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Highlights and recommendations

In relation to the drones for mapping:

Complementarity of small drones for high resolution mapping and flexible data acquisition

Exploit benefits of drone sampling and explore drone products for validation of existing EO products

Support new EO satellite missions

Risk of fragmentation of different drone products and infrastructure so needs sharing platforms

In relation to the integration of in situ photographs and remote sensing:

Maintain the temporal stability of in situ products like LUCAS

Streetview as a source of information for land cover mapping has incredible potential that has yet to be exploited

In relation to hybrid cropland mapping integrating existing products:

Gain a better understanding of the influence of the quality of the in situ data on the final classification cost compared to the processing costs to understand if more should be invested in good quality situ data

Data integration can be best done if data are FAIR and free, ideally cloud optimized and catalogued on the Open EO platform to facilitate seamless workflows

Data comparison, data integration and in situ data should get the necessary attention since crop type mapping can only be done with sufficient, high-quality ground data

More research is needed in optimizing training and validation collection to better understand how costs can be reduced, e.g., can costs be reduced while maintaining a minimum level of statistical robustness

An operational service (beyond mandate of ESA) for crop type mapping (major global crops) is needed

In relation to integration of administrative data and LLMs for matching and translation:

Provide better access to administrative data (in this case GSA) as gated-access is prevalent

Provide more computing resources for using LLMs for tasks like automated matching and translation

Provide more funding for this type of automated matching and translation so data can be compared

Other recommendations/comments

Standardize data such as the GSA and ask member states to publish them according to an international standard like the international species register or automate this process afterwards

While we require data from farmers, it is not mandatory for member states to provide these data in a harmonized way – why is this is this the case? What can be done to improve this?

Funders should put much less stress on the need to reach a minimum level of accuracy and instead stress common terminology (i.e., common classes) that are comparable across products even if this means that some individual classes may have higher commission and omission errors

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analysis of agriculture productivity of crops using ML and process-based models to create soil and farm management maps but also to predict future climatic scenarios;

crop seasonality assessments of maize; calculations were carried out including variables such as rainfall/sun related to the growing season to perform better crop yield estimation and identification of optimal planting;

winter wheat production and migration cropland to analyze the impact of climate change to better plan and manage production;

in Senegal rice represents a major nutritional crop; the goals of the project were to increase crop fields and their intensity while maintaining an effective irrigation network;

the Vietsco project aimed at improving food security in the Mekong delta; stressors on rice farming due to climate change and human pressures were analysed and using in situ and satellite data for model-based observation; outcomes have been the adaptation of the crop calendar; reduction of cropping density; conversion to other land use; decrease of methane emissions.

Recommendations

improve temporal resolution of EO mission;

analysis of climate change impact on agriculture require high resolution climatic data with large area coverage;

winter and summer crops needs to be separated in geospatial products;

there is the need of continuous integration of EO data and ML models in intervention and aid strategy;

there is the need of an enhanced integration of EU with regional data and data from agriculture organisations along with coordinated reporting of yield data;

satellite data should be adapted to tropical regionals;

more investment is needed on ground data and IoT;

there is the need of integrated observations of impact of multiple stressors;

inclusion of socio-economic data in climate change analysis is also relevant;

it is important to invest in better user uptake of EO applications, services, products by co-developing solutions in partnership with relevant (local) stakeholders also in testing phases.

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Contents and Hightlights:

The session addresses multisource data for multi stressors (p1), and for drought (p2, p3, p4) , and pests (p5 , p6)

Multiple stress is addressed separately for different sites/crops and conditions,

Some applications are very local, and when global scale is addressed, the approaches still remain qualitative

Mismatchs beween scale of EO products and reporting outcome in spatial maps need additional consolidation (statistical sampling)

Most contributions use statistical regression approaches, although some attempts to establish physical-based  models (e.g. for soil moisture in Africa)

Quantification still a challenge

Most works are still in R&D, involvement of stakeholders needed to move to operations

Recommendations

guarantee the access to contextual data (facilitate the access to ancillary data in the public domain

make available the link to sensitive data (e.g. socio-economic/ vulnerability data in Europe, and globally, particularly in developing countries )

Involve stakeholders in the whole process, from the definition of the objective until the validation of the results, and in the follow_on exploitation of the results

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Panel: PRISMA, EnMAP and ECOSTRESS experiences

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Panel: PRISMA, EnMAP and ECOSTRESS experiences

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•Panel members presented shortly the missions and instruments.

•The importance of PRISMA, EnMAP and ECOSTRESS in agriculture applications was clearly stated - data provided by these missions can serve to enhance currently existing products such as LAI or explore new products in applications such as fertilization, irrigation, soil organic content estimation etc.

