![]() “Classification of Ice and Open Water in Nizhnesvirsky Lower Bay using Sentinel-1 IW Product”.These are based on the Sentinel Hub API system, designed for streamlined access to earth observation data on the cloud. Use case notebooks take the analysis several steps further by adding processing steps and deriving analysis results. This notebook shows how to build an openEO process out of a series of steps that can then run automatically. This notebook illustrates how to build user-defined functions that can be part of an openEO process. Cloud masking, outlier filtering and smoothing the series using openEO tools aids futher analysis. This notebook demonstrates an application case, displaying NDVI time series for a set of agricultural fields. OpenEO has a high-performance batch job controller that takes care of processes running in parallel and allows you to follow the status of the job. This notebook shows how to scale up your workflow graph by using batch jobs. In openEO, workflows can be defined as a process graph. “Using openEO Batch Jobs to run large and heavy workflows”.This notebook provides an example for loading a data collection from the Copernicus Data Space Ecosystem and building an openEO DataCube. “openEO Basics: How to load a data cube from a data collection?”.This notebook shows how to import the openEO package and connect to the Copernicus Data Space Ecosystem openEO back-end. “openEO Basics: Discovery of Collections and Processes”.In the Jupyter lab, a set of openEO notebooks are provided to support entry to openEO and operational code development. OpenEO is an API system that supports efficient processing of large-scale earth observation datasets with simple and clear commands.It enables connecting different clients to cloud back-ends. A code example is also provided for downloading multiple files in one response, packed together in a TAR archive. However, this example also includes mosaicking of several images to create a cloud-free end result, and accessing different data collections such as elevation models. This is also a beginner's guide to downloading data using the Sentinel Hub Processing API. Instead of full image granules, the API enables selection of subsets based on spatial extent, spectral bands and data quality (cloud masking). This set of APIs enables streamlined access to the data in code. In addition to OData requests for full data granules, this notebook also provides examples of similar queries using the Sentinel Hub APIs. This notebook describes how to access the functionality of the Open Access Hub in the Copernicus Data Space Ecosystem using simple commands. “Migrating your workflows from the Copernicus Open Access Hub to the Copernicus Data Space Ecosystem”.It uses the OData protocol to run queries and select data to download. This notebook provides an example of interacting with the data catalogue and downloading EO products for further processing. “How to query Copernicus Data Space Ecosystem Catalogue and download products”.These notebooks show you how to use several libraries including OData, openEO and Sentinel Hub. ![]() The first set of code examples available in the Jupyter Notebooks represents various ways to access satellite data and also end-to-end use cases from environmental research.
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