Crop type mapping and growth monitoring thanks to a synergistic use of SAR and optical remote sensing

Crop type mapping and growth monitoring thanks to a synergistic use of SAR and optical remote sensing Pierre Defourny(1), Xavier Blaes(1), Moira Callens (2), Vincent Guissard (1), Valerie Janssens (2), Cozmin Lucau (1), Valentijn Pauwels (2), Niko Verhoest (2) (1) Department of Env. Sciences - Geomatics Université catholique de Louvain Louvain-la-Neuve, Belgium (2) Laboratory of Hydrology and Water Management Universiteit Gent Gent, Belgium 1 Outline Introduction Research results and applications concerning crop type, crop height, LAI, integrated approach from ESA - PRODEX Project ESA - DUP Project ESA - ESTEC Study Région Wallonne DGA Project STEREO Program STEREO-CROP Project MODELLING CROP GROWTH BASED ON HYDROLOGY AND ASSIMILATION OF REMOTELY SENSED DATA Perspectives 2 1

Why optical and SAR for agriculture? Optical data are much easier to use Reflectance closely related to photosynthesis but Agric. applications are much time-constrained : critical periods for crop observ. are short timing of information delivery is the key issue revisiting capability makes the difference 3 Froment Betterave Growing season in Chastre 23 by SPOT 5 Maïs 24/3 15/4 16/4 16/5 27/5 1/6 7/6 15/7 6/8 13/8 17/9 15/1 (V. Guissard, UCL-Geomatics,24) 4 2

1st April December May August September November January June October February March July 2 2 2 2 2 2 2 2 2 STEREO Scientific Meeting 2 September 25 UCL-Geomatics and RUG-Hydrology 5 Why optical and SAR for agriculture? SAR backscattered signal : very well calibrated (active sensor) not affected by atm. conditions/clouds acquisition day and night related to plant water content But SAR backscattered signal is a function of : vegetation biomass and water content crop type and plant elements geometry top-soil moisture and soil roughness land slope and seed row direction to be taken into account for any SAR application! STEREO Scientific Meeting 2 September 25 UCL-Geomatics and RUG-Hydrology 6 3

Crop type mapping Images : 3 optical & 15 SAR Field boundaries Field averages (X 1, X 2,..., X 6571 ) (Y 1, Y 2,..., Y 6571 ) 6571 parcels 39 crop types Parcel-based classification Crop map Maximum likelihood (39 classes) Accuracy (899 parcels) 7 Crop type mapping Optical images combinations (3 images) 1 image 2 images 21/3 8/6 1/8 Accuracy 22 % 58 % 64 % 57 % 67 % 71 % = % correctly classified parcels 3 images 75 % Accuracy of 75% for 39 crop types Information delivery in June : accuracy of 57% 8 4

Crop type mapping Optical + SAR images combinations (57%) 2 optical images + SAR 21/3 8/6 1/8 SAR 3 4 5 1 Accuracy 62% 63% 66% 72% (75%) 3 optical images + SAR 21/3 8/6 1/8 SAR Accuracy 3 5 77% 8% Optical + SAR : performances improved 2 optical + 1 SAR ~ 3 optical images but info in June! 9 Crop type mapping Operational crop control system : Belgian case Farmers application April Optical images March June July Declared crop type Field boundaries Parcel-based classification July No Suspicious parcel? Yes Accepted parcel Photo- Interpretation Rapid field visit Rejected parcel August 1 5

Operational crop control system : Belgian case Control performances assessment Fraud detected (%) # optical images # SAR images # SAR + 2 optical images # SAR + 3 optical images % Efficiency Higher the number of images, better the efficiency SAR alone less efficient than optical alone Optical images efficiency improved with SAR 11 Operational crop control system : Belgian case Visual SAR photo-interpretation is possible! RADARSAT temporal composition R: March, G: May, B: June 1) Filtering (enhanced Lee) 2) Masking 3) Relative comparison? Winter wheat Winter barley Grassland Sugar beet 12 6

Crop monitoring parameters retrieval Tandem interferometric coherence images ERS-1 ERS-2 (day 1) 2) ERS-1 ERS-2 Phase shift (Amplitude & phase) Correlation Coherence image 2 37 72 1 36 71 days Coherence image 13 Crop parameters retrieval Coherence decreases during crop growth,7. 57, : 14 7

Crop parameters retrieval Coherence winter wheat parameters (height, cover) Field measurements synchroneous with 6 Tandem pairs Coherence,9,8,7,6,5,4,3,2,1 y = -,51x +,817 R² =,92 (n = 32), 2 4 6 8 1 Crop Hauteur height (cm) (cm) Strong relationship between coherence and crop variables 15 Crop parameters retrieval Winter wheat height estimation model from coherence 1 9 8 7 measured values regression line IC5% IC9% H = 179.7 ρ + 149.7 Crop height (cm) 6 5 4 3 2 1.2.3.4.5.6.7.8.9 Coherence Crop height prediction error < 17 cm (for IC 9%) < 7 cm (for IC 5%) 16 8