•Panellists emphasized the importance of PRISMA, EnMAP and ECOSTRESS, which are experimental missions in the preparation of operational missions e.g. CHIME.

Recommendations

•use more PRISMA, EnMAP and ECOSTRESS data in the research in order to develop new algorithms – important in the view of new operational missions

•Large volumes of data require very demanding computation, new solutions in IT needed

•Relation between physical models and AI models have to be better understood

•More frequent observations needed

•New solutions to deal with clouds shall be studied - solutions from multispectral don’t work well.

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Monitoring irrigation dynamics (mapping and quantification) through multi-source satellite data is feasible, but room for improvement is large; the lack of-situ reference data is one of the major challenges to be faced

Many methods relying on several EO products do exist. Should they be standardized or at least made comparable?

Satellite data offer the possibility of monitoring irrigation impacts on freshwater availability, which is crucial especially over regions already facing water scarcity

Satellite data as a tool for assessing benefits or irrigation modernization

Several presentations focused over northern Africa: satellite data are an essential tool for environmental studies over scarcely monitored areas

Recommendations

LST and SM at higher resolution

Integrate information on agricultural practices in in-situ data sets

Future missions for reducing the spatiotemporal mismatch between satellite data and irrigation dynamics

Projects fostering the collection of high-quality in-situ data

Long time needed to adopt digital services at water authorities

Integration of WaPOR data into the Copernicus data space ecosystem

Machine learning VS process-based approaches: should the relative communities be connected?

Comparison of the many methods currently available for irrigation monitoring is needed

Consider operational aspects of ET algorithms (PT-JPL)

Irrigation monitoring not only in terms of extent, but also timing and quantification

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GEOGLAM: bringing together the community is the priority! Harmonize the dataset and make them FAIR.

Copernicus access to in-situ data: without in situ Copernicus cannot provide valid data, products and services.

EEA has cross-cutting coordinator role.

CORDA – access to origin of the data by default: LPS data; GSAA data

NASA HARVEST: Application areas: in situ is critical for all there applications.

How do we collect ground data? Partnership with the Ministries and with private sector, tremendous number of data exists; visual annotations, citizen science.

Green triangle: end to end crop insurance claims management with real-time EO based damage assessment and yield estimation.

IIASA is known as the powerhouse for global integrated modelling, mitigation and adaptation scenarios, population projections.

World Cereal project – most of the data are open and share. EO data will be place in the STAC catalogue – cloud optimized

Recommendations

Making the best use of in-situ data: quality assessment – part of data collection process.

Designing new protocols to collect crop type, surface and yield data

Increase in available data, AI, cloud processing and analysis libraries there is a growing need for the valid in situ data.

Reliable yield data are even more challenging than information on crop types and area) still the guidelines are needed

Involving national stakeholders is crucial for sustainability and EO endorsement

For crop type mapping the ground visits and critical, new opportunities for crowdsourcing

Short term survey projects often entail high logistical challenges.

Legal framework for data collection on the ground is often unclear, with access to existing data which is limited

Push more for the open data, transparency and interoperability.

All the ESA project must have a clear cause to make the in-situ data available. This will be a game changer. All the reference data must become available and the validation as well.

With ML data available, more data we have on crops the better the model will become.

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Session 5 Crop Yield estimation and Forecasting

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Session 5 Crop Yield estimation and Forecasting

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Rice and wheat yield modeling in the Nile Delta with the fusion of Sentinel-1 + Sentinel-2 data

GDD allows the model transferability from Spain to Egypt and minimizes the seasonal time shifts.

Good agreement for both rice model and wheat model on governorate level however both of the models show a systematic overestimation. S2 resolution is in the limit for smallholdings of Egypt

Combining Sentinel-1 and 2 data with machine learning to improve field-scale crop yield forecasting

Using ML for the crop yield forecasting using Sentinel-1 and Sentinel-2 data. S1 better for long term forecast (3-4 months). S2 for short term forecast (1-2 months).S1 and S2 are always better.

Estimation of wheat yield at field scale using Sentinel-1 and Sentinel-2

model to forecast wheat yield at field level from Sentinel-1 (S1) and Sentinel-2 (S2) data.

SWIR data shows promising results on wheat yield monitoring.

S1 challenging without S1B for the yield estimation

Combining Earth observation with digital tools to get accurate and timely in-season crop data to support decision making.

applications for in-situ data collection to collect data and build evidence base on cropping practices, pest damage and combine with EO data

important to understand end-user & co-develop all tools and methods

Resilience under Extreme Circumstances – Harvest and Yield Estimation for Ukraine 2023

harvest activity monitoring both reveal the same picture:
6.5% of the agricultural area of Ukraine (Crimea excluded) could not be used for agricultural production in 2023 along the frontline.

