The Spectrum of Earth Observation Applications in Agricultural Development: From Simple to Complex

How DevGlobal Leverages EO to Advance Agricultural Innovation

Earth Observation (EO) technologies have revolutionized our ability to monitor and evaluate agricultural interventions across diverse landscapes – ranging from continent-scale deforestation tracking to detecting single-gene crop improvements. This spectrum of applications reveals how EO bridges the gap between satellite data and on-the-ground impact, empowering governments, NGOs, and farmers to make data-driven decisions.

At DevGlobal, we often use EO data and tools to address challenges in global development and agriculture, helping our partners harness satellite imagery and geospatial analytics. Looking for a partner in navigating the application of Earth observation to your work? Explore the applications below and get in touch!

Understanding the EO Complexity Spectrum

EO applications in agriculture exist on a spectrum, ranging from simple, broad-scale assessments to highly complex, machine-learning-driven analyses. Understanding this spectrum allows organizations to select the right approach based on their needs, technical capacity, and resource constraints.

Landscape-Level Applications, Agroforestry and Irrigation Mapping 

At the simplest level, EO effectively monitors large-scale land use changes such as deforestation for agricultural expansion in the Amazon and across Africa. These applications rely on clear spectral differences between forest and agricultural land, making them relatively straightforward to implement with moderate-resolution imagery (10-30m) from platforms like Landsat or Sentinel-1 and 2. Platforms like Digital Earth Africa provide accessible, analysis-ready satellite data that enables governments and development practitioners to monitor and respond to environmental and agricultural transformations across the continent. DevGlobal has supported this DEA in the past, ensuring data accessibility for decision-makers tackling food security and climate adaptation.

In Action: Near Real-Time Monitoring of Tropical Forest Disturbance.

A study demonstrated the fusion of optical data from Sentinel-2 and synthetic aperture radar (SAR) data from Sentinel-1 to monitor tropical forest disturbances in near real-time. This approach capitalizes on the complementary strengths of both sensors: Sentinel-2 provides high-resolution optical imagery, while Sentinel-1 offers cloud-penetrating radar data, ensuring consistent monitoring even under frequent cloud cover. By integrating these datasets, the study achieved enhanced detection accuracy and timely identification of deforestation events, facilitating more effective conservation efforts. ​ 

Moving up in complexity, EO can distinguish agroforestry systems from conventional agriculture by detecting the distinctive signature of trees interspersed with crops. This requires higher spatial resolution (≤10m) and more sophisticated classification algorithms that can differentiate various vegetation types. 

In Action: Mapping Cocoa Plantations in Côte d’Ivoire and Ghana.

Researchers have developed methods to map cocoa plantations in West Africa by integrating data from multiple satellite sources. By combining vegetation indices from Sentinel-2 imagery (NDVI) with radar data (VH/VV ratios) from Sentinel-1, they trained a Random Forest (RF) classification model to differentiate cocoa farms from other land uses. This approach achieved 82.89% producer’s accuracy and 62.22% user’s accuracy, mapping 3.69 million hectares (Mha) of cocoa in Côte d’Ivoire and 2.15 Mha in Ghana. These methods require integrating multi-temporal satellite imagery and advanced machine learning techniques to effectively distinguish cocoa farms and assess their environmental impact.

Tracking Farm Areas Served by Irrigation 

Irrigation mapping utilizes satellite data to distinguish between irrigated and rain-fed crops by analyzing variations in vegetation indices and surface temperatures. By integrating this with evapotranspiration models, EO can provide accurate estimates of crop water use throughout the growing season. 

In Action: Monitoring Irrigation in Ethiopia’s Rift Valley

A study in Ethiopia’s Central Rift Valley used Sentinel-2 multispectral imagery combined with object-based image analysis and SPOT-6 high-resolution data to map irrigation expansion. This approach allowed researchers to detect irrigated plots with an accuracy of 85%, capturing seasonal variations in water availability. However, frequent cloud cover posed challenges, requiring the integration of precipitation datasets for improved classification.

Conservation Agriculture Practices 

Tracking regenerative agriculture practices, such as zero-tillage and residue retention, requires advanced spectral analysis to detect changes in soil reflectance and organic matter cover. Time-series satellite imagery provides the necessary historical context to monitor shifts in land management practices. 

