Experiences with Sentinel-2 data for crop monitoring in. Belgium

Transcription

1 Experiences with Sentinel-2 data for crop monitoring in Roel Van Hoolst, VITO, Scientist, Remote Sensing Applications 13/06/2018 THEIA Workshop Belgium

2 Sentinel 2 allows us to monitor crops at the parcel level.

3 A priori Why potato? Belgium = largest exporter of frozen potato products in the world Belgian potato industry developed into one of the fastest growing sectors in the Belgian food industry We are main consumers.

4 Applications & their needs

5 Crop monitoring Watch It Grow For all actors in the potato chain: Get access to satellite images, weather data, yield forecasts field data (e.g. treatments, yield samples, ) At parcel level: Crop development Field heterogeneity Risk at production and quality losses Yield forecasts More info

6 Potato phenology can be a matter of days Late potato example with above average temperatures 6th June th June th June 2018

7 Crop type mapping Currently, random forest pixel-based classification Sentinel-1 images Sentinel-2 images Coming soon: Field-based neural network classification

8 Automatic agricultural parcel delineation based on a deep convolutional neural network Sentinel-2 images Apply neural network, segment the result, and convert to polygons Automatic generation of cloud-free 128X128 NDVI stacks

9 L2A input & their impact on crop profiles

10 TerraScope terrascope.be Belgian Collaborative Ground Segment for Sentinel Data Coverage: Belgium TOC + LAI, FAPAR, FCOVER, NDVI Processing S2: Additional (partly) manual geometric correction Cloud/shadow: SEN2COR icor for atmospherical correction Biopar (~S2-Toolbox) for Biophysical parameters

11 ATMOSPHERIC CORRECTION: ICOR (TerraScope) vs MAJA Tile: 31UFS (Belgium) 26th July 2017 (Unclouded) B02 B03 B04 B05 B06 B07 B08 B8A B11 B12

12 CLOUD/SHADOW: SEN2COR vs MAJA Tile 31UFS 11th april 2016 Zoom 18th September 2018 Zoom 3th January 2013 Zoom 5th June 2013

13 Impact on NDVI potato pixel profiles Intuitive outliers extra observations

14 Supporting R & D

15 1. Multi-temporal cloud detection method at the source At radiometry level Step 1 Pre-filtering: Principle: The mean (mu) and standard deviation (sigma) of non-zero observations are computed; the clouds/shadows are the points falling outside the interval: [mu-rinf*sigma; mu+rsup*sigma] Step 2 - A modified Hampel filtering scheme Principle: Local temporal statistics to define bounds between cloudy/clear pixels How implemented?: Extra quality layer in TerraScope Interactive toolbox: Flexible outlier detection parameterize regionally and according to users needs

16 1. Multi-temporal cloud detection method at the source Original Outliers removed Original Outliers removed

17 2. Optimize Sentinel 2 crop parcel time series Starting point: All overlapping observations Basic cloud/shadow mask Basic Whittaker Smoothing potato fapar example First filtering: 95% valid pixels over parcel Basic cloud/shadow mask Basic Whittaker Smoothing Second filtering: 95% valid pixels over parcel Scene Classification Basic Whittaker Smoothing

18 2. Optimize Sentinel 2 crop parcel time series Improve Smoother: 95% valid pixels over parcel Scene Classification Weighted Whittaker Smoothing Local maxima: high weights Local minima: low weights Positive/Negative Slope potato fapar example Improve Smoother: 95% valid pixels over parcel Scene Classification Temporally Weighted Whittaker: Adjust weights per growing stage Remove outlier: 95% valid pixels over parcel Scene Classification Temporally Weighted Whittaker: Adjust weights per growing stage Outliers removed Max dip per day Max dif per day Stage 1 Stage 2 Stage 3

19 3. CropSar: Optical Radar fusion Sentinel-2 interrupted Sentinel-2 uninterrupted CROPSAR prediction Sentinel-1 uninterrupted Example potato field monitoring

20 3. CropSar: Test Dataset CROPSAR prediction Next steps: Further improvement of the method Inclusion of Sentinel-3 in the fusion procedure Evaluation of performance in different regions Expanding test period to current 2018 growing season Assessing added value for monitoring and classification Field-based fused S1-S2 time series for all agricultural parcels of 2017 growing season in Flanders

21 Colleague contributions to the presentation Ruben Vandekerckhove S2 focal point Kristof Van Tricht S1/S2 fusion Parcel Delineation Crop Type Mapping Anne Gobin Yield Modelling WIG Daniel Iordache Outlier Detection VITO Remote Sensing Bart Beusen S2 time series Yield modelling Jeroen Dries MEP parcel extraction Isabelle Piccard WIG coördination Roel Van Hoolst (S2 time series), VITO, Scientist, Remote Sensing Applications 13/06/2018 THEIA Workshop Thank you for your attention Sven Gilliams S1/S2 fusion concept Dominique Haesen S2 time series