Predicting in-season crop yield within fields: A Sentinel-2 time series based monitoring approach

GDD-based VIs time series enabled forecasting of winter cereals yield before of harvest.

Reliable and accurate yield forecasts were obtained around the peak of the growing season (flowering stage).

MLRA models showed a consistent increase in forecasting accuracy along with the crop progressive phenology development.

Recommendations

Crop yield data are clear limiting factor for yield estimation, There shall be one solution for data distribution and common validation strategies

Peer reviewed public datasets needed - too much time spent on collecting and understanding quality of data

S2 cloud mask getting better but still need to filter data.

S1-S2 Increase temporal availability of data

Decrease systematic undersampling due to orbit constraints

Use process-based crop models for yield prediction

Important availability of field data

Standardize the accuracy/uncertainty of final products

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Highlights

Improvement of nitrogen efficiency highlighted as a central pain point for farmers and for environment (GHG emission)

Physical models necessary for impact assessment especially in a changing climate.

EO Market segment for climate, environment and biodiversity highest (28%). Blockchain can serve this market segment, as well as food & agriculture and the insurance & finance segment.

CAP controlling became a very mature service using multi-sensoral approaches and open value chain.

Data as a Service and visualisation by widget-based apps enables Paying Agencies to get fast access and information on the parcel level.

Landscape features have important effects on farmland biodiversity, but the potential to use EO data for it has not been fully exploited it is challenging to map the different type of landscape features solely based on earth observation data

Impact of a agricultural treatment / cultivation practices on the environment can be assessed using ML

Recommendations

Providing farmers with information to support decision making should be the final goal a EO based services.

Integrating EO data and physical models is essential

Open-source farm reference data sets needed

For the Landscape Features we shall work with high resolution remote sensed product equivalent to European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL)

Joining efforts of biodiversity monitoring and landscape features monitoring for a more complete harmonized survey and a better landscape features-biodiversity assessment

Use causal machine learning for efficiently uplifting green metrics and personalizing sustainable agriculture

We have to differentiate between corelations and causal effects, overfitting of ML is a further risk.

ML have no predictive capabilities, process models are required here. Hybrid approaches should be further researched.

Monitoring of industrial plantations should be considered in the framework of the EUDR

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Satellite-based services for smallholder food producers in support of food security, A dream or a reality?

>4 million smallholders reached, about 1.2 million users, decreased use of inputs, increased production, higher incomes for farmers

Satellite-based germination insurance for smallholder farmers in Africa

Parameters developed to set up germination insurance in Ghana, farmers consulted, seed companies onboarded, germination insurance considered relevant, combination with yield insurance interesting

Development of a farm-scale water accounting model incorporating farmers behavior and remote sensed data

Success in estimating overextraction, development of groundwater management tool and farm-scale water balance; better estimation of Kc using remote sensing

Near-real time monitoring of irrigation water use per farm by combining satellite and in-situ data with hydrological models

Successful irrigated area mapping and irrigation water use estimates services developed in Australia making use of hydrologically similar pixels and incremental ET; paying client and opportunities for scaling up

Retrieving crop phenology at field scale in the Nile Delta using Sen2Like processor and PlanetScope imagery

No significant improvement of Sen2Like compared to S2; good agreement Sen2Like and PlanetScope; opportunities to study climate shift effect on crop phenology and support agricultural practices of smallholders

Mitigating food security challenges in Afghanistan: A geospatial and remote sensing approach

RS and field data for wheat monitoring (NDVI) and water management interventions; flood analysis with optical, radar, GloFAS (NDWI); analysis of trenches for water harvesting; future use of WaPOR to improve this

Recommendations

Allow for the fact that innovation and scaling takes time, especially in developing countries

Bundling of services to smallholder farmers provides benefits

Education and trust remain crucial success factors, when working with (smallholder) farmers

Better soil data needed for good fertilizer advice

Develop services and service delivery in a flexible way that allows for adaption and makes scaling up feasible: end users may have different requirements and local conditions, laws & regulations and ways of doing business may vary

Find ways to establish and maintain in situ data infrastructures for weather, water, environment and climate and mechanisms to promote and facilitate open and free data sharing

Satellite-based service implementation should take into account collaboration between research and industry, clearly communicate with end-users, considering their internal decision-making processes and be ready to integrate future EO missions

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Highlights

Using experimental hyperspectral data (PRISMA & EnMap) for new applications: plant water monitoring, estimating and predicting crop nutrients

Multi-year GSA (GeoSpatial Application, parcel declarations) hold unprecedented reference data enabling innovative method development: adding crop rotation to improve crop mapping accuracy and develop new in-season crop mapping approaches

WaPOR Accounter as an example dashboard that bridges the gap between data products and end-users

NRT harvest monitoring in Ukraine using Planet data and unsupervised clustering methods to reach a high accuracy (90% TBC)

Recommendations

OPEN in situ as a priority!

