Temperate Deciduous Forest. Deciduous – To shed The most important thing is…
RADIANT SURFACE TEMPERATURE OF A DECIDUOUS FOREST – THE EFFECTIVENESS OF SATELLITE
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Transcript of RADIANT SURFACE TEMPERATURE OF A DECIDUOUS FOREST – THE EFFECTIVENESS OF SATELLITE
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 2000-1 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/cm2 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 = 0.989- winter images – used modeled reference values (Snyder et al.
2001) of 0.968 for Ch. 4, 0.971 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
2001 15-minute Air Temperature Data Uncorrected Corrected
AVHRR TEMPERATURE COMPARISONS
AVHRR/CG3 46 m 2000-1
AVHRR-CG3 46 m temp. difference 2000-1
CG3 46 m-Air temp. 22 m 15-min. data Temp. difference 2001
CG3 LST/AIR TEMPERATURE COMPARISONS
CG3 46 m-Air Temp. 22 mDaily Mean Difference 2001
CG3 46m 2000-1
CG3 2m 2001
Tair 22 m Tair 2 m
AVHRR 2000-1 -1.96 K -2.24 K -1.92
CG3 46m #1
2001*
0.54 K 2.25 K
CG3 46m #2
2001 (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 2000Evident in both CG3s at 46 m
CG3 – Tair 22m
2000 2001
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
Date Water Vapor
Ch.4 Trans.
Ch.5 Trans.
Qin Aug 11, 2000
2.844 g cm-2
.7580 .6421
LOWTRAN Aug 11, 2000
2.844 g cm-2
.6196 .4717
Qin Sep 5, 2000
1.857 g cm-2
.8570 .7793
LOWTRAN Sep 5, 2000
1.857 g cm-2
.7552 .6493
SOURCES OF AVHRR BIAS (cont’d)
EMISSIVITY
Simultaneous Channel 4/5 error: .005 error 0.3-0.4 LST error
Single channel error: .005 error 0.7-0.9 LST error
Range of possible values
0.989/0.989 Ch. 4/5 – Qin/Sobrino (NDVI)0.9735/0.9732 Ch. 4/5 – NASA JPL Spectral Library
ASTER CH13 BT
Value
294.51 - 295
295.01 - 295.5
295.51 - 296
296.01 - 296.5
296.51 - 297
297.01 - 297.5
297.51 - 298
298.01 - 298.5
298.51 - 299
299.01 - 299.5
299.51 - 300
300.01 - 300.5
300.51 - 301
301.01 - 301.5
301.51 - 302
302.01 - 302.5
302.51 - 303
303.01 - 303.5
303.51 - 304
304.01 - 304.5
304.51 - 305
305.01 - 305.5
305.51 - 306
306.01 - 306.5
306.51 - 307
307.01 - 307.5
307.51 - 308
308.01 - 308.5
308.51 - 309
309.01 - 309.5
309.51 - 310
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
2000 2001
CG3 BIAS at 2 m – Solar heatingInstrument body temperature (KZT) vs. Tair identifies solar heating effects
CG3-Tair difference KZT-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