The dataset descriptions provide us with all the information we need to import and manipulate these datasets: the availability, the provider, the Earth Engine Snippet, and the available bands associated with images in the collection. In the following sections, we work with the MODIS land cover (LC), the MODIS land surface temperature (LST) and with the USGS ground elevation (ELV), which are ee.ImageCollections. If you want to know more about different data models, you may want to visit the Earth Engine User Guide. For example, the Global Administrative Unit Layers giving administrative boundaries is a ee.FeatureCollection and the MODIS Land Surface Temperature dataset is an ee.ImageCollection.
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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.
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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.
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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.