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Conversion to organic farming

• Relevant Sentinels:
  
  • Sentinel-2
• Relevant Indices:
  
  • Normalized Difference Vegetation Index (NDVI)
  • Leaf Area Index (LAI)
  • Bare Soil Index (BSI)
• Relevant Data Processing Techniques:
  
  • Time Series Analysis
  • Object Based Image Analysis (OBIA)
  • Data/Multi-sensor Fusion         
  • Phenological and Productivity Metrics 
  • Soil Moisture Analytics
  • Machine Learning / AI
• Relevant Markers:
  
  • Land cover marker       
  • Crop type marker
  • Bare soil marker
  • Homogeneity marker
• Relevant Future Copernicus Missions:
  
  • Copernicus Hyperspectral Imaging Mission for the Environment (CHIME)
• Typical Challenges / Minimum Monitorable Features, Variables or Activities:
  
  • Parcel Size and Shape
    
    • Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.
    • Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring.
  • Spatial Resolution Limitations
    
    • Sentinel imagery resolution too coarse for small features and detailed practices.
  • Specific Activities
    
    • Differentiating organic from conventional farming is possible only for some crop types
    • Some organic practices (e.g., input use, crop protection) are not directly observable using EO data.
    • Certain compliance aspects (e.g. monitoring use of prohibited inputs) are difficult to monitor
  • Environmental and Technical Constraints
    
    • Cloudy periods reduce data reliability.
    • Snow interrupts vegetation curves.

• Relevant Sentinels:
  
  • Sentinel-1
  • Sentinel-2
• Relevant Indices:
  
  • Normalized Difference Vegetation Index (NDVI)
• Relevant Data Processing Techniques:
  
  • Time Series Analysis
  • Object Based Image Analysis (OBIA)
  • Machine Learning / AI
• Relevant Markers:
  
  • Land cover marker       
  • Homogeneity marker
• Relevant Future Copernicus Missions:
  
  • Copernicus Hyperspectral Imaging Mission for the Environment (CHIME)
  • Copernicus Radar Observation System for Europe at L-band (ROSE-L)
• Typical Challenges / Minimum Monitorable Features, Variables or Activities:
  
  • Parcel Size and Shape
    
    • Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.
    • Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring.
    • Difficulty monitoring biodiversity measures due to fragmented/irregular parcels.
  • Vegetation and Crop Complexity
    
    • Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.
  • Spatial Resolution Limitations
    
    • Sentinel imagery resolution too coarse for small features and detailed practices.
  • Specific Activities
    
    • Absence of pesticide use difficult to monitor.
    • Species composition and biodiversity value difficult to monitor directly.
  • Environmental and Technical Constraints
    
    • Cloudy periods reduce data reliability.
    • Snow interrupts vegetation curves.

• Relevant Sentinels:
  
  • Sentinel-2
• Relevant Indices:
  
  • Normalized Difference Vegetation Index (NDVI)
  • Leaf Area Index (LAI)
  • Bare Soil Index (BSI)
• Relevant Data Processing Techniques:
  
  • Time Series Analysis
  • Phenological and Productivity Metrics
  • Machine Learning / AI
• Relevant Markers:
  
  • Land cover marker
  • Crop type marker
  • Bare soil marker
  • Harvesting marker
  • Homogeneity marker
• Relevant Future Copernicus Missions:
  
  • Copernicus Hyperspectral Imaging Mission for the Environment (CHIME)
  • Copernicus Radar Observation System for Europe at L-band (ROSE-L)
• Typical Challenges / Minimum Monitorable Features, Variables or Activities:
  
  • Parcel Size and Shape
    
    • Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.
    • Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring.
  • Vegetation and Crop Complexity
    
    • Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.
    • Difficult to detect vegetation cover in permanent crops.
  • Spatial Resolution Limitations
    
    • Sentinel imagery resolution too coarse for small features and detailed practices.
    • Monitoring of grazing and manure application can be difficult.
    • Localized agrotechnical processes difficult to monitor
  • Specific Activities
    
    • Mixed cropping creates complex spectral signals, making reliable crop identification and monitoring difficult.
    • Distinguishing between cover crop species and natural vegetation is difficult.
  • Environmental and Technical Constraints
    
    • Cloudy periods reduce data reliability.
    • Snow interrupts vegetation curves.
    • Soil monitoring limitations in case of permanent crops.

• Relevant Sentinels:
  
  • Sentinel-1
  • Sentinel-2
• Relevant Indices:
  
  • Normalized Difference Vegetation Index (NDVI)
  • Bare Soil Index (BSI)
• Relevant Data Processing Techniques:
  
  • Time Series Analysis
  • Object Based Image Analysis (OBIA)
  • Machine Learning / AI
• Relevant Markers:
  
  • Land cover marker       
  • Bare soil marker
  • Homogeneity marker
• Relevant Future Copernicus Missions:
  
  • Copernicus Hyperspectral Imaging Mission for the Environment (CHIME)
  • Copernicus Radar Observation System for Europe at L-band (ROSE-L)
• Typical Challenges / Minimum Monitorable Features, Variables or Activities:
  
  • Parcel Size and Shape
    
    • Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.
    • Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring.
  • Vegetation and Crop Complexity
    
    • Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.
  • Spatial Resolution Limitations
    
    • Difficulty in monitoring smaller landscape features (isolated trees, hedges, or wooden strips)
  • Environmental and Technical Constraints
    
    • Cloudy periods reduce data reliability.
    • Snow interrupts vegetation curves.

