Glassy, Joseph M.

Kim, Youngwook

Kimball, John S

McDonald, Kyle C.

English

University of Montana

University of Montana ScholarWorks at University of Montana Numerical Terradynamic Simulation Group Publications Numerical Terradynamic Simulation Group 2009 Global Mapping of Landscape Freeze-Thaw Status using Spaceborne Microwave Remote Sensing Youngwook Kim John S. Kimball University of Montana - Missoula Kyle C. McDonald City College of New York Joseph M. Glassy 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.; and Glassy, Joseph M., "Global Mapping of Landscape Freeze-Thaw Status using Spaceborne Microwave Remote Sensing" (2009). Numerical Terradynamic Simulation Group Publications. 367. https://scholarworks.umt.edu/ntsg_pubs/367 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. Global Mapping of Landscape Freeze-Thaw Status using Spaceborne Microwave Remote SensingYoungwook Kim1,2,*, J. S. Kimball1,2, K. C. McDonald3, and J. Glassy4(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, 91109(4)Lupine Logic, Inc., Missoula, MT, USA, 59812Surface air temperature data for FT_ESDR calibration and verification(1) NASA DAO (2000-2006) 6-hour reanalysis (1o x 1.25o)(2) NCEP/NCAR (NNR, 1988-2007) 6-hour reanalysis (1.875o x 2o) (3) NCDC (1988-2007): daily summary of the day from WMO weather stationsPassive ActiveSensor SSM/I AMSR-E SeaWindsPlatform DMSP Aqua QuikSCATFrequency 37GHz 36GHz 13.4GHz (Ku-band)Polarization V-pol V-pol V-pol/H-polResolution 25x25km 25x25km 15x25kmOverpass 6am/pm 1:30am/pm Daily averagePeriods 1988-2007 2003-2007 2000-2007IGBP land cover classification USGS 1km Res. Digital Elevation (DEM)AcknowledgementsThis work was performed at the Jet Propulsion Laboratory, California Institute of Technology, and at the University of Montana under contract to NASA (NNH06ZDA001N-MEaSUREs)Conclusions:-The various microwave sensors produce similar FT spatial-temporal patterns, with 72-93 % mean annual classification accuracy relative to NCDC stations, while global SSM/I F/T time series and corresponding annual frozen/non-frozen periods were quantified over a 20 year record; - Approximately 66 million km2 of the global land area are constrained by seasonally frozen temperatures; the seasonal progression of global frozen area ranges from 0.53 (±0.03) million km2 in July to 34.6 (±0.9) Million km2 in January. -The combined FT products show large transitional areas of opposing AM/PM FT states in spring and fall;-These results are being used to construct a consistent, systematic long-term (>20 yr) global daily record of F/T dynamics with well defined accuracy.- The FT_ESDR will be available online (http://freezethaw.ntsg.umt.edu/) and archived to the NSIDC DAAC.FT global accuracy assessment using NCDC stations:IN43C-1160Ancillary data for quality control (QC) assessmentData and Methods:The freeze-thaw (FT) status of the landscape is closely linked to surface energy budget and hydrological activity, vegetation phenology, terrestrial carbon budgets and land-atmosphere trace gas exchange. Spaceborne microwave radars and radiometers are ideally suited for global FT monitoring due to insensitivity to signal degradation by atmospheric contamination and solar illumination effects, are uniquely capable of detecting the distinct change in landscape dielectric properties between predominantly frozen and thawed states, and provide a surrogate measure of a range of biophysical processes associated with the F/T signal, especially at high latitudes. Introduction:F/T Algorithms:1Seasonal Threshold  Approach (STA)( )( ) frth frttσ σσ σ−∆ =− Jul)inTbSSM/I(meanstatereferencefrozennonJan)inTbSSM/I(meanstatereferencefrozen−==thfrσσ( )( )t Tt T∆ >∆ ≤ThawedFrozenExample pixel-wise dynamic Tb [K] change thresholds [T] derived using SSM/I P37V and NNR Tmx(above left) and SSM/I A37V and NNR Tmn (above right) in 2004; corresponding linear regression relationships for an example grid cell (at left).Global FT_ESDR domain:-defined by 7-year DAO reanalysis climatology using a 2cold temperature constraint index [CCI, days/yr];-encompasses all vegetated regions where low temperatures are a major constraint to ecosystem processes.≥>>−−≤=,TTif1,,TTTif,TTTT,TTif0,iTMmaxmnMminmnMmaxMminMmaxMminmnMminmnMin CCI = 365-iTMin Global FT_ESDR domain derived from CCI [days/yr] using DAO daily minimum air temperature climatology (2000-2006).TMmax and TMmin vary by major land cover class using a global Biome Properties Lookup Table (BPLUT)Open water fraction (Fw<20%)Seasonal pattern of daily mean global FT classification accuracy in 2004Combined SSM/IDOY=100 DOY=200 DOY=300 DOY=360Selected global daily FT patterns for 2004:Combined AMSR-ESpatial pattern of mean annual FT accuracy (%) relative to daily air temperatures from 3,701 NCDC stations in 2004SeaWindsDaily averageSSM/IPM overpass2004 annual non-frozen periodAMSR-EPM overpassSeaWindsDaily averageGlobal F/T classification:20-year SSM/I global non-frozen period variationcombined SSM/Icombined AMSR-ESeaWindsDaily averageUrban (13), Permenant ice/snow (15) & barren (16) areas were maskedQC MapThe FT_ESDR QC (DIM) map ranges from low (0) to best (1) quality based on sub-grid scale Fw & DEM heterogeneity within each 25km grid cell.Areas with Fw > 20% were maskedMean annual non-frozen periodVariation (Std Dev) in mean annual non-frozen periodMean FT seasonal cycle1Source: McDonald and Kimball, EHS 2004.2Source: Jolly et al, Global Change Biol 2005.- The STA based SSM/I and AMSR-E AM/PM F/T classifications are produced as discrete frozen (0) or non-frozen (1) daily values;-The combined SSM/I and AMSR-E F/T classifications are determined as frozen (0) or non-frozen (1) where the same F/T classification value occurs for both AM and PM retrievals, as daily output; as transitional (2) where AM frozen and PM non-frozen values occur, and inverse transitional (3) where AM non-frozen and PM frozen results occur; -The SeaWinds daily F/T classification (frozen or non-frozen) is derived using the BYU daily global sigma-0 browse product from QuikSCAT L1B data.Non-frozenFrozenInterannual variabilityTransitional

Global Mapping of Landscape Freeze-Thaw Status using Spaceborne Microwave Remote Sensing

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Global Mapping of Landscape Freeze-Thaw Status using Spaceborne Microwave Remote Sensing

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