Precision Agriculture: Deep Learning-Driven Crop Mapping from Multi-Temporal Sentinel-2

This project harnesses deep learning and multi-temporal Sentinel-2 imagery to enable efficient and accurate crop mapping, supporting the goals of precision agriculture through enhanced spatial-temporal understanding of phenology and crop types.

Project Highlights

• Data Pipeline: Utilized Sentinel-2 time-series data to derive vegetation phenology metrics such as NDVI curves, temporal growth patterns, and spectral change rates—key indicators for crop identification.
• Modeling Approach: Designed a deep neural network tailored for time-series input—likely involving convLSTM, 1D temporal convolutions, or U-Net variants—to learn from vegetation dynamics rather than static snapshots.
• Architecture Strengths: The model effectively differentiates among crops by capturing phenological signatures (e.g., green-up dates, senescence trends), improving classification in complex agricultural landscapes.
• Precision Agriculture Impact: Accurate crop mapping aids in optimized resource allocation (e.g., irrigation, fertilization), yield forecasting, and improved farm management decisions.

Key Outcomes

• Delivered high-resolution crop maps over agricultural regions, enabling real-time agricultural insight for stakeholders.
• Demonstrated deep learning’s ability to decode crop phenology, even across inter-annual variability and changing growth conditions.
• Contributed to precision agriculture workflows with improved accuracy and operational readiness for field-scale monitoring.

StoryMap reference: an ArcGIS StoryMap titled “Deep Learning Based High-Resolution Crop Mapping” that illustrates the full workflow—from Sentinel-2 data through phenology extraction to trained model outcomes:contentReference[oaicite:2]{index=2}.

Keywords: Precision Agriculture, Sentinel-2, Vegetation Phenology, Deep Learning, Crop Mapping, Time-Series Remote Sensing