The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

Schramm, Michael; Pebesma, Edzer; Milenković, Milutin; Foresta, Luca; Dries, Jeroen; Jacob, Alexander; Wagner, Wolfgang; Mohr, Matthias; Neteler, Markus; Kadunc, M.; Miksa, Tomasz; Kempeneers, Pieter; Verbesselt, Jan; Gößwein, Bernhard; Navacchi, Claudio; Lippens, Stefaan; Reiche, Johannes

doi:10.3390/rs13061125

Cited by 46 publications

(25 citation statements)

References 33 publications

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“…A 10 m inwards buffer was applied to each field to ensure that the extracted NDVI series represented the fields. The platform https://openeo.org/ (accessed on 27 January 2021) was used to extract 5 daily Sentinel-2 NDVI pixels (10 m resolution) within each buffered winter wheat field, apply a cloud mask based on the scene classification layer from Sentinel-2 and compute the average NDVI series for each field from the extracted NDVI pixels [16]. The obtained results were cloud-free NDVI series for each winter wheat field.…”

Section: Remote Sensing Data: Ndvimentioning

confidence: 99%

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

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Section: Remote Sensing Data: Ndvimentioning

confidence: 99%

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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“…There are many routes to achieve this, including the use of advanced datacube software such as the Open Data Cube or rasdaman [52,53], or on a more basic level, through APIs. While we have successfully deployed the Open Data Cube in combination with JupyterHub [54] and GeoServer [55] for serving Sentinel-1 applications over Austria (Austrian Data Cube) [51,56], we have focused on the use of the openEO API [57] as an additional access mechanism to our worldwide datacube. This API standardises contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, mimicking the functionalities of a virtual EO raster datacube independent of the providers' data storage system.…”

Section: Discussionmentioning

confidence: 99%

A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications

et al. 2021

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The Sentinel-1 Synthetic Aperture Radar (SAR) satellites allow global monitoring of the Earth’s land surface with unprecedented spatio-temporal coverage. Yet, implementing large-scale monitoring capabilities is a challenging task given the large volume of data from Sentinel-1 and the complex algorithms needed to convert the SAR intensity data into higher-level geophysical data products. While on-demand processing solutions have been proposed to cope with the petabyte-scale data volumes, in practice many applications require preprocessed datacubes that permit fast access to multi-year time series and image stacks. To serve near-real-time as well as offline land monitoring applications, we have created a Sentinel-1 backscatter datacube for all continents (except Antarctica) that is constantly being updated and maintained to ensure consistency and completeness of the data record over time. In this technical note, we present the technical specifications of the datacube, means of access and analysis capabilities, and its use in scientific and operational applications.

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“…In what follows, we also compare sits to other approaches for big EO data analytics, such as Google Earth Engine [3], Open Data Cube [21] and openEO [67].…”

Section: Comparative Analysismentioning

confidence: 99%

Satellite Image Time Series Analysis for Big Earth Observation Data

et al. 2021

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The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

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The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

Cited by 46 publications

References 33 publications

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Estimating Farm Wheat Yields from NDVI and Meteorological Data

A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications

Satellite Image Time Series Analysis for Big Earth Observation Data

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