GSA (GeoSpatial Application, parcel declarations) extremely useful; we need more countries to open this up: push politically

Define larger test areas (commercial farms) where we can have open data, going beyond just crop types (incl. farm management). Build partnerships outside Europe, play a leading role

Push for a larger budget share going to in situ data collection and sharing, the single largest bottleneck for major progress

RS data processing and sensors design

We need more focused efforts on standardized preprocessing workflows resulting from sensitivity analyses (benchmarking the preprocessing)

Standardized data access (STAC + COG)

Focus on upcoming SWIR bands

Fusion of multispectral and hyperspectral data

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Overview of institutional & commercial new and planned missions relevant for agriculture: CHIME, ROSE-L, LSTM, Sentinel 1&2 Next Generation, ConstellR thermal mission, PRISMA hyperspectral mission, NISAR SAR mission and HydroSAT thermal mission

Recommendations:

• Create synergy between institutional and commercial missions

• Work on data policy, making the data available to wider audience (including non EO data)

• Develop applications making use of complementarities between institutions missions and new space

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Panel: Copernicus uptake for EU Agricultural policies

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Panel: Copernicus uptake for EU Agricultural policies

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Topics discussed:

DEFIS’ ambitions engaging policy users for Copernicus uptake

Current use of EO in respective policy areas

Applications integrating EO and geospatial data

Can EO support policy coherence?

There are still some opportunities given the increasing operalisation of the Copernicus products. Continuity of the data provision is really important for EC.

EO can still increase the understanding of the situation on the ground – for better monitoring, more evidence based results and ways to reduce the administrative burden.

In the future DG AGRI plans to further strengthen the collaboration with the DG DEFIS, JRC and ESTAT and DGs and other agencies.

There is still a lotto be done in terms of the uptake of EO, environmental and other geospatial data by practitioners.

Data integration: some activities are not monitorable with EO. Sometimes the integration of data is needed (e.g. geotagged photos by farmer). Opportunity would be to see if EO data could delineate and measure the agricultural parcels which is still a challenge.

Intra annual variability, timeliness of the product is something that we should work still on.

Recommendations

To achieve policy coherence we need to have a common base for the analysis. There is a potential of integrating the data bases, maintained by AGRI, JRC,ESTAT, EEA, DEFIS, ESA or elsewhere.

We need to have unique point of contact, while requesting the same kind of the imagery by different parts of the Commission.

There is still an interoperability issue, not only technical but also the legal, organizational and governance level – organize workshop to join forces on that respect?

Resources are needed to have the possibility to use the information from EO in a way that is understandable and relevant for the policy, particularly when it comes to biodiversity or emissions calculations.

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The FAO Panel highlighted the use of Earth Observation (EO) by the FAO to solve real-world agricultural problems, emphasizing the user perspective and various applications. Livia Peiser (Senior and Land Water Officer, FAO) discussed monitoring agricultural resources using EO for land and water information systems, with initiatives like WaPOR for water productivity and land cover mapping. Lorenzo De Simone (Technical Adviser Geospatial, FAO) elaborated on the EOSTAT initiative's work on crop type mapping and yield forecasting, stressing the importance of engaging national statistics offices in co-designing and implementing pilot projects. Anne Branthomme (Forestry Officer, FAO) focused on using EO to understand and combat deforestation, particularly the significant impact of small-scale farming, and highlighted the need for improved tools and local expert involvement in EO modeling. Neil Marsland (Senior Technical Officer, FAO) presented on EO's role in assessing the agricultural impacts of war in Ukraine, addressing challenges such as data confidentiality, the urgency of analysis in conflict zones, and the necessity of updating outdated irrigation maps.

Recommendations

Strengthen partnerships and ensure the availability of open data, methodologies, and tools to support agricultural monitoring and decision-making.

Involve local experts and national statistics offices in the design and implementation of EO projects to ensure relevance and accuracy.

Foster climate engagement among different stakeholders to support sustainable forest management practices. Develop and utilize comprehensive forest monitoring systems to provide detailed and actionable information.