It can be done by regular optical means, although getting the auger out is probably easier!
below are a couple of abstracts and info. You need an understanding of synthetic aperture radar, and interferrometric techniques in general....There is also a publication listed from a group working at the Unversity of Buffalo where I am located dealing specifically with this issue
Determining depth and ice thickness of shallow sub-Arctic lakes using space-borne optical and SAR data
Authors: Duguay C.R.1; Lafleur P.M.2
Source: International Journal of Remote Sensing, Volume 24, Number 3, February 10, 2003, pp. 475-489(15)
Publisher:Taylor and Francis Ltd
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Abstract:
An approach to determine depth and ice thickness of shallow lakes and ponds using Landsat Thematic Mapper (TM) and European Remote Sensing (ERS)-1 Synthetic Aperture Radar (SAR) data is presented. A summertime Landsat TM image is used to map lake bathymetry and multi-date ERS-1 images acquired during winter are utilized to determine when and which lakes freeze to the bottom during winter. The two remotely sensed derived products are then combined to estimate ice thickness from lakes and ponds on a monthly basis. The approach has been developed and tested successfully in a sub-Arctic tundra-forest landscape in the Hudson Bay Lowland near Churchill, Manitoba, Canada. Lake depth estimates derived from Landsat TM band 2 generally compared well with measurements obtained in the field, especially in the tundra zone [rms error (RMSE) = 15 cm]. Maximum ice thickness estimates were also within the range of those typically measured during winter in this study area (tundra and forest-tundra zones 1.6 m; open forest zone 1.2 m). Results indicate that the approach is particularly well suited for estimating depth and ice thickness of shallow oligotrophic and ultra-oligotrophic lakes that are widespread in many regions above treeline. However, the results also suggest that the Landsat-based approach will require further testing and improvement if one wishes to map bathymetry for shallow lakes in which large nutrient concentrations or amounts of suspended sediments are found.
Document Type: Research article
Affiliations: 1: Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99775-7320, USA; e-mail:
[email protected] 2: Department of Geography, Trent University, Peterborough, Ontario, Canada K9J 7B8
Great Lakes CoastWatch Research and Product Development
George Leshkevich
Objectives-- Within Great Lakes CoastWatch, research and applications development utilizing imagery from new satellite sensors such as synthetic aperture radar (SAR) for ice classification and mapping and ocean color sensors such as the Sea Viewing Wide Field-of-View Sensor (SeaWiFS) for ocean color (chlorophyll) products complements the Great Lakes CoastWatch Program. These improvements are needed to enhance the Great Lakes CoastWatch product suite, to develop regional products and applications for the Great Lakes, and to contribute to the operational responsibilities of sister agencies such as the U.S. Coast Guard and National Weather Service.
Classified ERS-2 Scene (22 March 1997) Corrected for Power Loss and Local Incidence Angle Effect
:
1) SAR research and field experiment in FY2003 involving ground truth data collection on Lake Superior (concurrent with RADARSAT and ENVISAT overflight) for (a) validation of ice type classification algorithm for SAR data (b) development of interferrometric methods for ice thickness measurements (c) experimentation with ENVISAT dual polarized data for ice classification problem areas (d) validation of ice type classification algorithm for QuikSCAT scatterometer data.
2) Collaboration with researchers at the German DLR is being explored to investigate the application of a neural network based on a bio-optical model to the retrieval of chlorophyll, suspended sediment, and dissolved organic matter in the Great Lakes from satellite ocean color imagery (SeaWiFS or MODIS).
QuikSCAT scatterometer project: A site on Lake Superior (Granite Island near Marquette, MI) to mount a web cam to collect "ground truth" of ice type and movement was identified and installation of the camera took place during the 2002 winter season. Owing to a mild ice season last winter, no data was collected that season. As ice conditions were normal to severe this past (2002-03) ice season, except for a short outage in February, images were collected every 30 degrees in a 270 degree field of view every hour during daylight hours. The images will be used to "ground truth" QuikSCAT scatterometer imagery in the development of an ice classification and mapping algorithm using QuikSCAT data. (See report: Great Lakes Ice Mapping With Satellite Scatterometer Data).
Synthetic Aperture Radar (SAR) Research: RADARSAT data acquired during our 1997 field experiment has been acquired and calibrated by Satlantic, Inc. Analysis of this data using our library of C band polarimetric backscatter data is continuing, but the ice classification algorithm needs further validation, which is planned for this winter season, as last season was a very mild ice season.
