Kim, Youngwook

Kimball, John S

Lucotch, J.

McDonald, Kyle C.

Zhang, K.

English

University of Montana

University of Montana ScholarWorks at University of Montana Numerical Terradynamic Simulation Group Publications Numerical Terradynamic Simulation Group 2009 Terrestrial Freeze-Thaw Monitoring in the Northern Hemisphere using Satellite Active and Passive Microwave Remote Sensing Youngwook Kim John S. Kimball University of Montana - Missoula Kyle C. McDonald City College of New York K. Zhang J. Lucotch University of Montana - Missoula Follow this and additional works at: https://scholarworks.umt.edu/ntsg_pubs Let us know how access to this document benefits you. Recommended Citation Kim, Youngwook; Kimball, John S.; McDonald, Kyle C.; Zhang, K.; and Lucotch, J., "Terrestrial Freeze-Thaw Monitoring in the Northern Hemisphere using Satellite Active and Passive Microwave Remote Sensing" (2009). Numerical Terradynamic Simulation Group Publications. 373. https://scholarworks.umt.edu/ntsg_pubs/373 This Poster is brought to you for free and open access by the Numerical Terradynamic Simulation Group at ScholarWorks at University of Montana. It has been accepted for inclusion in Numerical Terradynamic Simulation Group Publications by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact scholarworks@mso.umt.edu. Terrestrial Freeze-Thaw Monitoring in the Northern Hemisphere using Satellite Active and Passive Microwave Remote SensingYoungwook Kim1,2,*, J. S. Kimball1,2, K. C. McDonald3, K. Zhang1,2 , and J. Lucotch1,2(1)Flathead Lake Biological Station, The University of Montana 32125 Biostation Lane, Polson MT. USA, 59860-9659. *Corresponding author: youngwook.kim@ntsg.umt.edu(2)Numerical Terradynamic Simulation Group, The University of Montana, Missoula, MT, USA, 59812(3)Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena CA. USA, 91109Introduction:Approximately 50 million km2 of the terrestrial Northern Hemisphere undergoes seasonal freeze-thaw (F/T) transitions each year. The timing and duration of  landscape F/T processes are closely linked to surface energy budget and hydrological activity, vegetation phenology, terrestrial carbon budgets and land-atmosphere trace gas exchange. Satellite microwave remote sensing is relatively insensitive to signal degradation by atmospheric contamination and solar illumination effects and is uniquely capable of detecting and monitoring a range of biophysical processes associated with the F/T signal, especially at high latitudes. Datasets:(3) Comparison between SSM/I F/T and AVHRR/MODIS NPP onset/offset(4) Multi-sensor F/T classification comparisonAnnual F/T timing (DOY) for 2006 from SSM/I, AMSR-E and SeaWinds:Cold temperature constraints, LC, and topographic variability:Surface air temperature data(1)National Climate Data Center (NCDC, 1988-2007): daily summary of the day from in situ weather stations(2) Pixel-to-pixel comparison with NNR air temperaturesCNV STAThawFreezeAccuracy (%)(1) Pixel-to-point comparison with NCDC air temperaturesCNVSTAThaw Freeze Unfrozen periodCNV STAThawFreezeAccuracy (%)F/T Algorithms:- Microwave sensors are able to observe freezing and thawing of  landscape has its origin in the distinct changes in landscape dielectric properties that occur during transitions between solid and liquid phases of water.( )( ) frth frttσ σσ σ−∆ =−( )( )t Tt T∆ >∆ ≤ThawedFrozen( ) '( ) ( )CNV t f x t x dxσ∞−∞= −∫(2) Temporal Edge Detection Approach (CNV)(1) Seasonal Threshold Approach (STA)(2) NCEP/NCAR (NNR, 1988-2007) : 6-hour reanalysis with 1.875 x 2 degree resolution SSM/IAMSR-EThawSeaWindsFreezeTemperature Constraint = 365 – iTmin; Jolly et al. 2005Standard deviation of DEM within 25kmx25km; criteria by Rawlins (2005)Percentage of major land cover type within 25kmx25km from AVHRR 1 kmfrozenprimaryCNVMinthawedprimaryCNVMaxsensorPassive:)(:)(Low cold constraintsMedium cold constraintsStrong cold constraintsLow land cover homogeneityMedium land cover homogeneityHigh land cover homogeneityHigh DEM homogeneityMedium DEM homogeneityLow DEM homogeneityJulinmeanJaninmeanfrfr==σσThawFreezeCNV STA CNV STA CNV STACold temperature constraints Land cover homogeneity DEM heterogeneityPassive ActiveSensor SSM/I AMSR-E SeaWindsPlatform DMSP Aqua QuikSCATFrequency 37GHz 36GHz 13.4GHzPolarization H-pol H-pol H-polResolution 25x25km 25x25km 15x25kmOverpass 6pm 1:30pm Daily averageIGBP land cover classification Digital Elevation Model (DEM)AcknowledgementsThis work was performed at the Jet Propulsion Laboratory, California Institute of Technology, and at the University of Montana under contract to the National Aeronautics and Space Administration.Conclusions:- Satellite F/T classification accuracy ranges from ~40-60% and is higher and lower during respective thaw and freeze transitions; classification accuracy is similar for different sensors and from year-to-year;- Thaw transition generally coincide with growing season timing and length defined by independent satellite (MOD17) derived NPP time series;- Annual mean F/T dates from SSMI and AMSR-E shows similar F/T patterns, while those from SeaWinds displays earlier thaw timing in STA (later in CNV) and earlier freeze timing in both algorithms;- The STA and CNV algorithms produce similar F/T patterns and time series, through STA shows earlier thaw timing than CNV;-F/T classification accuracy is generally improved over regions with medium cold temperature constraints, while F/T accuracy is mostly consistent with respect to LC and terrain heterogeneity.CNV STACold temperature constraints (>50)Long-term global record of F/T dynamics for all vegetated regions where low temperatures are a major constraint to ecosystem processes are constructedAccuracy assessment of Terrestrial F/T products:SSM/IAMSR-ESeaWindsCNV STAThawed Date (CNV)Frozen Date (CNV)At T=0.5, first F/T dates have been selectedF/T timing from three sensors are distributed similarly. Most differences between two algorithms occur in the lower latitude, resulting in earlier F/T timing in STA.Accuracy computation from NNR air temperature)(05.0,8306.0)(05.0,4367.022STAPRCNVPR<=<=)(466.0,0359.0)(327.0,0641.022STAPRCNVPR====)(05.0,4437.0)(05.0,2428.022STAPRCNVPR<=<=Lat=60.35NLon=166.0ELC=closed shrubSSM/I

Terrestrial Freeze-Thaw Monitoring in the Northern Hemisphere using Satellite Active and Passive Microwave Remote Sensing

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Terrestrial Freeze-Thaw Monitoring in the Northern Hemisphere using Satellite Active and Passive Microwave Remote Sensing

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