EO Capability Benefits
Estimating crop dry matter productivity (DMP) through Earth Observation (EO) supports agricultural planning, food security monitoring, and carbon accounting. DMP quantifies the biomass accumulated by crops, excluding water content, making it a reliable indicator of plant growth and performance. This information is valuable for evaluating land productivity, forecasting yields, identifying stress conditions, and managing inputs such as fertilizers and irrigation. EO-based DMP monitoring is especially useful in areas where field measurements are limited or inconsistent, offering a scalable and repeatable alternative for data-driven agriculture.
EO Capability Description
Crop DMP is typically calculated using EO-derived vegetation indices, most commonly NDVI (Normalised Difference Vegetation Index) or fAPAR (Fraction of absorbed Photosynthetically Active Radiation), combined with models of Light Use Efficiency (LUE). Optical sensors like Sentinel-2 or Sentinel-3 provide high-temporal and medium-spatial resolution imagery suitable for monitoring seasonal biomass accumulation. By modeling the photosynthetic efficiency and incoming solar radiation, DMP is estimated in units such as kg of dry matter per hectare.
Products are generated at resolutions ranging from 10 to 500 meters, with outputs updated dekadally or monthly. These maps reveal spatial and temporal variations in crop development and allow comparisons between years, regions, or crop types. They are especially effective for tracking growth anomalies caused by drought, pests, or poor soil conditions.
While models depend on assumptions about crop type and environmental factors, calibrated EO-DMP estimates provide actionable insights for government agencies, extension services, and insurers. High-quality products should be validated using field biomass measurements and accompanied by documentation on uncertainty and methodology. DMP mapping complements other EO capabilities such as crop yield estimation and Water Use Efficiency analysis, offering a core layer in agricultural monitoring systems.
