Better Hackathon 2020 - WFP - Enhancing Agricultural Mapping With BETTER Pipelines
- 1. Enhancing Agricultural Mapping with BETTER Pipelines WFP - BETTER Hackathon – 23rd October 2020 Big-data Earth observation Technology and Tools Enhancing Research and development http://ec-better.eu This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 776280
- 2. Relevance for WFP Changes in Agricultural Area under Conflict Crop type mapping in conflict areas: Quantify losses of cropland and post-conflict regeneration. First time cropland baseline (e.g. South Sudan) Technical assistance to governments: In close cooperation with Min of Agriculture. Smallholder support systems: Assess seasonal performance potential, likelihood of meeting procurement targets, changes staple-cash crop in area of intervention. WFP Project Interventions: Agriculture oriented FFA projects, resilience building interventions, index insurance. BETTER | Enhancing Agricultural Mapping with BETTER Pipelines
- 3. Sentinel-2 Data Assimilation Cloud Storage and Processing Centre Crop Type Map ONA Platform Assimilation, Quality Control, Conversion Sen2Agri System BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Crop Type Ground Data Collection
- 4. BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Preferred option: Geo-referenced perimeters Field Data Large Fields along roads: Geo-referenced Transects Field Capture Office Completion Huge Fields: Single Points / Corners
- 6. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Sen2Agri has served its purpose well. However: • It only uses S2 and L8, but no SAR. • There is a module for atmospheric correction but significant atmospheric interference may still have an impact on the quality of the classification WFP ClEO Team has implemented a flexible, data-driven, pixel-optimized filter: • So far routinely applied to medium resolution data, NDVI and LST – smooth, clean images without mosaicking artifacts or cloud inflicted gaps. • Can we apply the same to Sentinel-2 data? Good for hazard mapping, but is there a benefit for crop type (or more generally land cover) classification
- 7. http://ec-better.eu Jan-Oct 2019 BETTER | Enhancing Agricultural Mapping with BETTER Pipelines
- 8. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Filter behavior is modulated by two parameters: • S – controls the amount of smoothing that is applied. The smaller the S, the closer to the original data the output is. At the extremes the output will be a polynomial of degree n (usually 1 or 2). • S can be optimized (CV, V-curve)
- 9. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Filter behavior is modulated by two parameters: • p – controls the asymmetry of the filtered output and is a value between 0 and 1. When p is 0.5, filtered output balances noise. As p tends to 1 the filter fits the output to the higher envelope of the signal. As p tends to 0 the filter fits to the lower envelope of the signal (baseline) . • NDVI: use p > 0.5 • Reflectances: use p < 0.5 • p is not optimized, expert judgement
- 10. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines Data Set: • 8800 time series of raw and smoothed Sentinel-2 reflectances and SAR • Smoothing: S = optimized btn 3.0-4.0 and p=0.2 (p=0.3 for Band 8, 11 and 12) • Nature: pixels selected inside each agronomic data sample, 25m distance • Field data: Crop field outlines, collected early October 2020 (pre-harvest) • Location: Adamawa State, NE Nigeria • Dates: Time series from May to October 2020 • Crop Types: Maize, Millet, Sorghum, Beans, Soya and their mixes. Also fallow (unused) and No-ID (abandoned field) • 7000 labelled data and 1800 unlabelled
- 11. http://ec-better.eu BETTER | Enhancing Agricultural Mapping with BETTER Pipelines The Exercise: • Devise a classifier for crop types to be run with the various datasets provided and results compared • You are free to choose combinations of data. E.g. • Raw S2 vs Filtered S2 • Raw S2 + SAR vs Filtered S2 • SAR vs Filtered S2 • … whatever • You can team up if you want • Produce your predictions for the unlabeled data and submit Google Hangouts for Bilateral Support: https://meet.google.com/mde-useo-qrb
- 12. Enjoy the Work! Thank You! http://ec-better.eu