Crop rotation with leguminous crops • Relevant Sentinels: o Sentinel-2 • Relevant Indices: o Normalized Difference Vegetation Index (NDVI) o Leaf Area Index (LAI) o Bare Soil Index (BSI) • Relevant Data Processing Techniques: o Time Series Analysis o Phenological and Productivity Metrics o Machine Learning / AI • Relevant Markers: o Land cover marker o Crop type marker o Bare soil marker o Harvesting marker o Homogeneity marker • Relevant Future Copernicus Missions: o Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) o Copernicus Radar Observation System for Europe at L-band (ROSE-L) • Typical Challenges / Minimum Monitorable Features, Variables or Activities: o Parcel Size and Shape  Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.  Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring. o Vegetation and Crop Complexity  Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.  Difficult to detect vegetation cover in permanent crops. o Spatial Resolution Limitations  Sentinel imagery resolution too coarse for small features and detailed practices.  Monitoring of grazing and manure application can be difficult.  Localized agrotechnical processes difficult to monitor o Specific Activities  Mixed cropping creates complex spectral signals, making reliable crop identification and monitoring difficult.  Distinguishing between cover crop species and natural vegetation is difficult. o Environmental and Technical Constraints  Cloudy periods reduce data reliability.  Snow interrupts vegetation curves.  Soil monitoring limitations in case of permanent crops.

Mixed cropping - multi cropping • Relevant Sentinels: o Sentinel-2 • Relevant Indices: o Normalized Difference Vegetation Index (NDVI) o Leaf Area Index (LAI) o Bare Soil Index (BSI) • Relevant Data Processing Techniques: o Time Series Analysis o Phenological and Productivity Metrics o Machine Learning / AI • Relevant Markers: o Land cover marker o Crop type marker o Bare soil marker o Harvesting marker o Homogeneity marker • Relevant Future Copernicus Missions: o Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) o Copernicus Radar Observation System for Europe at L-band (ROSE-L) • Typical Challenges / Minimum Monitorable Features, Variables or Activities: o Parcel Size and Shape  Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.  Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring. o Vegetation and Crop Complexity  Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.  Difficult to detect vegetation cover in permanent crops. o Spatial Resolution Limitations  Sentinel imagery resolution too coarse for small features and detailed practices.  Monitoring of grazing and manure application can be difficult.  Localized agrotechnical processes difficult to monitor o Specific Activities  Mixed cropping creates complex spectral signals, making reliable crop identification and monitoring difficult.  Distinguishing between cover crop species and natural vegetation is difficult. o Environmental and Technical Constraints  Cloudy periods reduce data reliability.  Snow interrupts vegetation curves.  Soil monitoring limitations in case of permanent crops.

Winter soil cover and catch crops above conditionality • Relevant Sentinels: o Sentinel-2 o Sentinel-3 • Relevant Indices: o Normalized Difference Vegetation Index (NDVI) o Bare Soil Index (BSI) o Moisture Stress Index (MSI) o Normalized Difference Moisture Index (NDMI) • Relevant Data Processing Techniques: o Time Series Analysis o Soil Moisture Analytics o Machine Learning / AI • Relevant Markers: o Land cover marker o Crop type marker o Bare soil marker o Homogeneity marker • Relevant Future Copernicus Missions: o Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) o Copernicus Radar Observation System for Europe at L-band (ROSE-L) • Typical Challenges / Minimum Monitorable Features, Variables or Activities: o Parcel Size and Shape  Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.  Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring. o Vegetation and Crop Complexity  Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.  Difficult to detect vegetation cover in permanent crops. o Spatial Resolution Limitations  Sentinel imagery resolution too coarse for small features and detailed practices.  Monitoring of grazing and manure application can be difficult.  Localized agrotechnical processes difficult to monitor o Specific Activities  Mixed cropping creates complex spectral signals, making reliable crop identification and monitoring difficult.  Distinguishing between cover crop species and natural vegetation is difficult. o Environmental and Technical Constraints  Cloudy periods reduce data reliability.  Snow interrupts vegetation curves.  Soil monitoring limitations in case of permanent crops.

Improved rice cultivation to decrease methane emissions (e.g. alternate wet and dry techniques) • Relevant Sentinels: o Sentinel-1 o Sentinel-2 • Relevant Indices: o Normalized Difference Vegetation Index (NDVI) o Leaf Area Index (LAI) o Bare Soil Index (BSI) • Relevant Data Processing Techniques: o Time Series Analysis o Phenological and Productivity Metrics o Soil Moisture Analytics o Machine Learning / AI • Relevant Markers: o Land cover marker o Crop type marker o Bare soil marker o Homogeneity marker • Relevant Future Copernicus Missions: o Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) o Copernicus Radar Observation System for Europe at L-band (ROSE-L) • Typical Challenges / Minimum Monitorable Features, Variables or Activities: o Parcel Size and Shape  Small, narrow, fragmented, and irregular parcels (<0.2 ha) are hard to detect.  Boundary detection near forests problematic, hedges, or wooden strips complicate monitoring. o Vegetation and Crop Complexity  Complex/mixed vegetation, herb-rich grasslands, and buffer strips give inconsistent signals.  Difficult to detect vegetation cover in permanent crops. o Spatial Resolution Limitations  Sentinel imagery resolution too coarse for small features and detailed practices.  Monitoring of grazing and manure application can be difficult.  Localized agrotechnical processes difficult to monitor o Specific Activities  Mixed cropping creates complex spectral signals, making reliable crop identification and monitoring difficult.  Distinguishing between cover crop species and natural vegetation is difficult. o Environmental and Technical Constraints  Cloudy periods reduce data reliability.  Snow interrupts vegetation curves.  Soil monitoring limitations in case of permanent crops.