Full text: Proceedings, XXth congress (Part 7)

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
Rugege and Driessen (2002) demonstrated that based on the same 
production function indeed highly accurate estimates of maize 
yield can be obtained. This has promise for regional applications 
since it greatly reduces the computational data needs with few 
other forcing variables needed then the difference between 
ambient air and surface (or canopy) temperature. This is also the 
weakness of the approach that the uncertainty of the estimates 
inereases when AT is not available for all days in the crop cycle, 
as often pixels are not entirely cloud-free at the moment of the 
satellite pass. 
Note that for computations of AT the following is needed: one 
observer to record ambient air (7,) and another for surface (or 
canopy) temperature (7,). Key to accurately quantifying the 
thermodynamic process of transfer of energy from objects that are 
warmer than their surroundings to the air, or from the air to cooler 
objects, is that the absolute difference between the two are 
observed at the same instantaneous moment, and using observers 
that yield independent readings. Currently, ambient (air) 
temperature is often taken from meteorological surface 
observations. After these surface (point) observations have 
undergone objective analysis, their spatial and temporal 
resolutions have miraculously become commensurate with the 
surface (or skin) temperature observation from satellites. Diurnal 
variations of temperature (differences) can be highly dynamic, 
and in such cases the resulting ¢fH20 or any other derivation of 
the latent heat flux merely reflects the (lack of) quality of the 
interpolation technique being deployed rather than approximating 
the absolute transference of heat, inter alia crop stress. Even 
though acknowledged, for this research it is assumed that errors 
caused by this flaw can be neglected, and that positive differences 
between 7, and 7, can be fully subscribed to canopy heating, and 
thus crop stress. 
2.2.1 Observing crop canopy heating from Space 
A formidable challenge lies in combined use of data from 
multiple satellites of complementary specifications to further 
satisfy the spatial, temporal and radiometric requirements for 
canopy temperature inference. Estimating ambient air temperature 
from satellite data will possibly be dealt with in future 
experiments; this study focuses on estimating canopy temperature 
only. 
22.  Multi-sensor canopy temperature retrieval: 
Accurate retrieval of surface temperature is complicated if 
measurements are made by sensors aboard satellite platforms 
far from the ground. Atmospheric attenuation processes 
including absorption, upward atmospheric radiance and bi- 
directional reflection of downward atmospheric radiance affect 
transmission of the emitted radiation. Absorption of water 
vapour is considered to be the most important factor influencing 
radiance transfer in the thermal spectral range (Bastiaanssen, 
1995; Qin and Karnieli, 1999). 
Polar orbiting satellites have a relatively high signal-to-noise 
ratio, and depending on the flight characteristics of the spacecraft 
in question they cover the same spot of the earth surface once 
every day. Coupled with cloudy conditions very few clear-sky 
observations remain for repetitive skin temperature retrieval for a 
fixed position on the earth’s sphere. For skin (or canopy) 
temperature inference the so-called ‘split-window’ technique is 
commonly applied for data from multi-thermal band sensors. The 
technique eliminates effects of water vapour absorption and 
emission by using split data in the 10 to 13 pm range, often 
referred to as the 7,, and T,» bands. The concept exploits the 
different absorption characteristics of the atmosphere within these 
215 
different but close wavelengths, assuming that surface emissivity 
is constant over this spectral region. Detailed reviews of split- 
window algorithms are provided by Caselles et al (1997), Qin and 
Karnieli (1999) and by Parodi (2000). 
Sensors aboard satellites with a geosynchronous orbit observe 
diurnal changes of the atmosphere and earth surface for a fixed 
region, but do so at a lower signal-to-noise ratio due to their 
height (approx. 30.000 km). Another disadvantage is, despite their 
attractive temporal resolution, that they observe the earth surface 
at (nadir) resolutions of >5 km for TIR bands, actual resolutions 
depending on the sensor in question and distance from at nadir. 
Such spatial resolutions are insufficient for most regions, which 
have scattered land use practices resulting in mixed-pixel 
observations. Data from more *recent sensors with smaller at 
nadir resolutions (<3.25 km) may further help to overcome this 
drawback and permit to up scale the developed methodology to 
regions with less homogenous land cover. Most sensors aboard 
geo-stationary satellites also have two channels in the thermal 
infrared range of the spectrum (10 to 13 jum). Researchers have 
effectively exploited this to minimize the errors in estimating land 
surface (or canopy) temperature, analogue to the technique 
developed for polar orbiting satellites data as introduced above. In 
an attempt to further improve the technique for geo-stationary 
satellites. Sun and Pinker (2002) showed that by adding a second 
term of the brightness temperature difference (T, rT, the 
atmospheric effect can be further removed. They also noted that 
when the satellite viewing angle increases, the optical path and 
the atmospheric attenuation increase also. After McClain et al. 
(1985) they added a zenith angle correction term (sec@/) to 
further normalize the data for optical path variations. Yuichiroh 
(2004) further elaborated on this idea, and signalled that the effect 
of water vapor is not fully removed by the split-window 
technique. After Coll and Caselles (1997) he attempted to further 
improve the algorithm by calculating various coefficients for 
different precipitable water levels using a radiative transfer model 
over The Tibetan Plateau for the application of the method to 
satellite data from GMS-3. 
For this study satellite data from NOAA-/4 and GMS-5 were used 
because of the similar specifications of the two instruments 
onboard these satellites and their complementary viewing 
frequency. The NOAA-14 spacecraft passes at approx. 14.00 h 
local time (range: 13.00 — 15.00 h), whereas GMS-5 scans the 
whole of South East Asia every hour. 
2.2.3 Inter-calibration between GMS-5 and NOAA-14: 
Instrument calibrated data from GMS-5 have been evaluated 
against calibrated data from the polar orbiting satellites NOAA-14 
by the Japanese Meteorological Satellite Center, JMA (Tokuno 
M., 1997). The results of their evaluation revealed that the 
brightness temperatures of /R/ (10.5-11.5 um) of GMS-5 are 
about 1.2 (K) lower than those of Ch4 (10.3-11.3 um) of NOAA- 
14/AVHRR on average, and the brightness temperatures of /R2 
(11.5-12.5 um) of GMS-5/VISSR are about 0.6 (K) higher than 
those of Ch5 (11.5-12.5 um) NOAA-14/AVHRR. 
  
* SEVIRI (Spinning Enhanced Visible and Infra Red Imager) instrument 
aboard the European geo-stationary MSG-/ and 2 and the spectral 
channels of the Chinese Visible and Infrared Spin Scan Radiometer 
(VISSR) aboard the geo-stationary FY-2C will enhance upon the 
applicability of the procedures presented in this research by their 
improved temporal and spatial resolution. 
 
	        
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