RADIANT SURFACE TEMPERATURE OF A DECIDUOUS FOREST – THE EFFECTIVENESS OF SATELLITE MEASUREMENT AND TOWER-BASED VALIDATION

RESEARCH OBJECTIVES  Assess (allegedly improved) accuracy of radiant land surface temperature (LST) derivation via split-window (SW) algorithm  Identify appropriate validation instrumentation for deciduous forest  Compare long-term continuous LST and air temperature patterns from tower data

METHODOLOGY  Derive AVHRR LST using Qin algorithm using radiosonde profile input - AVHRR imagery concurrent with radiosonde (Zutter 2002) - derive LST for tower pixel and 3X3 window - emissivity from NDVI method and reference values  Compare AVHRR LST to tower radiometer (CG3) LST and air temperature - 71 images over 19 dates - comparison to 46 m tower radiometer, 22 m and 2 m air temp. - identical emissivity values used for 46 m CG3 data  Compare tower radiometer LST to air temperature over various temporal scales - primary comparison of 2001 data (limited 2000 comparisons) - arbitrary selection of 0.98 emissivity for all CG3 data - CG3 46 m and 22 m air temp; CG3 2m and 2 m air temp.

IMAGE PROCESSING/DATA EXTRACTION  Visual cloud clearing  Scan angle extracted from pixel number  Panoramic distortion correction  Radiometric correction - DNs converted to radiance - non-linearity correction  Rectification - performed on small subset images - grid points referenced to Lake Lemon  Selection of 3X3 pixel window centered on tower  Radiance values of 9 pixels exported to ASCII files for processing

ALGORITHM INPUTS  Scan angle  Columnar Water vapor, g/cm 2  Emissivity (1)Scan angle – from individual images (2)Water vapor - calculated with LOWTRAN7 - corrected temperature/humidity data from Zutter (2002) radiosondes - default profiles above top of Zutter profiles - rural aerosol extinction profile (23 km visibility) - nighttime images matched to earliest AM radiosonde

ALGORITHM INPUTS cont’d (3) Emissivity - derived in part as function of NDVI (Sobrino et al. 2001) - transition spring/fall images eliminated - leaf-out images implicate max emissivity = winter images – used modeled reference values (Snyder et al. 2001) of for Ch. 4, for Ch. 5; equivalent to ~ NDVI of 0.3

TOWER DATA PROCESSING  Aberrant data hand corrected from visual inspection  No replacement/interpolation of missing data  Calculated daily averages (1) concurrent data only and (2) independent  Comparisons made of 15-minute data, daily and monthly averages minute Air Temperature Data Uncorrected Corrected

AVHRR TEMPERATURE COMPARISONS AVHRR/CG3 46 m AVHRR-CG3 46 m temp. difference

CG3 46 m-Air temp. 22 m 15-min. data Temp. difference 2001 CG3 LST/AIR TEMPERATURE COMPARISONS CG3 46 m-Air Temp. 22 m Daily Mean Difference 2001

CG3 46m CG3 2m 2001 Tair 22 mTair 2 m AVHRR K-2.24 K-1.92 CG3 46m #1 2001* 0.54 K2.25 K CG3 46m # (Day 1-201)* 2.45 K CG3 46 m #2 2000* 0.34 K CG3 2 m 2001*2.24 K SUMMARY OF TEMPERATURE COMPARISONS MEAN TEMPERATURE DIFFERENCES, ROW MINUS COL. * 15-minute data

STEP CHANGE IN CG3 DATA – DECEMBER 2000 Evident in both CG3s at 46 m CG3 – Tair 22m CG3 #1 – CG3 #2 Difference

SYNTHESIS OF TEMPERATURE COMPARISONS  CG3 46 m & Tair 22 m are similar to within <0.5 K (from 2000 data)  AVHRR is substantially (~ 2 K) less than both CG3 46 m and Tair 22 m  Large positive bias exists in the 2001 CG3 data (both 46 m and 2 m)  CG3 46 m and Tair 22 m may be comparable long term climate variables  Absent negative AVHRR bias, either CG3 46 m or Tair 22 m may be suitable for comparison to satellite data  Search for sources of AVHRR (low) and CG3 (high) bias

SOURCES OF AVHRR BIAS Treatment of and apparent insensitivity of Qin algorithm to water vapor (Fig. 9) – results in relatively low LST

SOURCES OF AVHRR BIAS (cont’d) High transmittance from Qin algorithm equations (Table 9) – results in relatively low LST DateWater Vapor Ch.4 Trans. Ch.5 Trans. QinAug 11, g cm LOWTRANAug 11, g cm QinSep 5, g cm LOWTRANSep 5, g cm

SOURCES OF AVHRR BIAS (cont’d) EMISSIVITY Simultaneous Channel 4/5 error:.005 error  LST error Single channel error:.005 error  LST error Range of possible values 0.989/0.989 Ch. 4/5 – Qin/Sobrino (NDVI) / Ch. 4/5 – NASA JPL Spectral Library

AVHRR BIAS cont’d RESOLUTION – 2 K variability w/in 1 km pixel ASTER Brightness Temperature, 90 m resolution (June 16, 2001)

CONCLUSIONS – QIN/SPLIT WINDOW ALGORITHM  Uncertainties in water vapor and transmittance treatments  Small uncertainty in profiles used to derive transmittance equations  Substantial emissivity uncertainty  SW algorithm is generally not very portable  More generic atmospheric correction methods are preferable  Refinement of emissivity values is required

SOURCES OF CG3 BIAS 2 m difference, CG3 minus Tair – no abrupt jump from 2000 to 2001  different mechanisms/conditions between 46 m and 2 m

CG3 BIAS at 2 m – Solar heating Instrument body temperature (KZT) vs. T air identifies solar heating effects CG3-Tair differenceKZT-Tair difference  If CG3 is in equilibrium, elevated KZT should not cause positive CG3 bias  Since increased CG3-Tair difference occurs at times of apparent solar heating, some of the bias may be due to solar heating of CG3 window

CG3-Tair (22 m) difference KZT-Tair (46 m ) difference CG3 BIAS at 46 m  High CG3 bias even when KZT is lower than 46 m air temperature (general air temperature profile increases above canopy)  Indicates a greater CG3 bias than at 2 m, but not clearly related to instrument body temperature

CONCLUSIONS – CG3 BIAS  Some of the bias results from internal (solar) heating effects  Given jump in December 2000 and high bias even at night, suspect instrument setup/calibration problem at 46 m  Possible problems with 2 m and 46 m air temperature hinder drawing definitive conclusions

OVERALL CONCLUSIONS  AVHRR Results are in line with previous studies  Little advantage to use of existing split window algorithms  Acceptable accuracy in deciduous forest is achievable with proper emissivity/atmospheric correction  Tower radiometer appears appropriate type of instrument for satellite validation  Upper canopy air temperature may be similar to satellite or tower LST  Forest LST and air temperature exhibit similar long term patterns and differences may converge over long time periods