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