AGRISAR 2007 Final Conclusions

Transcription

1 AGRISAR 2007 Final Conclusions Irena Hajnsek & AGRISAR Team (124 participants), German Aerospace Center Folie 1 irena.hajnsek@dlr.de

2 Sentinel-1: Global data acquisition (land & ocean) One hour after data acquisition provision of the data Wave mode ocean IWS over land (400 km) Sentinel-2 Global data acquisition (land & ocean) Soil quality maps (monitoring) Water resources (monitoring) Natural protected areas (monitoring) Land cover map (service) Land use (service) Geophysical variable maps (service) -56 Southern America Northern Greenland Access rapidly within 1 to 3 days 13 spectral bands VNIR and SWIR several band in different resolution 10m/20m/60m resolution Bands ranging from 400 to 2400nm Compatible with LANDSAT continuity Red edge will give information about the plant condition status different band will cover it cm geolocation accuracy 4 ground station are planned + one station for commanding the satellite 1 ton mass small launcher life time for 7 year propellant for 12 years 2 Terabit solid state mass memory rate 450 Mb/s in X-band Folie 2

3 Wim: Eagle Overview 2 observation tower operated by ALTERA &? Grassland and close to the forest Satellite data available optical data (AATSR) ASAR data 15 june together with E-SAR data acquisition AHS, ITRES, E-SAR, ISAFoM Sky ARROW ERA (wind, heat fluxes), MIRAMAP L band solar range observations Thermal infrared observations on the tower Surface energy budget observation on the tower Goniometric observations Biosphysical properties Soil moisture observation 3D field laser observation Data base established Question: synthetic data processed in C-band Folie 3

4 Jason Howse: ITRES CASI 1500 Max spectral resolution 60 band 1.3*0.5m EAGLES (selected bands) /1.3*1.3m 370nm-1050nm 288 band 1.5*6m AGRISAR continues 1.1nm-FWHM Bidirectional effects not cancelled out Clouds effects available Bundle adjustment flight over Neubrandenburg Sentinel-2 simulations with 2 VNIR band data + using bandmath from ENVI Folie 4

5 Jose-Antonio: AHS VNIR + SWIR Absolute radiometric and spectral calibration Applanix INS positioning system 3100m flight height swath width 5 km 90 view angle Different data product levels available Lb1a/b (raw data image products), L2b (atmoshere corrected +ground referenced) 42 scenes were processed and delivered also the scenes acquired from CASI Problems SWIR noise effects availbale Simulation sentinel selection of the data from the S/N scene this in not compartibel with the sentinel simulations of the radar system 10/20/60 m resolution sampling shift of some bands Folie 5

6 Wout: sentinel simulation analysis of the AHS Dark pixel method atmosphere correction Parameter: top of canopy reflectance Top of canopy reflectance used for atmospheric corrections specification for sentinel simulation - results Dark pixel is assumed to come from the atmosphere -? Correction equation: surface atmosphere radiative transfer equation Conclusion: ASD band 18 &21 were corrected insuficciently INTA commented: band 21 is not well calibrated and 18 is only for atmosheric correction taken Malcolm commented: using just the channel that is dedicated to the atmosheric correction Folie 6

7 Victor Estelles: atmospheric aerosols & surface radiometric properties Aerosol optical depth (AOD) Columnar water vapour Ground measurements flourescence emission measured with a new device Data should be used from FUBISS and Cimel from MTOPS Aerospls are increasing with time (june to july 2006) Ground radiometric measurements: validation and calibration with the airborne sensors; Comparision chris/proba and meris Conclusion: aerosol corrected data will be still delivered to the ftp site Folie 7

8 Heike Guerigkausen: ground measurements & AHS results ha of agricultural fields Ave. field size of 80 ha 15 meterological weather stations CRASH model combined leaf and canopy RTM for esitmation of biophysical & chemical parameters: output: LAI, Prospect and SAILh Estimation of LAI with CRASH june performe good but july not (especially the wheat field seems to have problems the same and even worth for barley)! Wheat fields have a variation in AOI for the second flight Chlorophyll estimtes are not good at all! Better performance with SPAC CD Folie 8

