A downstream service to support agro production, planning and policy. FP7 SPACE CALL Contract N :

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1 A downstream service to support agro production, planning and policy FP7 SPACE CALL Contract N : Project coordinator Mirco Boschetti CNR IREA

2 PROJECT FRAMEWORK Who is ERMES (H)ermes (/ˈhɜrmiːz/; Greek: Ἑρμῆς) Olympian god in Greek mythology, son of Zeus and the Pleiad Maia. Hermes is a god of transitions and boundaries. He is quick and cunning, and as emissary and messenger of the gods. He is protector and patron of travelers,.poets, athletics and sports, invention and trade. In the Roman adaptation of the Greek pantheon.. is the patron of commerce. 7/1/2014 2

3 PROJECT FRAMEWORK Why ERMES: provide information to agro sector FP7 SPACE ERMES aims to develop a prototype of downstream service dedicated to rice sector based on assimilation of EO and in situ data within crop yield modelling. The objective of this service, targeted to European needs, is to: contribute to the regional authorities in the implementation of agro environmental policies; provide independent reliable information to the agro business sector. support farming activities for sustainable management practices; The long term goal is to extend and adapt the service to Asian and African markets, in order to boost European competitiveness and contribute to a sustainable development. 01/07/2014 3

4 PROJECT FRAMEWORK Who we are: project team 4

5 PROJECT FRAMEWORK Where we work (at the moment) Study area b Whom we would like to serve: ERMES user Regulatory Board of Denominación de Origen Arroz de Valencia (C.R.D.O.) DG Agricoltura Regione Lombardia (RL) ENTE RISI (ER) Cereal Institute of the Hellenic Agricultural Organisation (DEMETER) Agricultural cooperative Chalasytra B Thessaloniki Allianz Re KANAKAS BROS Ltd 5

6 1- SCIENTIFIC AND TECHNICAL ASPECT FP7-SPACE CALL - Contract N : REA Project officer Virginia Puzzolo

7 Innovative approach Provide (receive) customized (ground) information to (from) different END USERS, and disseminate it by SMART technologies (web 2.0) Smart app. Geoportal Synergic use of SAR and Optical data, from existing EO satellites and forthcoming ESA Sentinel missions, to derive specific products Satellite data Develop value added Agro information by assimilating i) satellite observations, ii) in situ measurements and iii) Copernicus core services in crop models WARM model 7

8 Traditional approach for modeling Models represent a powerful tool to estimate crop condition and yield. Simulation performance depends on availability of reliable external info Crop layer Soil map Management network CROP MODEL In situ data EO data/db Processing Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Output map Crop Season 01/07/2014 8

9 Traditional approach for modeling Models can be used for regional crop monitoring Spatio/temporal reliability of output relies on input quality Not easy to know at wide scale (regional/national) Crop layer Soil map Management network Not always at the necessary granularity CROP MODEL Often the available data are not adequate in term of spatial variability In situ data EO data/db Processing Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Output map Crop Season 01/07/2014 9

10 ERMES contribution: dynamic crop mapping High resolution EO data (SAR and Optics) can be used to identify crop distribution Multitemp SAR images Crop extent EP_R1 Soil map Management network CROP MODEL In situ data EO data/db Processing Output map Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Crop Season 01/07/

11 ERMES contribution: meteorological variables Geostationary satellite sensors can be used to provide spatiallisation of meteo variable Multitemp SAR images Crop extent EP_R1 Soil map Management network Geostat. Sat. images EP_R4 CROP MODEL In situ data EO data/db Processing Output map Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Crop Season 01/07/

12 ERMES contribution: agro practices and crop info Time series of EO data can be used to identify crop management and phenology Multitemp SAR images Crop extent EP_R1 Soil map Phenology EP_R2 Multitemp Opt images network GMES Biopar Geostat. Sat. images EP_R4 CROP MODEL Bio physical map EP_R3 In situ data EO data/db Processing Output map Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Crop Season 01/07/

13 ERMES contribution: in field information Exploit field information provided by farmers/operator using ad hoc smart technology tools Multitemp SAR images Crop extent EP_R1 Soil map Phenology EP_R2 Multitemp Opt images network GMES Biopar Geostat. Sat. images EP_R4 CROP MODEL Bio physical map EP_R3 In field data In situ data EO data/db Processing Output map Crop monitoring EI_R1 Yield forecast EI_R2 Risk alert EI_R3 Yield estimation at end of season EI_R4 Crop Season 01/07/

14 Data information service scheme ERMES INPUT ERMES SERVICE ERMES TARGET SECTOR FINAL USER smart Ground traditional EO product + Crop model + Smart technology Public authority Private Sector Farmers EO operational product and data Service info In situ info User Field operator 14

