NASA Workshop for Remote Sensing of Coastal & Inland Waters

Transcription

1 NASA Workshop for Remote Sensing of Coastal & Inland Waters Madison, Wisconsin June 20-22, 2012 Dr. Robert Shuchman, MTRI George Leshkevich, NOAA GLERL Contributors: Michael Sayers, MTRI Colin Brooks, MTRI 1

2 Great Lakes Algorithms -A Work in Progress- 2 2

3 Specific Great Lakes Optical Satellite Algorithm Suite Under Development CPA for chlorophyll (chl), dissolved organic carbon (doc) and suspended minerals (sm) Sediment plume model (extent, constituents, concentrations, and load) Primary productivity (daily, monthly, and annual averages) Lake bottom type (Cladophora/SAV, rocks, sand) Harmful Algal Blooms (HABs) Optical water properties (clarity, Kd, Photosynthetically Active Radiation (PAR), and photic zone) Wetlands mapping (combined optical + radar) 3

4 Status of Satellite Remote Sensing Algorithm Development Algorithm Retrieval Status Next Steps Remarks CPA Chl, doc, and sm concentrations Algorithm for each lake developed and tested (JGLR paper submitted); HO models updated Evaluation for CoastWatch operational production Algorithm outperforms standard OC3 chl approach & provides add l DOC & SM info Sediment Plumes Area extent, constituents, concentration, and load Algorithm developed and has undergone preliminary evaluation Quantify concentration and continue validation Addresses river plumes, bays and complex basins Primary Productivity Daily/monthly/yearly gc/m 2 /d or m or yr Algorithm is developed and undergoing evaluation Upgrade code to account for chl and temp and Kd variability within photic zone Existing algorithm using NASA Kd works well for spring and fall conditions Lake Bottom Mapping Bottom type (sand, SAV inc. Cladophora, or rock) in optically shallow water Fully developed and operational; Lakes Michigan & Huron done Collect additional truth to update biomass Mapping of Submerged Aquatic Vegetation (SAV) for Lakes Ontario and Erie underway HABs Location and extent Work in progress in collaboration with EPA GLRI, GLOS, NOAA GLERL Utilize GLERL Lake Erie 2011 dataset for algorithm evaluation (GLRI baseline) HABs mapping is utilizing knowledge gained from CPA and plume mapping Optical Water Properties Clarity (optically shallow water) Kd, PAR, photic zone Algorithm is operational presently using coastal ocean constants to calculate values Run additional time series and compare to ship based IOP Initial evaluation of algorithm indicates it is quite robust Wetlands NWI wetland type update for coastal Great Lakes (US + Canada) 4 year GLRI project in year 1 obtaining imagery, field data Use field data, existing data sources to start updated mapping work Combined SAR+optical algorithm 4

5 Lake Huron CPA EPA CHL CPA CHL CPA vs. OC3 CHL OC3 vs. Station t (ug/l) (ug/l) EPA (ug/l) (ug/l) EPA (ug/l) HU HU 45M HU HU HU HU HU 15M HU HU HU HU Averages Lake Huron CPA and OC3 chl retrievals for August 12, The red dots indicate the locations of the EPA sampling stations. In general the open Lake OC3 chl derived values are on the low side while the nearshore values are artificially high. August 2010 Lake Huron CPA and OC3 derived chl values compared to EPA in situ measurements for the stations indicated. Individual station values are presented along with differences and averages. A negative value indicates an under prediction. Note all the OC3 retrievals were underestimations when compared to the EPA observations. The CPA average chl observation compared quite favorably with the EPA truth. 5

6 MODIS Derived Saginaw Bay Sediment Plume 15 Apr 2009 TSSI_GL High Moderate 6

7 Lake Michigan Primary Productivity 7

8 Lake Michigan SAV/Cladophora In Lake Michigan, 24% of the visible bottom consists of SAV, mostly Cladophora (1024 km2 out of 4210 km2) Some areas of Chara, other diatoms The optical depth varied from 7m to 18m depth Conservative estimate of wet weight biomass is 375,000 metric tonnes Huron, Erie, Ontario coming Info site inc. mapping at: 8

9 MODIS Derived HABs Extent 09 Oct 2011 In collaboration with NOAA GLERL, GLOS, EPA GLRI EPA GLRI baseline maps Floating Algae Sediment Sediment/Algae Mix 9

10 Optical Example MODIS-Derived Lake Michigan Kd(490) 10

11 Optical Example MODIS-Derived Lake Michigan Kd(PAR) 11

12 Optical Example MODIS-Derived Lake Michigan Photic Depth 12

13 Sayers2 Optical Example Landsat-Derived Lake Michigan Kd 13

14 Slide 13 Sayers2 Change kd490 to simply KD Mike Sayers, 6/14/2012

15 Optical Example Landsat-Derived Lake Michigan Kd(PAR) 14

16 Optical Example Landsat-Derived Lake Michigan Photic Depth 15

17 Sayers3 Preliminary Lake Michigan Visibility Estimates from Water Bottom Visibility Depth (meters) Optical Depth K d K d (m -1 ) Kd(PAR) (m 1) KPAR Z EU (meters) 20 Photic Depth

18 Slide 16 Sayers3 Fix this figure Mike Sayers, 6/14/2012

19 Atmospheric Correction Example Lake Huron Atmospheric Correction Comparison White Aerosol Extraction Band NIR Black Pixel Remote Sensing Reflectance Standard Fixed Model Pair Fixed Model Pair NIR Iterative MUMM NIR Correction NIR/SWIR Switch No Aerosol Subtraction Wavelength (nm) In situ 17

20 Great Lakes Inherent Optical Properties Geospatial Database (GLIOPGD) Collection of the majority of optical properties of all the Great Lakes made from 1997 to the present during NOAA/EPA cruises Developed in partnership with NOAA/GLERL Database was essential to development of Color Producing Agent (CPA), Great Lakes Primary Productivity Model (GLPPM), plume and HABs algorithms Time-series of changing optical properties in the Great Lakes as a result of climate change, invasive species, and anthropogenic forcing can be generated GLIOPGD is used for algorithm validation Soon to be available to community at and other portals such as NOAA Coastwatch Incorporate into SeaBASS? 18

21 Ground-Truth/Calibration Issues (cont.) Share inherent optical properties large data set - with community Available soon Erie Ontario (m -1 ) c pg (440) Michigan a pg (440) Huron MI31B MI48B MI49B MI53B MI11 MI32 MI41 MI47 HU96B HU06 HU38 HU48 ER58 ER91M ER78M ER95B ER09 ON64B ON12 ON49 ON60 Station Example of CHL, FSS, VSS, TSS, CDOM, absorption, & attenuation data now stored in relational IOP geospatial database 19

22 Moving Forward in the Great Lakes with Algorithm Development Optimize MODIS HABs algorithm Utilize in situ values to calibrate plume model to provide TSS and TSM concentrations Validate Primary Productivity (PP) algorithm for Lakes Erie, Huron, Ontario, and Superior Estimate PP during Lake Stratification Continue optimizing atmospheric correction procedure Transition research algorithms to operational products (NOAA Coastwatch) 20