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The output will contain instructions on how to grant this notebook access to Earth Engine using your account. Therefore, read the description carefully and make sure you know what kind of dataset you are selecting! Run me firstįirst of all, run the following cell to initialize the API. satellite image, interpolated station data, or model output) vary from one dataset to another. Of course the resolution, frequency, spatial and temporal extent, as well as data source (e.g. GRIDMET temperature, precipitation, and evapotranspiration, for example.OpenLandMap datasets with soil properties at a resolution of 250 m (e.g.SRTM global elevation with a resolution of 30 m,.Well, inside the Earth Engine Catalog we find: temperature, precipitation, evapotranspiration). clay, sand, silt content) and some meteorological observations (e.g.

Let's say that we need to know the elevation of a region, some soil properties (e.g. Have you ever thought that getting a meteorological dataset could be as easy as finding the nearest pizzeria? To convince you, visit the Earth Engine Data Catalog and explore datasets using the search bar or browsing by tag. Exploration of the Earth Engine Data Catalog In this last part, we’ll see how to include some GEE datasets as tile layers of a folium map. Secondly, we will detail procedures for static mapping and exporting results as a GeoTIFF.įinally, the folium library will be introduced to make interactive maps. An application of this procedure will be done to extract land surface temperature in an urban and a rural area near the city of Lyon, France to illustrate the heat island effect. After some setup and some exploration of the Earth Engine Data Catalog, we’ll see how to handle geospatial datasets with pandas and make some plots with matplotlib.įirst, we’ll see how to get the timeseries of a variable for a region of interest. In this tutorial, an introduction to the Google Earth Engine Python API is presented.

How can we manipulate these petabytes of data?.

What data are available and where can it be found?.

When using these geospatial data, a few questions arise: These geospatial data are used every day by scientists and engineers of all fields, to predict weather, prevent disasters, secure water supply, or study the consequences of climate change.

wind speed, groundwater recharge), have become freely available from multiple national agencies and universities (e.g. land surface temperature, vegetation) or the output of large scale, even global models (e.g. Within the last decade, a large amount of geospatial data, such as satellite data (e.g.