Moreover, working cooperatively with scientists at JHU/APL, software to calibrate the RADARSAT ScanSAR Wide A data (block averaged) received at GLERL near real-time via the National Ice Center has been obtained from the Johns Hopkins University Applied Physics Laboratory and configured to run on a SUN computer at GLERL. The JHU/APL developed the software in their effort to derive and map coastal winds from RADARSAT data, where accurately calibrated data is also needed. Output converted to dB will be tested with our library of ice signatures. If successful, ice classified, color coded RADARSAT images will be put on the web this ice season for NWS and USCG use.
Great Lakes Winter Experiment 2002 (GLAWEX'02): The Great LAkes Winter EXperiment 2002 conducted by George A. Leshkevich (NOAA/OAR/GLERL) together with colleague Son V. Nghiem (NASA/JPL) with the participation of NASA's AIRSAR Science Team, Airborne Science DC-8 aircraft, and the U.S. Coast Guard icebreaker, USCGC Mackinaw, was successfully completed March 22, 2002. In over 40 hours of flight time, polarimetric and interferometric C, L, and P band Synthetic Aperture Radar (SAR) data was collected over various ice types/thickness in several locations on the Great Lakes along with concurrent in situ measurements of ice/snow type, ice thickness, and density. The ice/snow characteristics collected will be compared with the airborne AIRSAR data taken simultaneously. Data gathered during this experiment will be used to develop algorithms to map Great Lakes ice types and ice thickness using current and future satellite sensors. This information has applications in ice forecasting and modeling, climate and winter ecology research, hazard mitigation, as well as operational use.
2001 Accomplishments
Synthetic Aperture Radar (SAR)
RADARSAT data acquired during our 1997 field experiment has been acquired and calibrated by Satlantic, Inc. Analysis of this data using our library of C band polarimetric backscatter data is continuing, but the ice classification algorithm needs further validation, which is planned for this winter season. Our proposal to NESDIS to map/classify Great Lakes ice cover using QuikSCAT scatterometer data was funded. A site on Lake Superior to mount a web cam to collect "ground truth" of ice type and movement has been identified and installation of the camera is proceeding for use this winter season.
2000 Accomplishments
Synthetic Aperture Radar (SAR)
ERS-2 satellite SAR data acquired during the 1997 field experiment (together with the C-band ice backscatter measurements) was reprocessed using an improved calibration algorithm. The reprocessing using the new algorithm not only corrects for power loss (in the ERS-2 instrument) but also takes into account local incident angles (from 19.5 degrees to 26.5 degrees). The classified ERS-2 SAR image appears to be more accurate than the previous classification based on the ground truth data collected during the experiment, but the ice classification algorithm needs further validation, which is planned for the future.
Ocean Color
Collection of optical data and water samples continued to enhance the data base for statistical purposes. However, based on the previous analysis of measured chlorophyll data compared with output from four SeaWiFS chlorophyll algorithms (no atmospheric effects) and output from SeaDAS processed SeaWiFS satellite data (with atmospheric correction), it was found that returned chlorophyll values were highly variable in time and space. This could be due to what appears to be three coastal regimes in the Great Lakes: Case I (open ocean of second order), Case II (coastal), and Case III (highly turbid) and that chlorophyll is often not the major colorant. Although atmospheric correction algorithms have been improved, it appears that the chlorophyll ratioing algorithms themselves and not the atmospheric correction are the cause of the variable chlorophyll values (as the input remote sensing reflectance values were measured (in situ) values having no atmospheric affects).
Optical data (using Satlantic radiometer) and water samples (to obtain chlorophyll, TSM, DOC, POC) were collected on Lakes Superior, Michigan, and Erie for ground truth to allow ocean color algorithm development using SeaWiFS satellite data. This will lead to a remote sensing method for mapping chlorophyll distribution in the Great Lakes.
Shen, H., S. Nghiem, G.A. Leshkevich, and M. Manore. 1998. A summary of current remote sensing and modeling capabilities of the Great Lakes ice conditions. Understanding Great Lakes Issues, 98-11. Great Lakes Program, State University of New York at Buffalo, Buffalo, NY, 10pp.
Nghiem, S.V., G.A. Leshkevich, and R. Kwok. 1998. Satellite SAR Remote Sensing of the Great Lakes Ice Cover. Final Technical Report, JPL Project No. 56, NASA / Jet Propulsion Laboratory.