Crop parameters retrieval Coherence crop variables : 4 crop types Coherence,9,8,7,6,5,4,3,2,1, 2 4 6 8 1 Coherence.8.7.6.5.4.3.2.1. 2 4 6 8 1 Crop height (cm) Canopy cover (%).9 Winter wheat Sugar beet Maize Potato For all crops : Coherence decrease during growing period Great potential of the coherence for crop monitoring Relationships varies according to crop structure 17 Crop parameters retrieval Coherence for detection of sugar beet sowing date Coherence image of the 4-5 April 6 1 3 4 5 2 thanks to soil roughness change effect on the signal 18 9

Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture 19 Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition (23) Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture 2 1

Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition (23) Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture Stereographic photo technique Meshboard measurement 21 Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition (23) Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture Kopecky rings volumetric soil moisture 22 11

Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition (23) Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture 23 Crop parameters retrieval Intensive field campaign for ENVISAT, ERS & SPOT5 Simultaneous field measurements and SAR acquisition (23) Measurement frequency Once during the growing season Several times during the growing season Regularly during the growing season (every fortnight to every week, depending of the phenological stage, simoultaneously with SAR image acquisition) Measured parameters Crop variety Sowing date Row interval Inter plant distance in the row Row orientation Field slope Yield Fresh biomass Dry biomass Soil roughness Volumetric soil moisture Phenological stages Plant height Canopy cover Leaf Area Index Special events Gravimetric soil moisture Vertical digital photography technique 24 12

Crop parameters retrieval LAI map derived from optical data 27 Crop parameters retrieval more complex from SAR data Crop backscattering coeff. is influenced by soil moisture -4 cm Backscattering coefficient (db) 4 2-2 -4-6 -8-1 -12-14 -16-18 -2 5 1 15 2 25 3 35 4 Volumetric soil moisture (%) σ o =7.m v -2.6 R²=.81 28 14

Crop parameters retrieval Crop backscattering coeff. is influenced by soil moisture 4-9 cm Backscattering coefficient (db) 4 2-2 -4-6 -8-1 -12-14 -16-18 -2 5 1 15 2 25 3 35 4 Volumetric soil moisture (%) σ o =4.m v -15.1 R²=.54 29 Crop parameters retrieval Crop backscattering coeff. is influenced by soil moisture 9-135 cm Backscattering coefficient (db) 4 2-2 -4-6 -8-1 -12-14 -16-18 -2 5 1 15 2 25 3 35 4 Volumetric soil moisture (%) σ o =8.m v -8.6 R²=.34 3 15

Crop parameters retrieval Crop backscattering coeff. is influenced by soil moisture 9-3 cm Backscattering coefficient (db) 4 2-2 -4-6 -8-1 -12-14 -16-18 -2 5 1 15 2 25 3 35 4 Volumetric soil moisture (%) σ o =9.m v -8.9 R²=.43 31 Crop parameters retrieval CLOUD radiative model to retrieve crop parameters from backscattering coefficient direct contribution of vegetation σ ηcosθ = 2κ e ( 1 ( h) ) veg Tveg σ = σ + σ total veg soilt veg ( h) Tveg ( h) = h e 2κ e secθ contribution of the soil attenuated by the vegetation σ db C D mv soil ( ) = +. The linear equation Backscattering coefficient (db) -2-4 -6-8 -1-12 1 15 2 25 3 Volumetric soil moisture (%) 32 16

Crop parameters retrieval LAI estimation from SAR data Maize fields ( 95, 97 and 23) with row orientation between 6 and 9, n=24 (synchroneous top soil moisture measurement required) Model calibration Model inversion 33 Crop parameters retrieval LAI estimation from SAR versus optical data Winter wheat Mean Absolute Error winter wheat maize Optical data :.49.41 SAR data : 1.1.93 Best estimation from optical data but SAR estimate still relevant 34 17

Towards a more integrated retrieval approach SAR data frequent update Optical data reflectance Hydrological modeling Coupling of hydrological and radiative transfer models for crop parameters retrieval 35 Towards a more integrated retrieval approach TOPLATS as hydrological model 36 18

Towards a more integrated retrieval approach Volumetric soil moisture estimated by TOPLATS in order to replace synchroneous in-situ field measurement Winter wheat: field 1 37 Towards a more integrated retrieval approach LAI using volumetric soil moisture estimated bytoplats Winter wheat no significant impact of estimated values on SAR inversion promising results towards SAR inversion at regional scale 38 19

Towards a more integrated retrieval approach SAR data frequent update Optical data reflectance Crop growth modeling Hydrological modeling Coupling of hydrological and radiative transfer models for LAI assimilation into regional crop growth model 39 Towards a more integrated retrieval approach Maize - 23 LAI (m²/m²) Julian days LAI from optical data to adjust the crop growth model Soil moisture from TOPLATS to improve the crop growth model LAI from SAR dat to fill the gap/confirm the estimate of optical data 4 2

Conclusions and perspectives Current optical and SAR data was found very complimentary for operational crop type discrimination improving either the information time delivery, the accuracy or the visual interpretation 1-d Coherence data derived by SAR interferrometry is the most robust information for crop monitoring but no such EO capabilities foreseen before some time!!!! LAI estimate from SPOT-5 data is relevant for crop monitoring Current LAI estimate from SAR data is a useful substitute significant improvement expected soon from dual-pol. ratio Hydrological modelling enhances SAR agriculture applications further integration of hydrological model, radiative transfer model and crop growth model is required for a multi-sensor LAI estimate at regional scale 41 21