A Simple Python package to pre-processing satellite-based data using Google Earth Engine.
pymapee is a simple Python package that aims to provide common functionalities to pre-process or calculate vegetation drought indices using Google Earth Engine. Currently, this package supports cloud masking (MODIS, Landsat, Sentinel-2), composite (monthly), missing value interpolation, and calculation of vegetation anomaly index (VAI) and vegetation condition index (VCI). It also supports to download an image collection (e.g., time-series NDVI or LST) or an image.
- GitHub repo: https://github.com/tuyenhavan/pymapee
- Free software: MIT license
- Masking cloud-related pixels (e.g., MODIS, Landsat, and Sentinel-2)
- Making monthly and daily composite
- Calculating monthly vegetation anomaly index (VAI) and vegetation condition index (VCI).
- Interpolating time-series using linear interpolation
- Scaling data
- Downloading an image or image collection (e.g., time-series NDVI)
Examples are provided here, and it will be regularly updated.
Install pymapee using pip
pip install pymapee
or install from Github to get the latest update.
pip install git+https://github.com/tuyenhavan/pymapee.git
If you find pymapee useful in your research, please consider citing the following paper to support our work. Thank you.
- Ha, T.V., Huth, J., Bachofer, F., Kuenzer, C., 2022. A review of earth observation-based drought studies in Southeast Asia. Remote Sensing 14, 3763. https://doi.org/10.3390/rs14153763
- Ha, T.V., Uereyen, S., Kuenzer, C., 2023. Agricultural drought conditions over mainland Southeast Asia: Spatiotemporal characteristics revealed from MODIS-based vegetation time-series. International Journal of Applied Earth Observation and Geoinformation. 121, 103378. https://doi.org/10.1016/j.jag.2023.103378
This package was created with Cookiecutter and the giswqs/pypackage project template.