15 ERMES objectives Provide a system to monitor spatial variability of rice production at regional (district) and local scale 01/07/

16 Services description Two services are foreseen: Regional Rice Service (RRS) to provide to public authorities a customized agro monitoring system devoted to regional yield estimates and risk/damage alarming. Digital maps and bulletin will be disseminated via web through INSPIRE compliant geo portal; Local Rice Service (LRS) to provide to the private sector (agroservices and insurance companies) high level information on yield variability, risk alert and crop damage assessment at farm scale. Advanced smart technologies will be used to receive in situ observations from the user, to be ingested in crop model, and to disseminate customised information to them. 16

17 Solutions for regional application Different remote sensing products will be used to derive input data for the model EO product as model Input CROP MODEL Yield estimation at end of season x Crop area Sowing date Total production Model LAI vs satellite LAI Model Pheno vs satellite Pheno EO product for model recalibration LAI Phenology 01/07/

18 Solutions for regional application End of Season Yeild Estimation results 01/07/

19 ERMES for regional monitoring (RRS) to be tuned by end user Service Code Geo information Delivery time Spatial Resolution Added value EO products Delivery time Spatial Resolution EI_R1 Crop monitoring*, ** Apr Oct. bi monthly Simulation unit/nut3*** Rice crop map* (EP_R1) October 100 m RRS EI_R2 Yield forecast** Jul Sept. 2 bulletins Simulation unit/nut3 Phenology * (EP_R2) October m EI_R3 EI_R4 Risk alert (biotic abiotic)** Yield estimation and grain quality** In case Oct. 1 bulletins Simulation unit/nut3 Simulation unit/nut3 variable* (EP_R4) Daily 1 3 km * directly from EO data processing ** from crop modelling assimilating ERMES added value product *** NUTS: Nomenclature of Territorial Units for Statistics ERMES information (EI)*& ERMES EO product (EP) 19

20 RRS: satellite crop monitoring Flood index Bimomass proxy 01/07/

21 RRS: EP_R1&2 Rice mapping & phenology Apr Apr May Flooding Emergence July Sep Flowering Maturity 01/07/

22 RRS: EI_R1&3 Risk, production and quality Risk (impact): abiotic biotic + Rice spikelet sterility (cold air irruptions) High % of yield loss Low Blast disease impact Production: t/ha Quality: Thaibonnet milky white kernels Loto Amount 7/1/2014 Unit: % 22

23 Local Rice Service What Yield Variability at field scale Management support at field scale Support for pesticide application Support for fertilisation Damage estimation (insurances) How Use of RS data at higher resolution (e.g., ~10 m) Use of locally tuned crop simulation model Use of WEB tools to provide specific field level management info (variety, sowing date, treatments, ecc) Use of smart apps (by farmers) allowing: In field measurement of LAI and N content 01/07/

24 Local Rice Service EO Data HR Optical Multitemp. SAR Multitemp. Optical sat Process ing ERMES EO Products Cultivated Area Pattern Maps CROP MODEL ERMES Geo Information Yield Pattern Maps Risk Alerts Crop Damage Field Info Sowing Date Variety LAI Smart apps ERMES GEO PORTAL Distribution to End Users SMART APPS Local 24

25 ERMES for local monitoring (LRS) to be tuned by end user Service Code Geo information Delivery time Spatial Resolution Added value EO products Delivery time Spatial Resolution EP_L1 Cultivated area* July <20 m EI_L1 Yield pattern** October <20 m Soil/biomass constant patterns maps* (EP_L2) First year <20 m LRS EI_L2 Risk alert (biotic abiotic)** In case via Smart app Farm EI_L3 Crop damage*,** October. <20 m Seasonal patterns* (EP_L3) End of season <20 m * directly from EO data processing ** from crop modelling assimilating ERMES added value product *** NUTS: Nomenclature of Territorial Units for Statistics ERMES information (EI)*& ERMES EO product (EP) 25

26 LRS: EP_L2 Seasonal patterns Multi sensors monitoring April June Farmers & Agro service July August Landsat 8, OLI, Optical multispectral (30 m) 01/07/2014 Cosmo Skymed, SAR Band X (3m) multi temporal26

27 LRS: EI_L1 Yield Patterns Field data + HR images + crop model + + Farmers & Agro service AGB (g/m-2) DOY /07/

28 Interaction with farmers In field LAI estimation via smart apps, and provision of data to the crop model PocketLAI 01/07/

29 ERMES info distribution RRS: GEO portal Satellite data CROP MODEL GeoPortal data LRS: mobile apps In field observation ERMES SDI smartapp 01/07/

30 Opportunity for thesis participating to an FP7 project activities 01/07/