Evaluation of benefits and constraints for the use of Sentinel-2 for agricultural monitoring

Transcription

1 23/05/2014 Evaluation of benefits and constraints for the use of Sentinel-2 for agricultural monitoring Isabelle Piccard, Herman Eerens, Kris Nackaerts, Sven Gilliams, Lieven Bydekerke Sentinel-2 for Science Workshop, May 2014, ESA-ESRIN, Frascati (Italy)

2 Evaluation of the use of Sentinel-2 for agricultural monitoring» Studied aspects:» Spatial resolution» Temporal resolution» Evaluation based on time series of DMC-1/2 and Deimos-1 images (22m resolution) over Belgium (Flanders) 23/05/2014 2

3 Spatial aspects» Belgium: small agricultural parcels (very) high resolution imagery required for crop monitoring Study area in E-Belgium 23/05/2014 3

4 Spatial aspects» MODIS provides insufficient detail <10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% >70% Crop area fraction (%), derived from IACS data Winter wheat Example: MODIS vs. DMC derived anomaly maps for winter wheat, 2012, municip. Hoegaarden Sugar beets fapar return period MODIS May 2012 MODIS July 2012 Potatoes DMC 27 May 2012 DMC 25 July 2012 Maize 23/05/2014 4

5 Temporal aspects» Optical imagery: cloud cover» 10-day revisit time seems insufficient» MULTI-SENSOR approach: combination of S2, Landsat, DMC/Deimos, intercalibration!» Number of cloud free DMC/Deimos observations, (intensive acquisition campaigns in 2011 & 2012; other years: archive data) /05/2014 5

6 Continuous time series required! Gap filling: how?» Combination of different optical HR sensors: intercalibration» Combination of optical HR and MR sensors: resampling & intercalibration or using data fusion techniques (eg. STARFM and derived methods)» Combination of optical and SAR sensors» Combination of optical HR data with a crop growth model + = MODIS: - high frequency - low resolution Landsat: - low frequency - high resolution 23/05/2014 6

7 Phenology monitoring» Continuous time series temporal profiles» Phenology detection (calibration with field observations)» Development stage field practices / planning & logistics» Input for crop growth modelling» Control purpose (e.g. harvest date early potatoes ) Potato parcels, 2012 (IACS data) Declared earlies Declared earlies Not OK OK 23/05/2014 7

8 Crop monitoring» Common practice for low / medium resolution imagery:» Selection of EO index, preferably biophysical parameters» Comparison of actual values with long term average (LTA): absolute or relative differences, z-score, return period, VCI, VHI, VPI,» Similar approach for high resolution imagery 23/05/2014 8

9 High resolution Biophysical parameters» Algorithm for DMC/Deimos developed by INRA Source: INRA Source: INRA 23/05/2014 9

10 High resolution Biophysical parameters» Validation:» DHP measurements» N-Belgium» Maize & potatoes, /05/

11 DMC fapar Building a high resolution long term average» DMC long term average (LTA) derived from MODIS (spectrally similar)» Comparison DMC-fAPAR vs. MODIS-fAPAR at 250m resolution (same day, equal sampling intervals) equation» unmixing of MODIS-S10 fapar [ ] per crop & per municipality MODIS LTA statistics MODIS LTA image at 25m resolution equation DMC LTA image R²=0.92 fapar DMC = * fapar MODIS MODIS fapar DMC fapar, long term average 1st dekad of June (winter wheat) 23/05/

12 Building a high resolution long term average» Validation: comparison of MODIS based LTA per municipality with actual DMC LTA ( ) for winter wheat MODIS derived LTA < actual DMC LTA MODIS: mixed pixels, influence summer crops MODIS derived LTA > actual DMC LTA 23/05/

13 Crop monitoring & anomaly detection» Monitoring at parcel level: comparison of actual and average index values» Intra-field variability: can be improved with 10m S2 data» Inter-field variability 14 May 25 May 27 May 28 May 24 July 25 July 10 Aug 18 Aug 29 Aug 4 Sept 7 Sept 8 Sept 16 Sept 30 Sept 15 Oct Example: potato monitoring with DMC/Deimos at 25m resolution based on fapar return period (May-Oct 2012) Field 140 (high yield: 69 ton/ha) 23/05/

14 Crop monitoring & anomaly detection» Validation: comparison with MODIS, pure pixels Min. 20% winter wheat 23/05/

15 Yield estimates» Development of yield models based on time series of HR data (example: potatoes) Crop harvester with integrated Probotiq Yield Master Pro sensor Spatial variability of yield for single field 23/05/

16 Other examples» Approach also tested in other regions:» Ethiopia (ISAC & Spot4Take5)» Morocco (Spot4Take5)» Input: DMC, Landsat, SPOT-4, RapidEye NDVI images & land cover maps» LTA computed based on MODIS, only resampled & translated to HR» Anomaly maps computed & validated with field data» Check out our poster! 23/05/

17 Conclusions» S2 benefits:» Improved spatial (and spectral) resolution» S2 constraints:» 5-10 day revisit time may be too long, especially in cloud-prone areas!» S2 opportunities for crop monitoring at parcel level!» Calculation of biophys. par s & high resolution LTA (has limitations )» Continuous time series Multi-sensor approach required need to invest in gap filling & intercalibration!» Not mentioned but equally important for (HR) time series analysis:» Geometric correction make sure basic products are OK» Atmospheric correction make sure basic products are OK» Accurate cloud & shadow detection: not easy to automate 23/05/

18 Thank you! 23/05/