Crops

Henk Janssen
Expert Agri

Henk Janssen
Expert Agri

Crop Data. Every field. Every growth stage.

Continuous, satellite-driven crop monitoring. From species identification and growth tracking to stress detection and phenological staging.

What we monitor

We monitor crops throughout the entire growing season. Detecting what is grown on each field, tracking how it develops week by week, and identifying where growth is deviating from expected patterns.

We use detection to establish crop presence, classification to identify species and type, and change analysis to follow phenological stages, stress events and harvest timing. The result is a continuous, field-level view of what the land is producing and how it is performing at any point in the season.

Methodology & Technology

We process multi-spectral satellite imagery (including Sentinel-2 and other high-resolution sources) through machine learning models trained to classify crop types and calculate vegetation indices at field level.

NDVI, EVI and related spectral indicators are used to quantify vegetation vigour, detect stress conditions and map phenological development throughout the growing season.

Classification accuracy depends on image resolution, revisit frequency and local crop diversity, and outputs are validated against reference datasets including the BRP (Basisregistratie Gewaspercelen) and independent field verification. The time series is continuously updated as new satellite observations become available, enabling both real-time in-season monitoring and multi-year trend analysis.

Examples of what we detect

Crop type

Crop type classifies the specific cultivated plant species or category grown on each field. From maize, potatoes and wheat to temporary grassland, agri-environmental mixtures and speciality crops. It is the foundational classification that determines which benchmarks, risk profiles, compliance frameworks and market specifications apply to each parcel.

Crop development stage (phenology)

Phenological stage tracks where each crop is in its biological growth cycle. From emergence and canopy closure through flowering, grain fill and senescence to harvest. Knowing the development stage in real time enables precise timing of agronomic interventions, more accurate yield forecasting and earlier detection of anomalies relative to typical seasonal progression.

NDVI (Normalized Difference Vegetation Index)

NDVI measures the current greenness and density of crop vegetation using the ratio of near-infrared to red light reflectance. Values above 0.4 indicate actively growing, healthy vegetation; values at or below 0.4 signal stress, senescence or the absence of crop cover. Making NDVI the standard operational indicator for in-season crop health monitoring.

EVI (Enhanced Vegetation Index)

EVI provides a more sensitive vegetation health signal than NDVI in high-biomass conditions, and is less affected by atmospheric interference, shadows and background soil reflectance. It is the preferred index for dense or vigorous crops where NDVI is known to saturate and lose discriminating power.

Crop vigour and stress signals

Vigour and stress indicators are derived from combinations of spectral indices and flag parcels where vegetation performance is below expected levels for the crop type, growth stage and location. Stress signals can reflect drought, waterlogging, disease pressure, pest damage or nutrient deficiency. Providing early warning before problems become visible to the eye or appear in yield data.

Biomass (above-ground vegetation mass)

Biomass quantifies the amount of plant material present on a parcel, derived from vegetation index values and crop-specific growth models. It is a proxy for yield potential mid-season, a key input for carbon and nitrogen cycle modelling, and a required metric for biomass-based subsidy and certification schemes.

Harvest detection

Harvest detection identifies when a parcel transitions from a growing crop to bare or stubble conditions, using change analysis between consecutive satellite observations. It enables automated tracking of harvest timing across large agricultural areas. Relevant for supply chain planning, post-harvest reporting and compliance verification.

Change signal (land use change / crop rotation)

The change signal captures shifts in crop type between seasons, flagging where rotation patterns have changed, where land use has shifted from arable to grassland or fallow, and where new crops have appeared outside expected patterns. It is a critical layer for agri-environment scheme monitoring, deforestation-linked commodity traceability and supply chain due diligence.

Output & integration

Crop data is available via API for direct integration into agricultural management platforms, GIS environments and supply chain traceability systems, and via periodic reports for organisations that require structured seasonal or annual delivery.

All outputs are provided in standard geospatial formats, linkable to BRP parcel identifiers and Cadastral references for seamless connection to existing administrative workflows.

The dataset combines directly with parcel data for field-level context and risk profiling, with water data for drought and irrigation efficiency analysis, with landscape element data for agri-environment scheme verification, and with satellite weather and soil data for yield forecasting and stress attribution.