• Posts
GDAL
• Slopes with GDAL
GEE
• NDVI, Ppt and ETr
• CHIRPS
  • CHIRPS Part 1
  • CHIRPS Part 2
• GFS
  • GFS Part 1
  • GFS Part 2
Node.js
• GFS Dashboard
• GOES Visualizer
QGIS
• Batch Processing

GEE Python API: NDVI, precipitation and real evapotranspiration

Project Overview In this post, we’ll explore the correlation between multiple environmental data variables using the Google Earth Engine (GEE) Python API. Specifically, we’ll analyze yearly aggregated Normalized Difference Vegetation Index (NDVI), precipitation, and real evapotranspiration (ETr) over a region of interest spanning approximately 5,000 km² and over a five-year period (2019-2023). NDVI data will be extracted from the Sentinel-2 satellites. Precipitation data will be sourced from the CHIRPS dataset. Real evapotranspiration (ETr) data will be obtained from the MODIS satellite.

Monday, September 2, 2024 Read

GEE Python API and CHIRPS: Analyzing precipitation in Buenos Aires - Part 2

Project Overview In this post, we continue our exploration of the 2023 severe drought in Buenos Aires province, Argentina. In our previous post, we used the CHIRPS dataset to analyze the extent and impact of the drought. Now, we’ll take our analysis a step further by extracting time series data from specific coordinates within the affected region. To ensure that you can follow along and reproduce the results, all the code used in this analysis is available in my GitHub repository.

Thursday, August 15, 2024 Read

GEE Python API and CHIRPS: Analyzing precipitation in Buenos Aires - Part 1

Project Overview Welcome back! In this post, we’ll delve into the severe drought that affected Buenos Aires Province in Argentina, in 2023, using the CHIRPS dataset and the Google Earth Engine (GEE) Python API. As detailed in the GEE catalog, CHIRPS—short for Climate Hazards Group InfraRed Precipitation with Station data—is a 30+ year quasi-global rainfall dataset. This dataset integrates satellite imagery with in-situ station data at a 0.05° resolution to generate gridded rainfall at daily temporal resolution.

Tuesday, July 30, 2024 Read

GEE Python API and Precipitation Forecasting - Part 2

Project Overview Hello again! Welcome to the continuation of our deep dive into precipitation forecasting using the GFS dataset and the GEE Python API. In our previous post, we demonstrated how to use the GEE Python API along with the XEE library (an integration of GEE and xarray) to forecast precipitation for specific coordinates. This time, we’re going to expand our analysis to cover an entire region. Besides, we’ll leverage additional libraries such as Geopandas and Cartopy to create comprehensive spatial maps of precipitation forecasts.

Thursday, July 25, 2024 Read

GEE Python API and Precipitation Forecasting - Part 1

Project Overview Greetings! Welcome to the first part of a deep dive into Google Earth Engine (GEE) and its Python API. In this series, we’ll explore how to leverage the power of GEE for geospatial analysis, focusing on precipitation forecasting using the Global Forecast System (GFS) dataset. GFS is a widely-used weather forecast model developed by NOAA. It provides comprehensive weather data, including temperature, wind, and precipitation forecasts, on a global scale.

Monday, July 15, 2024 Read