Atmospheric Correction: candidate improvements Erwin Wolters, Sindy Sterckx, and Stefan Adriaensen

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1 Atmospheric Correction: candidate improvements Erwin Wolters, Sindy Sterckx, and Stefan Adriaensen

2 Contents Current AOT and AC algorithm Overview AOT datasets and retrieval approaches Overview areas Results Implementation aspects Tasks + effort estimates Conclusions and recommendation 2 PROBA-V QWG 6, 9 10 November 2017, Redu

3 Current PROBA-V AOT retrieval methodology Retrieval Maisongrande et al. (2001) Uses BLUE and SWIR channels, exponential relation SWIR ~ NDVI TOA : R SWIR / R BLUE = 1.305exp(3.225*NDVI TOA ) 4 possible values: τ 550 = {0.05, 0.15, 0.30, 0.50} Ratio established for SPOT-VGT, not corrected for PROBA-V Executed per 8 8 pixels bilinear interpolation in between Retrieved for NDVI > 0.2 and R SWIR < 0.4 If no retrieval possible static AOT is used τ 550 = 0.2 cos φ 0.25 sin(φ + π 2 ) Time and longitude invariant 3 PROBA-V QWG 6, 9 10 November 2017, Redu

4 Current Atmospheric Correction: SMAC Simplified Model for Atmospheric Correction v4.2 TOA TOC reflectance Scattering and absorption expressed as analytical functions Continental aerosol model 4 PROBA-V QWG 6, 9 10 November 2017, Redu

5 AOT INTERCOMPARISON AOT datasets/ AOT retrieval approaches evaluated : Current operational approach (image based retrieval combined with latitude dependent AOT fallback) CAMS (Copernicus Atmospheric Monitoring Service) climatology CAMS NRT icor (image-based retrieval) Cisar (image-based retrieval) 5

6 Research questions: AOT sensitivity study Which of the proposed AOT datasets compares best against collocated AERONET AOT observations? How do differences between the proposed AOT datasets and the reference AOT propagate into differences in TOC reflectances for all PROBA-V channels, as well as for the TOC NDVI at segment level, when using the SMAC atmospheric correction algorithm? 6 PROBA-V QWG 6, 9 10 November 2017, Redu

7 icor AOT ICOR Approach already throughout validated for Sentinel-2 and Landsat-8 within ESA- NASA ACIX intercomparison. Implementation for Sentinel-3 on-going Modtran-5 based LUTs Extra icor functionalities for coastal waters (PV-LAC) AOT retrieval based on Guanter et al. (2008) Superpixels of 30 x 30 km 2 atmosphere invariant within area Dark target retrieval (max AOT boundary), refinement by end-member inversion Current implementation for PROBA-V: AOT LUTs computed for CENTER camera altitude dependent LUT For rural aerosol model Application to Level 2A segments (TOA reflectance) LUT extension for LEFT and RIGHT cameras ongoing 7 PROBA-V QWG 6, 9 10 November 2017, Redu

8 CAMS AOT NRT: based on CAMS / MACC-II re-analysis data Atmospheric composition forecast + CAMS aerosol model Upgraded version contains MODIS Deep Blue AOT + PMAp AOT 2 forecast initialisations (00 and 12 UTC), 5-day forecast Nominal resolution km 2, online interpolated to o Automatic download facilitated Aerosol microphysical properties available Climatology: based on CAMS / MACC-II re-analysis data Combination of modelling and observations Prapared for , to be extended Monthly mean AOT 550 at o o 8 PROBA-V QWG 6, 9 10 November 2017, Redu

9 CAMS NRT AOT upgrade Changes in atmospheric composition ~40% more organic aerosol, ~40% less sulphate AOT bias decrease: ~30% 10 15% Reduced AOT over dust areas Model resolution increased to ~40 x 40 km 2 Previous CAMS NRT Upgraded CAMS NRT 9 PROBA-V QWG 6, 9 10 November 2017, Redu

10 CISAR Coupled Inversion of Surface and AeRosols 3-layer RTM: FASTRE Multiple PROBA-V observations (16-day accumulation window) Joint retrieval of AOT and BHR (Bi-Hemispherical Reflectance ) per PROBA-V channel Optimal Estimation, including uncertainty characterisation Runs only on PROBA-V 1km data Currently only on single pixels v v s s System WV, O3 WV, O3 AOT h RPV parameters Ps 10 PROBA-V QWG 6, 9 10 November 2017, Redu

AERONET stations AERONET station Coordinates [lon, lat] Altitude [m] Site information Banizoumbou (Niger) 2.665, 13.541 250 Cultivated sandy area Beijing (China) 116.381, 39.

11 AERONET stations AERONET station Coordinates [lon, lat] Altitude [m] Site information Banizoumbou (Niger) 2.665, Cultivated sandy area Beijing (China) , Urban area; instruments located at research institute s rooftop Bure OPE (France) 5.505, Open grassland area near Hourdelaincourt Fowlers Gap (Australia) , Arid location in New South Wales Harvard Forest (MA, USA) , Located near deciduous forest Manaus EMBRAPA (Brazil) , About 30 km north of Manaus near tropical rain forest SEARCH Centreville (AL, USA) , Located near deciduous forest 11 PROBA-V QWG 6, 9 10 November 2017, Redu

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14 Methodology AOT validation AOT operational apprach (OP) AOT icor AOT Cisar AOT Aeronet (Level 2.0) AOT CAMS NRT (max time difference: +/- 30 min) AOT CAMS clim 14

15 AOT vs AERONET Operational icor CAMS NRT CAMS clim CISAR 15 PROBA-V QWG 6, 9 10 November 2017, Redu PROBA-V QWG 6, 9 10 November 2017, Redu

16 Validation statistics Accuracy (A, MBE) n A = 1 n i=1 ε i Ɛi=AOTdataset AOTAERONET Precision (P, urmse) P = 1 n 1 n i=1 (ε i A) 2 Uncertainty (U, RMSE) U = 1 n n ε 2 i i=1 Relative uncertainty (ru) ru = U m m indicates the average AOTAERONET 16 PROBA-V QWG 6, 9 10 November 2017, Redu

17 AOT statistics AOT dataset Accuracy Precision Uncertainty Relative Uncertainty Regression slope Regression intercept icor CAMS NRT CAMS climatology CISAR v OP Accuracy (A, MBE) A = 1 n n i=1 ε i Precision (P, urmse) P = 1 n 1 n i=1 (ε i A) 2 Uncertainty (U, RMSE) U = 1 n n ε i 2 i=1 Relative uncertainty (ru) ru = U m 17 PROBA-V QWG 6, 9 10 November 2017, Redu

SMAC H 2 O, O 3, angles TOC CISAR TOC OP TOC icor TOC CAMS

18 Methodology: impact AOT difference TOA+AOT CISAR TOA+AOT OP TOA+AOT icor TOA+AOT CAMS clim, NRT TOA+AOT AERONET SMAC H 2 O, O 3, angles TOC CISAR TOC OP TOC icor TOC CAMS clim, NRT TOC AERONET 18 PROBA-V QWG 6, 9 10 November 2017, Redu

19 BLUE TOC vs AERONET Operational icor CAMS NRT CAMS clim CISAR 19 PROBA-V QWG 6, 9 10 November 2017, Redu PROBA-V QWG 6, 9 10 November 2017, Redu

20 RED TOC vs AERONET Operational icor CAMS NRT CAMS clima CISAR 20 PROBA-V QWG 6, 9 10 November 2017, Redu

21 NIR TOC vs AERONET Operational icor CAMS NRT CAMS clima CISAR 21

22 SWIR TOC vs AERONET Operational icor CAMS NRT CAMS clima CISAR 23

23 NDVI TOC vs AERONET Operational icor CAMS NRT CAMS clima CISAR 24

24 Methodology SMAC vs icor TOA+AOT CAMS NRT H 2 O, O 3, angles TOA+AOT CAMS NRT H 2 O, O 3, angles SMAC icor TOC SMAC TOC icor 25 PROBA-V QWG 6, 9 10 November 2017, Redu

25 icor vs SMAC TOC reflectances Aerosol type relative contribution Water soluble Dust-like Soot ω 0.55 µm [-] icor (MODTRAN5) 70% 30% 0% SMAC (6S) 29% 70% 1% Different contributions of water soluble and dust-like aerosols Small soot contribution in 6S more absorption 26 PROBA-V QWG 6, 9 10 November 2017, Redu

26 icor vs SMAC TOC reflectance TOC BLUE TOC RED TOC NIR TOC SWIR TOC NDVI AOT: CAMS NRT H 2 O = 2.0 g cm -2 O 3 = 350 DU 27

27 PV-LAC: CISAR BHR vs MODIS BHR 28 PROBA-V QWG 6, 9 10 November 2017, Redu

28 Implementation icor Task Effort estimate (working days) Replace EstimateAerosol 3 Replace AtmosphericCorrection 3 GenerateL2ABWorkflow.java 7 EstimateAerosol.java 7 AtmosphericCorrection.java 7 ConvertToiCORInput.java 7 ConvertToL1ABOutput.java 7 Integrate 10 Module Testing 10 Integration Testing 10 TOTAL PROBA-V QWG 6, 9 10 November 2017, Redu

29 Implementation CAMS NRT / climatology Task CAMS NRT Effort estimate (working days) CAMS climatology Task Effort estimate (working days) Set-up and testing of automatic CAMS NRT download Interpolation to nominal PROBA-V resolution Extension of current monthly CAMS AOT climatology: data download + preparation + calculation + validation Investigate impact of missing CAMS NRT forecast data + implementation and testing of work-around Implementation and testing in atmospheric correction workflow Total estimated effort Investigate AOT daily/10 day climatology generation Implementation and testing of CAMS AOT climatology data in atmospheric correction processing workflow Total estimated effort PROBA-V QWG 6, 9 10 November 2017, Redu

30 Implementation CISAR Task Estimated effort (working days) Input data preparation and verification CISAR operational driver development and test Code speed-up Implementation in operational ground segment and documentation Evaluation Total estimated effort PROBA-V QWG 6, 9 10 November 2017, Redu

31 Summary and to be discussed AOT dataset/retrievals Overall CAMS NRT AOT closest agreement with AERONET AOT, icor gives also relative good relationship No correlation between operational AOT retrieval and AERONET Largest impact for the BLUE TOC reflectance and NDVI CISAR:BHR very promising relative to MODIS BHR Discussion items AOT from external dataset or retrieval from observations? What about SMAC? CAMS NRT stable dataset? CAMS climatology monthly/dekadal climatology? Replace AOT + replace AC what is feasible within budgetary constraints Same AC for 1 km, 300m, 100m? 32 PROBA-V QWG 6, 9 10 November 2017, Redu

Improvement of optical depth relations to PM2.5 concentrations using lidar derived PBL Heights

Improvement of optical depth relations to PM2.5 concentrations using lidar derived PBL Heights S. Chaw, Y. Wu, B. Gross,F. Moshary, S. Ahmed Optical Remote Sensing Laboratory City College of New York,