9 Rolf: SAR data processing Questions: InSAR data availability / processing of the EAGLE sentinel-1 Folie 9

10 Ralf Ludwig: Ground measurements Soil moisture station 2x Benefits: Retrieval bio-/geophysical parameters Needs: Continuous recording is needed Selection of sample plots Uniform data analysis strageies Field data and uncertainties TDR Representativeness of sample point Aaplied sampling strategy Comptence and experience of changing obeserved Mandatories: Plausabilities of the gound data Accessing the errors More continous measuremnts temporal coverage is more important thn spatial coverage Stronger clusters Further application potential Improved assessment soil erosion for application Ks mv Structural and biochemical vegetation parameter Assimilation of data in model and management tools to prevent land degradation in productive agri area Identify and protect areas of high ersosion Managing water resources Folie 10

11 Christian vab der Toi - ITC: BRDFs acquired by directional radiative measurements Testing different models SAIL / DART for thermal 3D directional measurements Goniometer: mechanical devise in order to measure the plant strucure from each direction in a half dome Strong angular dependence in optics: Strong angular dependence in termal: wheat / sugar beet / corn Less angular dependence: forest type / winter barley Folie 11

12 Gandia: vegetation pigments & chlorophyll measurement Sentinel2 simulations for chlorophyll measurements Folie 12

13 Phillipe: surface roughness estimates with circular polarisation No sensitivity in ks change for corn in time? Variability observed within a field of s [cm] Folie 13

14 Alkhaier: soil moisture estimates TDR measurements only ground measurements taken and analyised Folie 14

15 Mattia: soil moisture estimates robust algortihms require a priori information Questions of Attema: the variability of the HH and VV curve against mv are due to the calibration effect (plus-minus of 2 db) Answer the variability is coming from the soil and not from the calibration error Folie 15

16 Junama: differential extinction cr were to small the signal is very low to see them X-band vv realistic extinction estimates can be retrieved C-band and L-band the CR are not visible Clutter changes strongly between the data acquisition (4 db) HH 3.4 db VV 5.5 db CR are accumulated with soil and water Ulaby IEEE TGRS 1987 one of the first esimtates for the exinction Stacy IGARSS 2005 extinction measured in X-band on the border of a field Error of the compass of about 20 degrees Only at the maize fields CR could recognised 20cm CR now should be increased to 40cm Lesson learned: More CR should be possitioned Lager ones Should be installed for the whole vegetation period And used flags to estimates Ground based SAR a good tool to measure the extinction increasing the resolution or increasing the CR size Irena: what is the best way to measure extinction Folie 16

17 D Urso: soil moisture estimates SAR and optical data Correlation to NDVI and LAI when the L-band SAR image is strongly reduced Aggregation of 20m statistical effects due to the properties of SAR L-band using as a proxy for NDVI Combination of SAR and optical data can be used for exchanging a priori knowledge Malcolm: which type of product would need Senitinel-1 and Sentinel-2: NDVI D Urso: no that s no true combination would be the LAI! Need for more continuous measurements Folie 17

18 D Urso: soil moisture estimates SAR and optical data Analysis of co-pol ratios for L & C band VV/HH Corn hh/vv strong decrease of the signal RRLL circular pol L-band phase difference corn phase differences obseverd for the vegetation Winter rape C-band cross-pol ratio no strong changes L-band HHVV phase difference L-band RL-LL Winter wheat Interpretation of ratios is very difficult! Provided a table with different ratios for different crop types that give a weightening maize / wheat promissing results with LLRR sensitive to the ratio variability Folie 18

19 David: polarimetric ratio s Analysis of co-pol ratios for L & C band VV/HH Corn hh/vv strong decrease of the signal RRLL circular pol L-band phase difference corn phase differences obseverd for the vegetation Winter rape C-band cross-pol ratio no strong changes L-band HHVV phase difference L-band RL-LL Winter wheat Interpretation of ratios is very difficult! Provided a table with different ratios for different crop types that give a weightening maize / wheat promissing results with LLRR sensitive to the ratio variability Folie 19

