Automatic retrieval of biophysical and biochemical canopy variables: an example based on AHS data from AGRISAR campaign

Transcription

1 Automatic retrieval of biophysical and biochemical canopy variables: an example based on AHS data from AGRISAR campaign Wouter Dorigo, Heike Gerighausen & Erik Borg German Remote Sensing Data Centre, German Aerospace Centre (DLR) AgriSAR and EAGLE campaign final workshop, ESA/ESTEC, Noordwijk,

2 Contents Introduction Test site DEMMIN Key notes The CRASh model Automatic retrieval of canopy variables Results Conclusions & Outlook AgriSAR and EAGLE campaign final workshop, Folie 2

3 Introduction Activities of DLR Neustrelitz within AgriSAR 2006 Local Coordination: C. Wloczyk Weekly ground truth in close cooperation with ZALF In-situ parameters sampled: LAI Biomass (wet and dry) Vegetation height, crop coverage Soil moisture (at two depths) BBCH phase Chlorophyll (SPAD) Plants per m², shoots per m AgriSAR and EAGLE campaign final workshop, Folie 3

4 Information Information Network Network Monitoring Monitoring Multidisciplinary Multidisciplinary Durable Durable Environmental Environmental Test Site DEMMIN Durable Environmental Multidisciplinary Monitoring Information Network * * AgriSAR and EAGLE campaign final workshop, Folie 4

5 Test Site DEMMIN Greifswald Neustrelitz Approx ha of agricultural fields, average size per field is 80 ha Unique test site regarding overall size and field dimensions Cooperation with IG-Demmin since 1999: 3 Ltd s, 2 joint-stock companies Extensive data base by cooperation with farming association AgriSAR and EAGLE campaign final workshop, Folie 5

6 DEMMIN data base Digital quasi-static Data Soil maps Land use/ Crop maps Digital dynamic Data Yield and application maps Macro and micro nutrients (i.e. Nitrogen sensor) Various in-situ measurements Meteorological measurement network At present 15 stations E.g. air temperature, wind direction and speed, soil moisture and temperature, pyranometer, pyrgeometer AgriSAR and EAGLE campaign final workshop, Folie 6

7 Calibration and Validation at DEMMIN CATENA DEMMIN Products Validation Automated Processing Chain for Derivation of Value Added Information Products on basis of Remote Sensing Data Test Site and Information Network for Validation of Value Added Information Products on basis of RS AgriSAR and EAGLE campaign final workshop, Folie 7

8 Perspectives of DEMMIN Extension of existing monitoring facilities N-Sensor Agrarian Meteorological Network In-situ measuring techniques Automation of data integration Systems planned in future Corner reflectors for radar (Sentinel1) Geometric calibration sites Spectral calibration sites Yield measurement facilites AgriSAR and EAGLE campaign final workshop, Folie 8

9 The CRASh model (by Wouter Dorigo) Inversion of a combined leaf and canopy radiative transfer model PROSPECT+SAILh for the estimation of biophysical and biochemical parameters Output variables: LAI, leaf chlorophyll, dry matter, and water content Agricultural land cover types Regional approach: sensors with a high spatial resolution (Precision Agriculture) Specifically designed for airborne hyperspectral sensors with a wide FOV, but applicable to different band configurations (HS/MS VNIR/FR) and observation properties AgriSAR and EAGLE campaign final workshop, Folie 9

10 The CRASh model (by Wouter Dorigo) Raw Data Image based and completely automatic: flexible Laboratory Calibration Data Transcription Level 0 Product Open to a priori knowledge on land cover / phenology Vicarious Calibration System Correction Build Metadata Archiving L0 Onboard Calibration Sources Level 1 Product Link to existing processing chain Attitude Data, operative at DLR Imaging Position Data, Parametric Geocoding DEM Spectroscopy group Archiving L1 Level 2a Product RTM, Meteorologic Data, DEM Atmospheric Correction Level 2 Product Archiving L2 Thematic Product AgriSAR and EAGLE campaign final workshop, Folie 10

11 The CRASh model at a glance TOC reflectance data Autom. Classification (SPECL) View/sun Configuration AZI, θv Products Leaf area index Leaf chlorophyll CRASh Dry matter Water content LUT LC 1 LUT LC 2 LUT LC n DB with canopy properties per class PROSPECT +SAILh AZI θv Atm. params AgriSAR and EAGLE campaign final workshop, Folie 11

12 CRASh - SPECL classification Automated land cover classifier based on template spectra SPECL spectral land cover classes Dark/average/bright vegetation Yellow flowering vegetation (e.g. rape) Sparse vegetation Mix soil / vegetation Dry vegetation / soil Various non vegetative classes Opportunity of local soil characterization AgriSAR and EAGLE campaign final workshop, Folie 12

