tanbul 2004 
/| vegetated 
of AVHRR 
soil. 
reflectances 
ited areas of 
ta computed 
composites 
ben 1986). 
o that (final 
e Tair. 
(lues applied 
DVI 
3.1 
ben 
iation in the 
) overpasses 
d. 
56x + 0.0703 | 
| 
27x * 0.5188 
  
2.00 
4) variation 
losely to 2K 
mall changes 
Tair. 
temperature 
temperatures 
ding of the 
es. 
lectances for 
rformance of 
'HRR (1024 
n the quality 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
of land surface coverage and the thermal dynamics. However, 
before any additional information of the atmospheric lower 
layers dynamics, the pronounced variations on the Tair during 
November 04 night remains unexplained. In doing so, taking 
into account only the obtained results, it lead us to conceive that 
the produced linearizations between Tair and LST are not 
theoreticaly reliable. It is due to the high uncertainty and 
variable state of the atmosphere (as wind speed regime) from 
one day to another, in the lower layers near the surface. 
This type of verification is only contemplate very briefly here 
but will be the topic of future research on the analysis of these 
features. 
Preliminary results within the same data base also suggested 
there is not relation for the both LST and Tair variations 
associated to the land surface altitude. 
3.1 Selected Bibliography 
Andersen, H. S. (1997). Land surface estimation based on 
NOAA-AVHRR data during the HAPEX-Sahel experiment. 
Journal of Hydrology, 189, 788-814. 
Becker, F. and Li, Z-L. (1990). Towards a local split window 
method over land surfaces. International Journal of Remote 
Sensing, 11, (3), 369-393. 
Goetz, S. J. (1997). Multisensor analysis of NDVI, surface 
temperature and biophysical variables at a mixed grassland site. 
International Journal of Remote Sensing, 18, (1), 71-94. 
Gusso, A. (2003). Monitoramento de temperaturas noturnas da 
superficie terrestre no Estado do Rio Grande do Sul com uso 
do sensor orbital AVHRR/NOAA. Porto Alegre: UFRGS, 1997. 
8lp. Dissertation (Mestrado em Sensoriamento Remoto) — 
Centro estadual de Pesquisas em Sensoriamento Remoto e 
Meteorologia, Universidade Federal do Rio Grande do Sul. 
Porto Alegre. 
Gusso, A. e Fontana, D.C. (2003). Ensaio comparativo sobre 
métodos de monitoramento da temperatura da superficie 
terrestre no Estado do Rio Grande do Sul com uso dos satélites 
NOAA. XI Simpósio Brasileiro de Sensoriamento Remoto. 
p.1185-1192. Belo Horizonte, 2003. Anais. Belo Horizonte. 
Holben, B. N. (1986). Characteristics of maximum-value 
composite images from temporal AVHRR data. International 
Journal of Remote Sensing, 7, 1417-1434. 
Kerényi, J. and Putsay, M. (2000). Investigation of land surface 
temperature algorithms using NOAA/AVHRR images. 
Advances in Space Research, 26, 1077-1080. 
Kerr, Y. H.; Lagouarde, J. P.; Imbernom, J. (1992). Accurate 
land surface temperature retrieval from AVHRR data with use 
of an improved split window algorithm. Remote Sensing of 
Environment, 40, 1-20. 
Li, Z-L., and Becker, F. (1993). Feasibility of land surface 
temperatureand emissivity determination from AVHRR data. 
Remote Sensing of Environment, 43, 67-85. 
Lagouarde, J. P. and Brunet, Y. (1993). A simple model for 
estimating the daily upward longwave surface radiation flux 
from NOAA-AVHRR data. /nternational Journal of Remote 
Sensing, 14, (5), 907-925. 
159 
McMillin, L. M. (1975). Estimation of sea surface temperature 
from two infrared window measurements with different 
absorption. Journal of Geophysical Research, 80, 5113-5117. 
Oliveira, H. T. (1997). Climatologia das Temperaturas 
Minimas e Probabilidade de Ocorréncia de Geada no Estado 
do Rio Grande do Sul. Porto Alegre: UFRGS, 1997. 8lp. 
Dissertation (Mestrado em Fitotecnia) — Programa de Pós- 
Graduagáo em Agronomia, Universidade Federal do Rio Grande 
do Sul. Porto Alegre. 
Ouaidrari, H.; Gowart, S. N.; Czajkowski, K. P.; Sobrino, J. A.; 
Vermote, E. (2002). Land surface temperature estimation from 
AVHRR thermal infrared measurements: An Assessment for the 
AVHRR Land Pathfinder II Data Set. Remote Sensing of 
Environment, 81, 114-128. 
Qin, Z.and Karnieli, A. (1999). Progress in remote sensing of 
land surface temperature and ground emissivity using NOAA- 
AVHRR Data. International Journal of Remote Sensing, 
London, v.20, p.2367-2393. 
Rouse, J. W.; Haas, R. H.; Schell, J. A., Deering, D. W. and 
Harlan, J. C. (1974). Monitoring the vernal advancement and 
retrogradation (greenwave effect0 of natural vegetation, 
NASA/GSFC type III Final Report, Greenbelt, MD. 
Sandholt, L.; Rasmussen, K.; Andersen, J. (2002). A simple 
interpretation of the surface temperature/vegetation index space 
for assessment of surface moisture status. /nrernational Journal 
of Remote Sensing, 79, 213-224. 
Sobrino, J. A.; Caselles, V.; Coll, C. (1993). Theoretical split- 
window algorithms for determining the actual surface 
temperature. // Nuovo Cimento, Verona, 16C, 3, 219-236. 
Sullivan, J. T. (1999). New radiance-based method for AVHRR 
thermal channel nonlinearity corrections. /nternational Journal 
of Remote Sensing, 20, 3493-3501. 
Valor, E. and Caselles, V. (1996). Mapping land surface 
emissivity from NDVI: Application to European, African and 
South Americam Areas. Remote Sensing of Environment, 57, 
167-184. 
Van de Griend, A. A. and Owe, M. (1993). On the relantionship 
between thermal emissivity and NDVI for natural surfaces. 
International Journal of Remote Sensing, 6,1119-1131. 
3.0 Acknowledgements 
The authors would like to thank the National Oceanic and 
Atmospheric Administration and NOAA Satellite Information 
System for the AVHRR/NOAA data and informations 
continually provided. 
The authors are also thankful to the 8° DISME/INMET and 
FEPAGRO (Brazil) institutions for the metcorological data 
provided.