a dictionary containing:
The output must be a dictionary with:
IFS weather variables (in order):
Note: Evapotranspiration are variables computed using the Penman-Monteith equation (FAO-56 compliant). see soil_apis.py for more information on the data processing, transformations, and scaling.
EARTHDATA_USERNAME=<YOUR_EARTHDATA_USERNAME>
EARTHDATA_PASSWORD=<YOUR_EARTHDATA_PASSWORD>
EARTHDATA_API_KEY=<YOUR_EARTHDATA_API_KEY> # earthdata api key for downloading data from NASA
sudo apt-get install -y gdal-bin
sudo apt-get install -y libgdal-dev
sudo apt-get install -y python3-gdal
Via brew
brew install gdal
pip install -r requirements.txt
Run the main prediction service:
python soil.py
Name | Description | Spatial Resolution | Temporal Resolution | Link |
SMAP L4 | Volumetric water content, soil moisture | 9km | 3 hrs | |
Sentinel-2 | Red and near-infrared bands | 50m | 5 days | |
SRTM | Elevation data | 500m | N/A | |
ERA5 | Surface temperature, wind speed, pressure, precipitation | 9km | 1 hr | |
H3 | Grid Mapping | N/A | N/A |
What It Takes To Be Fluent In Software Development?
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