20 Henning: classiifcation Bayes ML classification Complex whishart Hoekman & vissers classification Training vs test sets Freeman & durden (training set) Eigenvector decomposition (training set) Knowledge-based (rule-based) methods Not optimum data set for crop classification not enough numbers of different crop types Hv vs AOI for different crop types HV vs time / LL pol / VV Jeffries-Matusita distance measurement using only PolSAR strong distance observed where the distance for ex/ax/an is much smaller Differentiation between crop and forest very good (only rape & forest have a confusion) ML classifier C-band gives poor results compared to L-band Multi-temporal data gives not too much more information Cross multi temporal results for C-band Conclusions: singel/dual pol better / Lee wishart is the best / multi-temporal is needed when singel/dual pol is used and if fully pol is used multi-temporal give not more information Folie 20

21 Hoekman: unsupervised classification L-band results with 91% classification results PALSAR 69 % and 73.5% slightly worth but having similar trends Multi-temporal approach gives good results 3 time scenes Folie 21

22 Schmullius: ASD and SAR Folie 22

23 ITC: forest structure & energy balance modelling wind profile are needed for the energy balance modelling 3 D view of the forest with a terrestrial laser scanners Folie 23

24 Pawels: energy balance modelling low bowen ratios even on hot days Upper and lower soil alyer are decoupled under dry condition LAS show negative values during the night PROMET show larger and stronger diurnal cycle than the measurements but the diurnal cycle of the latent heat flux is understimated Promet performes better for the water soil balance and Uni Gent model better for the energy budget Folie 24

25 Wim: energy balance model LAS wind speed measurements for the vegetation measuring sensible heat flux Folie 25

26 Discussion General points for discussion Which are the important products that characterises land surface? How operational (accuracy) are we for different products? System specification Which is a optimum data acquisition frequency to observe changing agric. phenomena? What is the best choice of frequency for agriculture? What is the preferred optical band range for agriculture? Folie 26

27 Conclusion Uniqueness of the AGRISAR/EAGLE Campaign: Multi-temporal SAR and ground measurements data set Simultaneous acquired optical data Atmospheric data from different ground based sensors Quite complete ground measurements Data quality of the data SAR data are of high different frequencies (Sentinel-1) Optical data are of high quality (it needs to be agreed for one Sentinel-2 simulation) Ground data are of good quality Folie 27

28 Conclusion Outcome of the AGRISAR / EAGLE campaigns Preliminary analysis show a high potential of using the acquired data for further studies Multi-disciplinary approaches show a high potential of new product development SAR: depending on the application either C-band or L-band is preferable L-band: classification / soil moisture estimation / surface roughness C-band: classification / soil moisture estimation / vegetation structure characterisation Combination: classification Multi-temporal HV vs polarisation for classification approaches Optical: CASI and AHS band system specification are all together quite optimal for biophysical parameter derivation Atmosphere: Validation and sensitivity of water soil balance and energy balance models (sensible heat fluxes, latent heat flux, soil heat flux,absorbed radiation) Thermal data provide an important input to the model Folie 28

29 Conclusion Potential products for agriculture Land cover classification maps (pre-operational) Soil moisture maps (experimental) Surface roughness as input for soil erosion detection (experimental) Chlorophyll map (experimental) Biomass map (experimental) Fraction of vegetation cover (experimental) LAI map (experimental) Evapotranspiration map (experimental) level 3 Recommendations from the AGRISAR / EAGLE Team Need for a continuous agricultural data acquisition (observation of stronger variability) Need for a higher crop diversity and variability in surface condition Need of a higher ground spatial sampling in order to derive a reference map Need of a higher data acquisition frequency Need of investigation for the separation between physical (vegetation growth) and natural (wind, rain) effects Need of multi-temporal observations with both SAR & optical sensors simultaneously Stronger multi-disciplinary (SAR & optics & atmosphere) projects Intensive analysis with the collected data should be supported General Recommendation for Sentinel-2 Radiometric sensitivity of Sentinel-2 should make the measurement of inland water possible/feasible Folie 29

30 Conclusion Open Actions Sentinel-1 simulation of a selected EAGLE data set Sentinel-2 simulation: need to be agreed for one final simulation Ground data need to be checked and homogenised Important Dates Workshop paper submission deadline: END of October 2007 Final report submission deadline:16 of November 2007 Suggestion Workshop paper availability on the DLR-HR website Joint reviewed paper to be submitted to IEEE TGARS in January 2008 Folie 30