In Action: Zero-Tillage Expansion in Zambia 

A mid-term evaluation of the Conservation Agriculture Scaling-Up (CASU) project in Zambia used historical Sentinel-2 imagery to monitor post-harvest residue cover. By setting a threshold of >30% residue retention, researchers mapped a 64% increase in zero-tillage adoption over eight years. However, distinguishing between conservation practices and naturally occurring plant residues required validation through on-ground surveys.  

Crop Type Differentiation, Intercropping Systems and Area Estimates  

At the most advanced end of the spectrum, EO can potentially differentiate between crop varieties, drought-resistant cultivars, or even single-gene-modified plants. Distinguishing crop types in mixed farming systems relies on high-frequency EO data to capture phenological differences across the growing season. Machine learning models trained on extensive ground-truth datasets further enhance classification accuracy. 

In Action: Mapping Global Crop Types with Sentinel Data 

The ESA WorldCereal project leverages Sentinel-1 and Sentinel-2 time series data, combined with PRISMA hyperspectral soil information and CatBoost machine learning models, to classify 10 major crops at a 10-meter resolution globally. This methodology enables accurate differentiation between crops such as maize and wheat but requires at least five cloud-free satellite images per season for reliable results. Intercropping systems present unique challenges for EO, as multiple crop species blend within a single pixel. Identifying these crops requires ultra-high-resolution imagery and spectral unmixing techniques. 

In Action: Sorghum-Peanut Intercropping in the Sahel 

A study in the Sahel region used PlanetScope’s 3-meter resolution data to monitor intercropping patterns. By analyzing near-infrared (NIR) reflectance for plant height variations and NDVI curves for flowering timing mismatches, researchers distinguished intercropped from monocropped fields with 76% accuracy. However, daily satellite revisits were necessary to capture growth-stage differences, emphasizing the need for near-continuous monitoring. 

Crop Varietal Differentiation  

At the higher end of the complexity spectrum lies the differentiation of crop varieties. This application demands high spatial and temporal resolution and specialized sensors that can detect subtle differences in plant morphology, maturity timing, or specific spectral traits unique to certain varieties. 

In Action: Identifying Drought-Tolerant Maize in Zimbabwe 

Hyperspectral imaging techniques were used to differentiate drought-tolerant maize varieties in Zimbabwe by analyzing leaf wax content (SWIR bands) and stomatal conductance (Red Edge index). Using UAV-mounted hyperspectral sensors, researchers achieved 82% classification accuracy before tasseling. However, the requirement for field-level calibration and specialized spectral libraries limits large-scale deployment.

Single-Allele Differentiation between related varieties of a crop 

The most complex application involves differentiating varieties that differ by a single allele, such as submergence-tolerant rice or Bt cowpea. These applications push EO to its limits, often requiring hyperspectral imagery with hundreds of spectral bands that can detect morphological or biochemical differences in plant tissues resulting from genetic modifications.  

In Action: Detecting Sub1 Submergence-Tolerant Rice in Bangladesh 

A groundbreaking study combined Sentinel-2 flood monitoring with genomic marker analysis to identify Sub1 rice varieties, which survive prolonged flooding. By linking satellite-derived waterlogging duration (>14 days) with post-flood survival patterns, researchers correlated resilience traits with Sub1 allele distribution. This fusion of EO and genetic data has the potential to guide climate-resilient crop deployment but still requires UAV-based hyperspectral imagery for validation at sub-meter resolutions. 

The Future of EO in Agriculture: Bridging Data & Decision-Making

As EO applications grow in complexity, several factors must be considered:

1. Data Accessibility: Higher-resolution data is costly—DevGlobal advocates for open-access EO solutions like Digital Earth Africa to democratize data use.
2. Local Capacity: Advanced EO requires trained analysts—our capacity-strengthening programs ensure local experts can integrate EO into agricultural decision-making.
3. Multisector Integration: EO insights must be linked to policy frameworks, farmer advisory services, and climate-smart agriculture initiatives—areas where DevGlobal’s expertise bridges the gap between technology and action.

To learn more about how DevGlobal leverages EO for sustainable agriculture and global development, get in touch with our geospatial team today!