Operational Ice Type Classification and Water Quality Satellite Retrievals for the Great Lakes

Transcription

1 Amercan Academy of Water Resources Engneers ( AAWRE) Applcatons of Remote Sensng ASCE elearnng Webnar Applcaton of Remote Sensng to Water Resources Engneerng Operatonal Ice Type Classfcaton and Water Qualty Satellte Retrevals for the Great Lakes George Leshkevch NOAA/Great Lakes Envronmental Research Laboratory george.leshkevch@noaa.gov December 7, 2017

2 Capablty Name: Satellte Retreval of Ice Type and Water Qualty Parameters for the Great Lakes Capablty Developer (NOAA fundng organzaton): OAR/Great Lakes Envronmental Research Laboratory Partnershps n Development: Ice Type: Dr. Son Nghem, Jet Propulson Laboratory Water Qualty: Dr. Robert Shuchman, MTU-Mchgan Tech Research Insttute Capablty Bref Descrpton/Status: Algorthms for the satellte retreval of ce type/thckness and water qualty parameters (CHL, CDOM, SM) are currently beng transferred to NESDIS for operatonal producton 2

3 Key capablty dstngushng nnovaton: Ice Type: Classfy Great Lakes ce types and thckness ranges usng satellte C-band SAR data that enhance/vsualze the operatonal U.S. Coast Guard ICECON ce severty ndex scale Water Qualty: Accurately classfy chlorophyll, CDOM, and suspended mnerals (CPAs) n the Great Lakes usng satellte data wth a non-ratong algorthm Early Successes (testbeds, feld tested, prototyped): Both algorthms based on lbrares of feld measured data and both algorthms feld tested and valdated wth mult-year n stu data 3

4 Addtonal Informaton: <Capablty Web Lnk> Major Users: U.S. Coast Guard Natonal Ice Center (NIC) Shppng Industry Ecosystem, Water Intake, Beach Managers Modelers, Forecasters Meda Publc 4

5 Advancng A Great Lakes Satellte SAR Ice Type Classfcaton Algorthm And Its Relaton To The Operatonal ICECON Rsk Assessment Tool G. Leshkevch 1, S. V. Nghem 2 1 Great Lakes Envronmental Research Laboratory Natonal Oceanc and Atmospherc Admnstraton (NOAA) 2 Jet Propulson Laboratory Calforna Insttute of Technology 5

6 Methodology for Great Lakes Ice Classfcaton Prototype Usng Satellte C-Band SAR Great Lakes Wnter Experment JPL Scatterometer used on USCG Macknaw Brash Ice Pancake Ice Patchy snow cover on snow ce over black ce Black ce wth patchy and rough snow cover Rough consoldated ce floes patchy snow cover New Black (Lake) ce USCG Macknaw n Whtefsh Bay Lbrary of backscatter sgnatures from dfferent ce types on Lake Superor measured usng Jet Propulson Lab C-band scatterometer durng Great Lakes Wnter Experment (GLAWEX97) 6

7 7 7

8 USCGC Macknaw Shp Track (red) Across Lake Superor - March 21-24, Drawn On MODIS True Color Image March 24,

9 9

10 Ice type classfcaton from Radarsat-2 mage March 20, 2014 Expermental Example 10

11 Proposed SAR Ice Type ICECON Scale 11

12 February 27,

13 Whtefsh Bay - March 22, 2017 MODIS RADARSAT-2 - ICECON Scale ICECON 0 (Water) ICECON 2 (Pancake Ice) ICECON 5 (Brash) ICECON 1 (New Lake Ice) ICECON 3/4 (Consoldated flows) (Lake ce) (Snow/snow ce/lake ce) Land 13

14 RADARSAT-2 SCWA-HH Green Bay March 3, 2016 Consoldated Floes Cat 3/4 ICECON Classfcaton Pancake Ice Cat 2 Consoldated Floes Cat 3/4 Pancake Ice Cat 2 14

15 An Operatonal Algorthm for the Retreval of Water Qualty Parameters n the Great Lakes from Satellte Data George Leshkevch NOAA Great Lakes Envronmental Research Laboratory Authors: George Leshkevch NOAA GLERL george.leshkevch@noaa.gov (734) Robert Shuchman MTRI shuchman@mtu.edu (734) Mke Sayers MTRI mjsayers@mtu.edu (734) Red Sawtell MTRI rwsawtel@mtu.edu (734) Karl Bosse MTRI krbosse@mtu.edu (734)

16 Motvaton for Case II Color Producng Agent Algorthm Ocean ratong algorthms lke OC3 do not work well n tme or space on the Great Lakes (Case II optcally complex water) where chlorophyll s not the only (and sometmes not the major) colorant. The Color Producng Agent Algorthm (CPA-A) s a semanalytcal nverse radatve transfer bo-optcal model to retreve water qualty parameters from satellte observed reflectance. The CPA-A requres knowledge of the nherent optcal propertes of a gven water body to produce accurate retrevals of the prmary color producng agents (CPAs) namely chlorophyll (CHL), suspended mneral (SM), and CDOM

17 Buldng Great Lakes Hydro-Optcal Models 20 years of IOP/AOP measurements usng the Satlantc, AC-S, and BB9 nstruments and concurrent water samplng on all the Great Lakes at all tmes of the season 17

18 CPA Algorthm Color Producng Agent (CPA) Algorthm Rrs a b a b (b H 2O, H 2O, C C chl chl a* b* chl, chl, C C sm sm /a a* b* ) (b sm, sm, a /a ) e 2 s( 443) C = Vector representng concentraton of each CPA a = Bulk absorpton coeffcent at band b = Bulk backscatterng coeffcent at band a* CPA, = Specfc absorpton coeffcent for each CPA at band b* CPA, = Specfc backscatterng coeffcent for each CPA at band Mnmze error - between modeled and measured Rrs S = Measured Remote Sensng Reflectance from satellte Rrs = Calculated remote sensng reflectance from CPA concentratons, HO-model Chlorophyll a 50-55% decrease 18 18

19 CPA-A Tme Seres Evaluaton n the Great Lakes Comparson between MODIS-derved OC3 and CPA-A chlorophyll product and NOAA/MTRI n stu chlorophyll measurements In stu data from Comparsons were made aganst satellte retrevals collected wthn 24 hours of n stu measurements CPA-A produces more accurate chlorophyll n both nearshore and offshore Good agreement for all fve lakes over varyng concentraton levels 19

20 CPA-A Outputs for Lake Mchgan 8 products (MODIS Aqua) Snapshots, weekly, and monthly products Some overlap wth MODIS OC3 products Data record dates to 2002 The dffuse attenuaton coeffcent (Kd) and photc depth are functons of CPA concentraton and are therefore nherently retrevable wth the CPA-A

21 Thank You For Your Attenton Journal of Great Lakes Research Specal Issue on Remote Sensng 21