Crop Monitor for AMIS
The GEOGLAM Crop Monitor  for the Agricultural Market Information System (AMIS) is a monthly bulletin on current growing conditions for the four major crops (wheat, maize,... More detail
Crop Monitor for Early Warning
The Crop Monitor for Early Warning (CM4EW) is a monthly, multi-source consensus bulletin assessing crop conditions in countries at risk for food insecurity, to anticipate... More detail
Rangelands and Pasture Productivity (RAPP)
The GEOGLAM RAPP initiative aims to improve global monitoring of rangelands and pastures, assessing their capacity to sustainably produce animal protein.   ... More detail
Asia Rice Crop Estimation and Monitoring (Asia-RiCE)
The Asia-RiCE initiative aims at improving operational rice crop monitoring and estimation using Earth observations in the Asian region.            ... More detail
Research and Development Towards Operations
The R&D component of GEOGLAM develops monitoring and reporting protocols, tools, and best practices suitable for monitoring the variety of global agricultural systems.... More detail
Earth Observation Data Acquisition and Dissemination Coordination
A close cooperation with Committee on Earth Observation (CEOS) to ensure provision of necessary satellite data for global crop monitoring, in a context of new satellites being... More detail

Sen2agri Project

Sentinel-2 for Agriculture

Scope & Background

The European Space Agency (ESA) launched the Sentinel-2 for Agriculture (Sen2Agri) project as a major contribution to the GEOGLAM initiative addressing its components related to Research & Development, Capacity Development and National Monitoring System. The project aims at demonstrating in close cooperation with users the benefit of the Sentinel-2 mission for the agriculture domain across a wide range of crops and agricultural practices. The intention is to provide the international user community with validated algorithms to derive Earth Observation products relevant for crop monitoring. These Sen2-Agri outputs consist of a suite of four validated EO products: cloud-free surface reflectance composites, dynamic cropland masks, cultivated crop type maps along with area estimate and crop status. The final objective of the project is to develop an open source processing system capable for national scale agricultural monitoring at field scale based on multi-temporal Sentinel-2 and Landsat-8 observation. The developed tools will be demonstrated and validated with the ultimate goal to transfer them to national mandated institutions and users.

 Sen2Agri 12TestSites v2

Caption: Twelve globally distributed test sites selected for the Sen2Agri algorithm and system design.

Major Achievements, Partnerships/Linkages, & Future Plans

  • Algorithm development and benchmarking over 12 JECAM sites (Bontemps 2015) for dynamic crop mask (Matton 2015, Valero 2015), crop type mapping (Inglada 2015) and crop status monitoring
  • Development and validation of an open source processing system capable of crop monitoring at national scale at high resolution (up to 10 meters)
  • National crop monitoring demonstrations in producers and food insecure countries (Ukraine, Czech Republic, Mali and South Africa) based on Sentinel-2 and Landsat-8 observations during the 2016 crop season
  • Capacity building, training and transfer of open source tools to national and international stakeholders as well as to the scientific community
  • Publication of algorithms and benchmarking results in peer reviewed open access journal
  • Partnerships: JECAM network, FAO, WFP, IFAD, CIGAR (ICIRSAT), JRC

 Sen2Agr 2ndUserWorkshop Bxl Nov2015 v2

Caption: The second Sentinel-2 for Agriculture User Workshop hosted by the European Commission, DG-Grow in Brussels, November 2015

Leadership (Point of Contact)

  • Project Leader: Pierre DEFOURNY - UCL, Belgium, This email address is being protected from spambots. You need JavaScript enabled to view it.
  • ESA Technical Officer: Benjamin KOETZ – European Space Agency, This email address is being protected from spambots. You need JavaScript enabled to view it.



- Bontemps, S.; Arias, M.; Cara, C.; Dedieu, G.; Guzzonato, E.; Hagolle, O.; Inglada, J.; Matton, N.; Morin, D.; Popescu, R.; Rabaute, T.; Savinaud, M.; Sepulcre, G.; Valero, S.; Ahmad, I.; Bégué, A.; Wu, B.; de Abelleyra, D.; Diarra, A.; Dupuy, S.; French, A.; ul Hassan Akhtar, I.; Kussul, N.; Lebourgeois, V.; Le Page, M.; Newby, T.; Savin, I.; Verón, S.R.; Koetz, B.; Defourny, P. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sens. 2015, 7, 16062-16090.

- Inglada, J.; Arias, M.; Tardy, B.; Hagolle, O.; Valero, S.; Morin, D.; Dedieu, G.; Sepulcre, G.; Bontemps, S.; Defourny, P.; Koetz, B. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sens. 2015, 7, 12356-12379.

- Matton, N.; Canto, G.S.; Waldner, F.; Valero, S.; Morin, D.; Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sens. 2015, 7, 13208-13232.

- Valero, S.; Morin, D.; Inglada, J.; Sepulcre, G.; Arias, M.; Hagolle, O.; Dedieu, G.; Bontemps, S.; Defourny, P.; Koetz, B. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens. 2016, 8, 55.



SIGMA Project

Stimulating Innovation for Global Monitoring of Agriculture (SIGMA)

SIGMA is part of Europe’s contribution to GEOGLAM, actively networking expert organizations world-wide, in a common effort to enhance current remote sensing based agricultural monitoring techniques. SIGMA is financed through the EC’s Research Framework programme (FP7), is led by VITO, and actively participates in the JECAM Research & Development Network.

SIGMA has three main objectives:

  • Identify, map and assess agriculture and crop land changes, globally, regionally and locally
  • Identify, map and assess changes in agricultural production levels and shifts in cultivation practises
  • Identify, map and analyze environmental impacts of Agriculture and cultivation practices

SIGMA implements these activities at local to regional to global scales with partners around the world (Table 1), by establishing operational test site networks in Europe, Russia, Ukraine, China, Vietnam, Africa, Argentina and Brazil. SIGMA further has a number of activities geared toward developing capacity for agricultural monitoring, and regularly carries out training activities and stakeholder workshops.

 SIGMA Project Partners

Table 1. SIGMA Project Partners

SIGMA’s research activities will strengthen international agricultural risk management capacity. Developed methods will significantly increase scientific knowledge and understanding of agricultural dynamics in relation to the environment and produce tangible products:

  • cropland maps and statistics, identifying potential for expansion
  • maps of agricultural systems, shifts in cultivation practices and crop yield gaps, identifying potential for intensification
  • assessments of impact of agriculture on the environment, both due to intensification as expansion
  • training sessions, modules and materials on remote sensing based agricultural monitoring

Already, based upon the cross site experiments and other research task, SIGMA has had multiple successful outputs, including an agro-environmental stratification at different levels (Figure 1), a priority map for landcover classification, a unified cropland mask at 250m an online time-series viewer (, developed by VITO), an online satellite-based service for vegetation monitoring (VEGA-PRO, developed by IKI), an online validation tool (, developed by IIASA).


 SIGMA GAES AgroEnvir Strat

Figure 1. An example SIGMA output – Global Agro-Environmental Stratification (GAES) - Level 4


Leadership (Point of Contact)

  • Sven Gilliams (VITO) – This email address is being protected from spambots. You need JavaScript enabled to view it.

Projet STARS

Projet STARS : Spurring a Transformation for Agriculture through Remote Sensing