13 CRASh - Inversion scheme Inversion per class and view zenith angle (interval 1-3 ) Atmospheric properties, local soil conditions, view and sun constellation Predictive VI regression functions based on LUTs Optimized for LC and sun/observation geometry Model/sensor noise included Selection of best performing VIs Linear and exponential fit Weight in cost function depends on R² and RMSE AgriSAR and EAGLE campaign final workshop, Folie 13

14 CRASh for AHS data from AgriSAR campaign Input image data 06./ AHS Flight line P01 and P02; / Atmospheric and geometric corrected Original spatial resolution 2m resampled to 6m Ground truth data: sampled by Chlorophyll: ZALF, University of Valencia Biomass: ZALF, University of Kiel LAI: ZALF, University of Kiel, LMU Munic, ISSIA 04./ Location of ground truth sampling points ZALF/ DLR, University of Kiel: 06 and 07/2006 = 27 Partner: 06/2006 = + 12, 07/2006 = AgriSAR and EAGLE campaign final workshop, Folie 14

15 CRASh for AHS data from AgriSAR campaign Calculation of canopy parameters from ground truth data: Leaf water content Cw [g/cm²] = (Biomass (wet) Biomass (dry) ) / LAI Leaf dry matter Cdm [g/cm²] = Biomass (dry) / LAI Leaf chlorophyll Cab [µg/cm²] SPAD calibration from University of Valencia, S. Gandia Universal Cab = 0,722*DC, R² = 0.76 Crop specific Cab Maize = 0,703*DC, R² = 0.81 Cab Barley = 0,730*DC, R² = 0.67 Cab Wheat = 0,769*DC, R² = 0.88 Cab S.-Beet = 0,695*DC, R² = 0.43 Leaf area index LAI [m²/m²] = direct input AgriSAR and EAGLE campaign final workshop, Folie 15

16 CRASh Results: LAI Wheat Barley Wheat Rape Maize Rape Barley P01 Barley Rape Rape Wheat Sugarbeet Wheat P02 Barley AgriSAR and EAGLE campaign final workshop, Folie 16

17 CRASh Results: LAI Similar pattern for LAI measurements from AgriSAR partners in July during 3rd intensive campaign Maize Wheat High RMSE but good estimates for sugar beet and maize Sugarbeet Wheat AgriSAR and EAGLE campaign final workshop, Folie 17

18 CRASh Results: Chlorophyll / P02 RMSE Universal Crop-specific Universal Crop-specific P Cab (universal) P Tab.: Results based on ZALF grey, Results based on Uni Valencia - white General overestimation of Cab values Better performance with SPAD DC SPAD DC AgriSAR and EAGLE campaign final workshop, Folie 18

19 CRASh - Results SPECL classification Bare soil Mixed veg./ soil Dry veg./ soil Mixed veg./ soil P02 P01 Field 450, winter barley: Bright veg. Average veg. Field 230, winter wheat: P02 Average veg. P01 ID LAI Estim. LAI Meas. Std.Error / / / Scan angle during image data acquisition AgriSAR and EAGLE campaign final workshop, Folie 19

20 CRASh Results Cdm and Cw not applicable because of parameter definition: leaf water content and leaf dry matter Correction factor/ function according to plant phenological stage Improvement of results for AHS?! Model performance with pixel specific viewing angle: presented results based on assumption of nadir-acquisition for each pixel Integration of land use information Impact of different spatial scale?! Model 18x18m = 324m² Sensor 6x6m = 36m² Plot 1x1m = 1m² AgriSAR and EAGLE campaign final workshop, Folie 20

21 Conclusions & outlook DEMMIN test site is in permanent development Extension of calibration and validation facilities CRASh Automatic retrieval of canopy parameters Part of processing chain Satisfying performance on other data (Hymap, Chris-Proba) Revision of first results AHS data Comparison with CASI data AgriSAR and EAGLE campaign final workshop, Folie 21

22 For questions on CRASh please contact: Mr. Wouter Dorigo: Formerly DLR, Now: Institute of Photogrammetry and Remote Sensing (I.P.F.) Vienna University of Technology (TU Wien) References Dorigo W., Baret F., Richter R., Ruecker G., Schaepman M. & A. Müller (2007) Retrieving canopy variables by radiative transfer model inversion an automated regional approach for imaging spectrometer data. Proc. 5th EARSeL Workshop on Imaging Spectroscopy, April, Brugge, Belgium. W.A Dorigo (2007). Retrieving canopy variables by radiative transfer model inversion - a regional approach for imaging spectrometer data. PhD thesis. TU München. AgriSAR and EAGLE campaign final workshop, Folie 22

23 Thank you IG-Demmin: Trunk, Paschen, Mr. Zabel ZALF: Prof. Sommer, Mr. Wehrhan, Dr. Verch and his team DLR Neustrelitz team ESA and the whole AgriSAR-Team for coming to the DEMMIN test site You are welcome back for research on test site DEMMIN! AgriSAR and EAGLE campaign final workshop, Folie 23

24 Thank you for your attention! AgriSAR and EAGLE campaign final workshop, Folie 24