A comparison of global agricultural monitoring systems and current gaps

Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

GMFS Final report Stage 1 and Stage 2

Global Monitoring for Food Security (GMFS) is a Global Monitoring for Environment and Security (GMES) Service Element (GSE) project, part of the European Space Agency (ESA) contribution to the European Union (EU) /ESA GMES Programme. GMFS aims to establish an operational service for crop monitoring in support of Food Security Monitoring to serve policy makers and operational users. The GMFS project started in March 2003 as part of Stage 1 of the ESA Earthwatch GMES services Element “Service Consolidation Actions”, and was continued in October 2005 as part of the Stage 2 of the ESA Earth watch GMES services Element – “Scaling Up Consolidated GMES Services”. In this document an overview is given of the work done throughout the previous six years. GMFS aimed at monitoring crop state /vegetation condition at continental and national scale. Low resolution Earth Observation (EO) data was used for monitoring purposes at continental scale, while at national scale products were based upon medium and high resolution data, field work and agro-meteorological models. The project was guided by a project strategy group with members from the United States Agency for International Development - Famine Early Warning System Network (USAID-FEWSNET), Directorate General for Development (DG-DEV), Consultative Group on International Agricultural Research - International Wheat Improvement Center (CGIAR-CIMMYT), European Commission Joint Research Center (EC-JRC), United Nations World Food Programme (WFP) and United Nations Food and Agricultural Organisation (FAO). The goal of the project in Stage 1 (March 2003 –November 2004) was to consolidate an early warning system for food security. This started off by an intensive literature review and setting up an initial service for the Centre de Suivi Ecologique (CSE) in Dakar Senegal. In the second Phase of Stage 1 activities focussed more on the actual service delivery and setting up activities with users. Those activities included the monitoring agricultural production for Senegal, monitoring agriculture in Malawi and giving support to the Crop and Food Supply Assessment Mission (CFSAM) of FAO /WFP. Additionally, services were set up for the centre Agro-Hydro-Météorologique (AGRHYMET) as a result of a meeting between AGHRYMET and Vlaamse Instelling voor Technologisch Onderzoek (VITO). During 2005 the early warning service was continued to support GMFS users although there was at that time no formal contract to do so. At the start of the Second Stage, in October 2005, a GMFS user executive board, consisting of one representative from: EC-JRC, FAO, WFP, Southern Africa Development Community Regional Remote Sensing Unit (SADC-RRSU), Regional Centre for Mapping of Resources for Development (RCMRD) and AGRHYMET, was set up to support the consortium in defining the correct services and to review the work. Since the focus for the Second Stage was on up scaling the consolidated services, it was decided that the early warning service and support to the CFSAM were to be continued, the agricultural mapping service was to be expanded to more countries - namely, Senegal, Sudan, Ethiopia, Malawi and Zimbabwe - and extra services on yield modeling using remote sensing and agro-meteorological models were to be provided. During the second year of this stage, the services were even more extended with, support to the Ministry of Agriculture and Meteorological Department in Mozambique, extra activities in Ethiopia and Sudan and support to the regional centers on operational use of the ESA Data Dissemination System (DDS)

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

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Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: a case study for Belgium

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types are largest. Furthermore we showed that the concept of classification confidence derived from the random forest classifier provided insight in the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.status: publishe

Land cover and land use products in service of agriculture and ecosystem monitoring EuroGEO showcases

International audiencemyECOSYSTEM consists of three highly complementary pilots, developed to maximize services to user groups both in their specific topical areas, but specifically through integrating and jointly using information from remote sensing (mySPACE), insitu observation (mySITE) and high-level indicators verification and testing with an exemplary focus on biodiversity (myVARIABLE). All pilots pull together experts from the former H2020 project ECOPOTENTIAL, the emerging eLTER Research Infrastructure and GEOBON to maximise the outcome and the user community of the showcase

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations