Remote sensing of water quality: The development and use of water processors available in BEAM

Transcription

1 Remote sensing of water quality: The development and use of water processors available in BEAM Sampsa Koponen, Helsinki University of Technology (TKK)

2 Acknowledgments Roland Doerffer (GKSS, Germany) Carsten Brockmann, Marco Peters (Brockmann Consult, Germany) Kari Kallio, Timo Pyhälahti (SYKE, Finland) Antonio Ruiz Verdu (CEDEX/INTA, Spain) Thomas Heege (EOMAP, Germany) Kai Sorensen (NIVA, Norway) Page2

3 Contents Short introduction to remote sensing of water quality BEAM Data processors (MERIS LAKES & C2R, ICOL, FUB WeW) Current results Page3

4 Important concepts Optically significant substances (OSS) Chl a: concentration of Chlorophyll a TSM: concentration of total suspended matter (organic and inorganic) a CDOM (λ): absorption coefficient of Colored Dissolved Organic Matter λ can be e.g. 400 nm or 443nm A.k.a Gelbsoff or yellow substances Water quality constituents (WQC), optically active substance (OAS), Radiance: The radiant energy emitted per unit time in a specified direction by a unit area of an emitting surface (something that the remote sensing instruments typically measure) Remote sensing reflectance: Ratio of radiance and downwelling irradiance Makes observations made with different measurement conditions more comparable to each other Page4

5 Basics of remote sensing of water Sun E d,toa L TOA Blue Sky (E d,sky ) Satellite Sensor Radiance L TOA L TOA = t*l wl + L atm, t = atmospheric transmittance L atm Adjacency effect Wavelength Step 1: Atmospheric correction L wl Atmosphere Page5 E d,0 L wl In water OSS: Chl a, TSM, CDOM Land Radiance Wavelength Step 2: Reflectances to OSS (e.g. Bio optical modeling)

6 BEAM software package BEAM is a toolbox for viewing, analysing and processing of remote sensing data ( consult.de/beam/) Can handle data from: MERIS, AATSR, ASAR, ERS ATSR, MODIS, AVNIR, PRISM, CHRIS/Proba, Available for free Contains many useful tools and processors (not just water quality) Page6

7 Water processors in BEAM 4.2 C2R (Case 2 Regional) Developed using data from coastal waters Roland Doerffer (GKSS, Germany), Brockmann Consulting (Germany) MERIS LAKES Developed using data from lakes MERIS LAKES project team (Finland, Germany, Spain) Closely related to the C2R processor FUB WeW Case 2 waters Freie Universitaet Berlin (Germany) Page7

8 MERIS LAKES project Development of MERIS Lake Water Algorithms Funded by ESA Objective: Develop and validate BEAM plug in processors that convert topof atmosphere radiance values measured by the ENVISAT/MERIS instrument to data about lake water quality Started Jan. 2007, ended June 2008 Processors are publicly available (bundled into BEAM version 4.2) Documentation available: ATBDs on atmospheric correction and bio optical models Validation report (and validation protocol) Processor help files consult.de/beam wiki/display/lakes/home Page8

9 MERIS LAKES processors Lakes processors are based on the architecture of the C2R processor Modules for atmospheric correction and bio optical models Neural networks trained with simulated data New atmospheric correction module (common to all three processors) Uses bands 1 10, 12 and 13 as input Does not use extrapolation > no negative reflectances Two new bio optical neural networks: Boreal lakes (lakes typical in boreal forest region) Absorption by CDOM can be high (training range /m at 440 nm) Most are oligotrophic, but meso and eutrophic lakes exist as well Eutrophic lakes Optical properties dominated by phytoplankton Chlorophyll a concentrations can be high (training range mg/m 3 ) Plug in software for BEAM by Brockmann Consulting Page9

10 MERIS LAKES & C2R: Algorithm overview L1 TOA Radiances, O 3, Surface Pressure, Solar Irradiance (TOA) Atmospheric correction (AC) neural networks (NN) Optional adjacency effect correction (ICOL ) L_path Transmittance AOT Å coeff. Solar and observation angles R_Lw (water leaving reflectance) VAL BIO OPTICAL NEURAL NETWORKS Case 2 Regional (C2R) Boreal Lakes (BOR) Eutrophic Lakes (EUL) VAL Concentrations of Water quality variables, IOPs, quality indicator Page 10

11 MERIS LAKES & C2R: Training of a neural network for atmospheric correction Atmosphere optical model 1 Bio optical model NNforward water (based on Hydrolight simulations) RLw Selection Max sunglint Max tau_aerosol Min. Rlw(560) Etc. 12 Transmittance L_up 10 MC code 2 R. Doerffer (GKSS) RLpath_noglint RLpath_glint Ed_boa Tau_aerosole RLpath Ed_boa Tau_aerosole Optional Polarisation correction RLtosa RLpath Ed_boa RLw Tau_aerosole 11 Training & Test data set 13 Page 11

12 MERIS LAKES & C2R: The atmospheric correction NN Input Output RL_tosa 12 bands sun zenith x y z Neural Network Tau_aerosol 412, 550, 778, 865 Sun_glint ratio a_tot, b_tot 12 MERIS bands Trans tosa surface trans_ed Path radiance reflectance RL_path RLw errcode R. Doerffer (GKSS) RLw(θ,φ) =Lw (θ,φ) /Ed Page 12

13 MERIS LAKES & C2R: Bio optical NN with optimization r, r log of reflectances c log of concentrations g geometry information q quality indicator NN input NN output r adjust c r invnn q q g g c forwnn r c c R. Doerffer (GKSS) iterate: Min! Page 13

14 Summary of Lake/C2R processor output products Water leaving radiance reflectance, R_Lw (12 bands) Water quality Chlorophyll concentration (Chl a) Total Suspended Matter concentration (TSM) CDOM (gelbstoff) absorption coeff (a_gelb) Atmospheric parameters Path radiance reflectance, R_Lpath (12 bands) Transmittance (12 bands) Aerosol optical depth (AOD) Angstrom coefficient IOPs Scattering coefficient of TSM at 443 nm (b_tsm) Phytoplankton absorption coeff. at 443 nm (a_pig) Total absorption coeff. at 443 nm (a_total) Other Min. Irrad. attenuation coeff. (K_min) Signal depth (Z_90_max) Chi_square Page 14

15 FUB/WeW Water Processor Wind speed Surface pressure Sun and observation geometries TOA Radiances (MERIS Level 1b) U.S Standard Atmosphere Constant ozone loading 344DU Training vectors Rayleigh Ozone Neural Network mixture: maritime& continental H 2SO 4 aerosol types (Mie calculations) Atmospheric correction Neural Network Training vectors Wind speed (1.5 and 7.2 m/s) Air pressure (980 and 1040hPa) Ocean Model Absortion and Scattering of: Pure water CDOM SPM 8 Reflectances at mean sea level (MSL) Bands 1 7 and 9 Aerosol Optical Thickness (AOT) 440 nm 550 nm 670 nm 870 nm Geometry parameters Surface pressure Wind speed Retreival Algorithm NN Log(CHL) Log(TSM) Log(YEL) For Validation purposes: MSE(a) = 0.05 MSE (b) = Page 15

16 Summary of FUB/Wew Water Processor output products (MSL) bands 1 7,9 water leaving RS 412 nm (1/sr) water leaving RS 442 nm (1/sr) water leaving RS 490 nm (1/sr) water leaving RS 510 nm (1/sr) water leaving RS 560 nm (1/sr) water leaving RS 620 nm (1/sr) water leaving RS 665 nm (1/sr) water leaving RS 708 nm (1/sr) (AOT) aerosol optical 440 nm aerosol optical 550 nm aerosol optical 670 nm aerosol optical 870 nm WATER QUALITY chlorophyll a concentration (log scale, mg/m^3) yellow substance 443 nm (log scale, 1/m) total suspended matter concentration (log scale, g/m^3) Page 16

17 ICOL (Improved Contrast between Ocean and Land) Processor that reduces the Adjacency Effect (AE) in MERIS images Estimates atmospheric parameters Computes new TOA radiances that do not include AE due to aerosol and Rayleigh scattering Available in BEAM through the Module Manager Santer R., Zagolski F., and Gilson M. (2007). Université du Littoral, France, ICOL ATBD, Version 0.1, Feb. 28, ICOL 2 being negotiated with ESA Faster processing More scientific Page 17

18 MERIS LAKES validation areas Finland 4 lakes Germany Lake Constance Spain 5 lakes Egypt Lake Manzalah Lake Victoria Finnish lakes (SYKE & TKK) Low to moderate Chl a Moderate to high CDOM Lake Constance (EOMAP) Low Chl a Areas of moderate TSM Spanish reservoirs (CEDEX) Moderate to very high Chl a Cyanobacteria frequent Lake Manzalah (SYKE, C CORE) Shallow Moderate to high Chl a Lake Victoria (NIVA) Moderate Chl a Surface plants Validation campaigns Data existing prior to the project Page 18

19 Validation measurements in Europe AOPs Above and in water radiometry (ASD, Ramses) IOPs in situ Absorption and scattering (Wetlabs ac s, ac 9) Backscattering (ECO bb9) IOPs in laboratory Filter pad method WQ constituents: Chl a (HPLC or spectrophotometric) TSM (gravimetric) CDOM (spectrophotometric) Atmosphere Cimel 318NE in situ Aeronet Page 19

20 Validation campaigns and lakes in Finland June 4, and Aug 7, 2007: Lake Vesijärvi: Mesotrophic lake, low CDOM concentration. Lake Päijänne: Large and oligotrophic lake Lake Pääjärvi: Humic lake (high CDOM) Pyhäjärvi Pääjärvi Päijänne Vesijärvi Aug 23, 2007: Lake Säkylän Pyhäjärvi: Mesotrophic lake Helsinki # Kilometers Page 20

21 MERIS data from Finnish campaigns June 4, 2007 Aug 7, 2007 Aug 23, 2007 Page 21

22 Water leaving reflectance with in situ data and MERIS observations with and without ICOL Lake Päijanne region June 4, 2007 R water leaving 7 x S1 BOR S2 BOR S3 BOR S1 ICOL BOR S2 ICOL BOR S3 ICOL BOR S1 insitu S2 insitu S3 insitu Wavelength (nm) Page 22

23 Water leaving reflectance with in situ data and MERIS observations with and without ICOL Lake Päijanne region Aug 7, 2007 R water leaving 6 x S1 BOR S2 BOR S3 BOR S1 ICOL BOR S2 ICOL BOR S3 ICOL BOR S1 insitu S2 insitu S3 insitu Wavelength (nm) Page 23

24 Water leaving reflectance with in situ data and MERIS observations with and without ICOL Lake Pyhäjärvi Aug 23, 2007 R water leaving 7 x P1 BOR P2 BOR P3 BOR P1 ICOL BOR P2 ICOL BOR P3 ICOL BOR P1 insitu P2 insitu P3 insitu Wavelength (nm) Page 24

25 Relative error of Rwl with and without ICOL Error = R insitu R R insitu MERIS 100% 100 Boreal only vs. in situ 100 Boreal & ICOL vs. in situ Relative error (%) Wavelength (nm) June 4 S1 June 4 S2 June 4 S3 Aug. 7 S1 Aug. 7 S2 Aug. 7 S3 Aug. 23 P1 Aug. 23 P2 Aug. 23 P3 Relative error (%) June 4 S1 June 4 S2 June 4 S3 Aug. 7 S1 Aug. 7 S2 Aug. 7 S3 Aug. 23 P1 Aug. 23 P2 Aug. 23 P Wavelength (nm) Page 25

26 AOD 550 In situ data from a station approx. 70 km from Lake Säkylän Pyhäjärvi (Aug 23) and approx. 110 km from Lake Päijanne region (June 4 and Aug 7) Date In situ Boreal & ICOL Boreal only S S S Paa S S S Paa P P P P Elevated values caused by a thin cloud. Page 26

27 IOPs with Boreal processor MERIS a btsm at 442 nm (m 1 ) MERIS 0.4 a pig at 442 nm (m 1 ) Processor Boreal + ICOL a btsm(442) a ph(442) Regression equation y = 0.98x 0.02 y = 0.78x 0.13 R N 7 7 MERIS MERIS b tsm In situ 3 a gelb at 442 nm (m 1 ) In situ b tsm (m 1 ) In situ b tsm 440 MERIS atot In situ atot (m 1 ) In situ atot 440 o = Boreal + ICOL * = Boreal without ICOL a cdom(442)* y = 0.174x a tot(442)* y = 0.74x b tsm(442) y = 1.25x Boreal a btsm(442) y = 0.58x a ph(442) y = 0.52x a cdom(442)* y = 0.035x a tot(442)* y = 0.22x b tsm(442) y = 0.51x * Without the two in situ measurements of the humic Lake Pääjärvi. Page 27

28 Boreal bio optical model & Finnish in situ reflectances a_gelb Boreal NN a gelb (1/m) In situ a gelb (1/m) Page 28

29 Concentrations (Boreal without ICOL) Symbols: o = , + = , = Boreal only Chl a Slope = Bias = 4.08 R 2 = In situ a gelb Boreal only Slope = 0.4 Bias = 0.21 R 2 = TSM In situ Page 29

30 Concentrations (Boreal with ICOL) Symbols: o = , + = , = Boreal & ICOL Chl a 10 Slope = 3.01 Bias = R 2 = In situ a gelb Boreal & ICOL Slope = 1 Bias = 0.7 R 2 = TSM humic lake In situ Page 30

31 Transect data in Finland A flow through devise installed on a boat Pumps water through a measurement system as the boat moves Chl a, turbidity, a CDOM, nutrients, cyanobacteria, temperature, Transect route at Lake Säkylän Pyhäjärvi on Aug. 23, 2007 Can collect several thousand data points during a day (on Aug 23, 2007: ) Interpolated Chl a map Page 31

32 TSM a with Boreal processor with and without ICOL In situ transect data 5 ICOL Boreal & ICOL 5 No ICOL Boreal only 4.5 June 4 (blue) N = June 4 (blue) N = 84 4 Aug. 7 (red) N = 57 4 Aug. 7 (red) N = 107 MERIS TSM (mg/l) Aug. 23 (green) N = 181 All R 2 = All slope = All bias = 0.27 RMSE = MERIS TSM (mg/l) Aug. 23 (green) N = 322 All R 2 = All slope = All bias = RMSE = In situ TSM (mg/l) In situ TSM (mg/l) Page 32

33 Chl a with Boreal processor with and without ICOL In situ transect data 35 ICOL Boreal & ICOL 10 June 4 (blue) N = 84 No ICOL Boreal only MERIS Chl a (µg/l) June 4 (blue) N = 89 Aug. 7 (red) N = 57 Aug. 23 (green) N = 181 All R 2 = All slope = 4.66 All bias = 5.29 RMSE = 3.42 MERIS Chl a (µg/l) Aug. 7 (red) N = 107 Aug. 23 (green) N = 322 All R 2 = All slope = All bias = 2.19 RMSE = In situ Chl a (µg/l) In situ Chl a (µg/l) Page 33

34 Chl a with Eutrophic (0.92) processor with and without ICOL In situ transect data chisquare < 0.2 Ångtröm > 0.75 chisquare < 0.1 Ångtröm > ICOL Eutrophic & ICOL 12 No ICOL Eutrophic only June 4 (blue) N = 73 MERIS Chl a (µg/l) Aug. 7 (red) N = 79 Aug. 23 (green) N = 395 All R 2 = All slope = 0.99 All bias = 3.19 RMSE = MERIS Chl a (µg/l) June 4 (blue) N = 87 Aug. 7 (red) N = 90 Aug. 23 (green) N = 402 All R 2 = All slope = 0.31 All bias = 3.85 RMSE = In situ Chl a (µg/l) In situ Chl a (µg/l) Page 34

35 Data statistics with Boreal and Eutrophic (transect data) Chl a TSM Slope Bias R 2 RMSE In Situ Mean Slope Bias R 2 RMSE In Situ Mean Boreal Boreal & ICOL Eutrophic Eutrophic & ICOL ICOL improves R 2 in all cases ICOL has large effects on the slope values. Page 35

36 TSM on without ICOL (Boreal) Light gray = land Dark gray = level 2 invalid flag Page 36

37 TSM on with ICOL (Boreal) Light gray = land Dark gray = level 2 invalid flag Page 37

38 TSM map derived with the Boreal processor (+ICOL) Finland, June 4, S1 S2 In situ MERIS Thin cloud/ aircraft contrail S Paa Light gray = land Dark gray = Level 2 invalid flag Page 38

39 TSM map derived with the Boreal processor (+ICOL) Finland, Aug. 7., 2007 S1 S2 S3 Paa In situ BOR Light gray = land Dark gray = Level 2 invalid flag Page 39

40 Chl a map derived with the Eutrophic processor (no ICOL) Finland, Aug. 7., 2007 S1 S2 S3 Paa In situ MERIS Light gray = land Dark gray = ootr and wlr_oor flags Page 40

41 Chl a map derived with the Eutrophic processor (+ICOL) Finland, Aug. 7., 2007 S1 S2 S3 Paa In situ MERIS Light gray = land Dark gray = ootr and wlr_oor flags Page 41

42 Chl a map derived with the Eutrophic processor (+ICOL) Finland, June 4., 2007 In situ EUL S Thin cloud/ aircraft contrail S2 S3 Paa Light gray = land Dark gray = ootr and wlr_oor flags Page 42

43 Effect of ICOL preprocessing on CHL estimation in Finland (August 23, 2007, Eutrophic lakes processor) No ICOL Chl a in µg/l Water sample stations S1 In situ 5.3 EUL 5.1 EUL & ICOL 7.5 S S S Light gray = land Surface sample (0 3 cm) from S5 has Chl value 15 Dark gray = level 2 invalid flag 10 9 No ICOL ICOL ICOL 8 7 Chl a (ug/l) Page Pixel number Transect line pixel number

44 Effect of ICOL preprocessing on TSM estimation in Finland (August 23, 2007, Boreal Processor) No ICOL TSM in mg/l P1 P2 P3 P5 Water sample stations In situ Boreal Boreal & ICOL km ICOL No ICOL ICOL TSM (mg/l) Transect line Pixel pixel number number Page 44

45 Atmospheric correction Spain & Germany CUERDA DEL POZO, STATION A, RLw (sr 1) RLw (sr 1) ALMENDRA, IN SITU STATION A, EUL ICOL+EUL Wavelength (nm) RS Reflectance (+0) IN SITU EUL ICOL+EUL Chl a: Wavelength (nm) Station LA April , Lake Constance C2R v125, 9 pixels ICOL C2R, 9 pixels RAMSES radiometer wavelength [nm] Page 45

46 Combined results (OSS) TSM TSM (mg/l) TSM (mg/l) y = 2.33x 1.47 R 2 = y = 1.28x 0.65 R 2 = 0.64 MERIS C2R MERIS BL 4.0 GER 2.0 FIN SPA IN SITU 2.0 GER FIN SPA IN SITU C2R PROCESSOR BOREAL PROCESSOR Page 46

47 Combined results (OSS) Chl a MERIS C2R Chl a (mg/m 3 ) 18.0 y = 2.21x R 2 = GER FIN SPA IN SITU MERIS EUL Chl a (mg/m 3 ) y = 1.26x R 2 = GER 2.0 FIN SPA IN SITU C2R PROCESSOR EUTROPHIC PROCESSOR Page 47

48 Lake Victoria km 2 Altitude 1000 m Eutrophic Lake High concentration of cyanobacteria Floating water plants Page 48

49 Average Chl a (mg/m3) in Lake Victoria in August September 2005 based on EUL processor Red dots are Chl a in situ values Black dots are calulated from Secci Disc Depth Chl_Conc EUL mg/m Chl_conc = 1.602* Chl a_fl 3.13 R2 = Chl a (mg/m 3 ), Measured as Chl a fluorescence Page 49

50 Main characteristics of Lake Manzalah, Egypt Complex lake system with several inter connected basins Very shallow Large areas of aquatic vegetation Water quality values are from the measurement campaigns in 2007 (total number of stations: 40). Area (km 2 ) Mean depth (m) Maximum depth (m) Mean Chl a ( g/l) [min max] Mean Turbidity (NTU) [min max] (maximum of the in situ data) 29.8 [ ] 20.0 [0.4 98] Page 50

51 Lake Manzalah, Egypt Eutrophic processor with and without ICOL Chlorophyll estimate vs. in situ 25 y = x R 2 = TSM estimate vs. in situ turbidity CHL, satellite (mg/m^3) y = x R 2 = TSM, satellite (g/m^3) R 2 = R 2 = CHL in situ (mg/m^3) CHL (Eutrophic ICOL ) CHL (Eutrophic no ICOL ) TUR (turbidity) in situ (NTU) Nearly all pixels were flagged out of scope for water concentration estimation TSM (Eutrophic ICOL ) TSM (Eutrophic no ICOL ) Page 51

52 Lake Manzalah: TSM eastern part Without ICOL With ICOL Areas where atmospheric correction failed are masked All water quality pixels Page 52

53 Chl a without (left) and with ICOL (left) ICOL improves performance slightly near shore, but the effect is not as visible as with TSM. Page 53

54 Chl a in the Gulf of Finland on May 8, 2006 (Spring Bloom) with various processors without ICOL Boreal Eutrophic C2R FUB/WeW Page 54

55 Chl a in the Gulf of Finland on May 8, 2006 (Spring Bloom) with various processors with ICOL Boreal Eutrophic C2R FUB/WeW Page 55

56 Chl transect location May 8, 2006 Page 56

57 Chl a transect in the Gulf of Finland on May 8, 2006 Page 57

58 Summary C2R/MERIS LAKES: Atmospheric correction Good agreement in green red bands Larger errors in very clear, humic or high chl a lakes General improvement with ICOL C2R/MERIS LAKES: Estimation of water constituents Linear response, some over and underestimations Acceptable errors at moderate concentration ranges Larger errors in turbid lakes, humic lakes and very clear lakes Likely reasons for errors in the estimation are the errors in the atmospheric correction Estimation of water quality is possible, however the processors are still experimental Improvements still needed Page 58