|SAGRIS BACKEND TESTING SERVICE|
Check out parcel-based SAGRIS reports from a number of selected farms in Lithuania, which are currently used for development of a new COSFARM smart-farming service. We offer samples of pre-processed images for the clients looking for production-ready Sentinel-1/2 datasets.
SAGRIS (Sentinels-based AGRiculture Information Service Component) is automated service for satellite data pre-processing, operational mapping and per-parcel statistical sampling, providing a database back-end for application developers and Big Data analytics. It is under development since 2016 by GEOMATRIX UAB - a small private company founded in 2011 and currently based in Vilnius (Lithuania). In 2017 SAGRIS was selected for a Copernicus Masters Accelerator and received a H2020 SME Instrument P1 grant for development of a feasibility study and business development plan. Results of the study revealed unique capabilities and a significant market potential of the service in heavily clouded regions of Europe, encouraging it's full-scale implementation and commercialisation. The main achievements of SAGRIS project and insights into its future development were recently published on CORDIS portal.
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SAGRIS is designed as a cloud-based modular satellite imagery processing and BIG Data analysis engine with artificial intelligence sub-system, designed for continuous monitoring over millions of farmland parcels, tracing certain indicators of deteriorating crops or development of potentially hazardous conditions and continuously providing the end-users with personalised situational reports and early warning notifications on any negative developments. Based on Copernicus open data – Sentinel-1 polarimetry SAR products combined with multi-spectral images from Sentinel-2 and Landsat-8 satellite sensors – and exceptional use of open source software, it is designed as a back-end for commercial B2B and B2C Farm Advisory System (FAS) applications and B2P EU Common Agriculture Policy (CAP) subsidies control services operated in heavily clouded regions.
SAGRIS processing chain is based on techno-logically independent, but functionally inter-connected components – or processing “blocks” – including automated (A) discovery and retrieval of EO products, (B) pre-processing into production-ready images, (C) injection into EO Data Cubes, (D E F) aggregated statistics, standard biophysical indexes, (G) per-parcel sampling of image statistics and graphics, (H) Big Data analysis and (I) on-line reporting.
SAGRIS back-end service can be customised for different applications based on continuous time-series of ortho-rectified and calibrated satellite images, per-object sampling of signal statistics from millions of objects (parcels, forestry blocks, etc.), detection of certain patterns with OBIA, data mining and machine learning algorithms, as well as generation of per-object personalised reports or customised thematic maps for operational monitoring and reporting purposes.
CAPCON and COSFARM services will extend SAGRIS Analyst and Reporter modules with specific filters, classification algorithms and triggers for a front-end CAP subsidies control and smart farming services.
Functional independence of SAGRIS processing blocks enables a number of expansion scenarios into the countries with already established smart farming applications. In addition to standard SAGRIS smart farming services, it could provide B2B market intelligence services for targeted marketing of PA services, as well as back-end services of image pre-processing, statistical sampling, digital mapping and flexible BIG Data analysis for the existing smart farming or market analysis services. SAGRIS production and operational costs, as well as a steadily growing proportion of RTD and marketing costs, will be covered by revenues from B2B and B2P back-end image processing and CAP subsidies control services.
SAGRIS back-end service was used for analysis of crop development and soil moisture throughout 2018 farming season in a randomly selected 46 ha field of winter wheat (N Lithuania), demonstrated on a selection of standard biophysical indexes derived from S2 images and filtered VV polarisation back-scatter signal. Density, consistency and maturity of vegetation cover presented by S1 polSAR data seems to be closely resembling vegetation index patterns derived from the S2 optical sensor, although spatial accuracy of S1 IW GRD data is considerably lower. S2 vegetation (RDVI) and chlorophyll (CIG) indexes revealed a large patch with homogeneous vegetation cover (detected by S1 on 2018-06-09 and 06-22), but likely infested by weeds (see S2 chlorophyll index) in the upper part of the field, proving a potential of EO sensors complexity in smart farming applications.
SAGRIS back-end service is based on a data-cube technology used to handle and process a complete time-series of polarimetry SAR and MSI products and automated production of aggregated temporal (weekly or monthly) statistics for parcel-based automated crop monitoring. This approach was developed during the commercial CAPCON (2017-2018) service, using complex machine learning algorithms and large training samples. Currently SAGRIS EO Data Cube is implemented using open source GRASS GIS software platform offering industry-leading automation functions for parallel processing, raster algebra, moving window filtering and time-series analysis. However, for large scale cloud computing we are considering migration into Rasdaman data cube platform. There is a H2020 PARSEC project proposal which – in case of successful evaluation – would be dealing with deployment of SAGRIS back-end service on a cloud-based Rasdaman platform during 2019-2020.
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