F. Sospedra

Thermal–infrared emissivities of natural surfaces: improvements on the experimental set-up and new measurements

Ground measurements of thermal infrared emissivities of terrestrial surfaces are required to derive accurate temperatures from radiometric measurements, and also to apply and validate emissivity models using satellite sensor observations. This paper focuses on the demanding aspects that are involved in the field measurement of emissivity using the box method and a hand-held radiometer. Measuring emissivities in field conditions can be hampered by external factors such as wind and solar irradiance. This can increase the time spent on the field campaign but, most importantly, it can cause no-sense fluctuations between consecutive observations. Here we propose original developments for the experimental instrumentation to ensure consistency of measurements. Moreover, we present a dataset of emissivity values for different soils, rocks and vegetation samples measured in the 8–14, 8.2–9.2, 10.5–1 1.5 and 11.5–12.5 µm wavebands.

Temperature and emissivity separation from calibrated data of the Digital Airborne Imaging Spectrometer

Abstract The Digital Airborne Imaging Spectrometer (DAIS), with six thermal infrared channels in the 8–14 μm window, was flown over the Barrax test site, Spain, in the framework of the DAIS Experiment in the summer of 1998. Atmospheric correction of the DAIS thermal channels was performed by means of local radiosonde measurements and a radiative transfer model. Ground measurements of temperature and emissivity for six selected spots (two bare soils, two water bodies, and two vegetated fields) were conducted with the objective of providing calibration and validation targets. Three targets were used for a linear ground calibration of the DAIS thermal channels. With the ground-calibrated images, surface temperatures and channel emissivities were retrieved using a temperature–emissivity separation algorithm. We applied the Adjusted Normalized Emissivity Method (ANEM) in which an initial maximum emissivity was assumed for each surface type according to ground emissivity measurements. The other three targets were used to test the accuracy of the ground calibration and the ANEM method. The derived temperatures and channel emissivities were mostly within ±0.005 and ±1°C of the measured values, respectively. Such results are comparable to other methods for temperature and emissivity separation.

Effective wavenumber for thermal infrared bands-application to Landsat-TM

In this paper we suggest that brightness temperature from the band radiance measured by a thermal sensor using Plancks law can be obtained along with effective parameters adequate for a given band, and a given temperature range. We propose an accurate algorithm to obtain these parameters (effective wavenumber or effective wavelength), which is based on Taylors expansion of the exponential function that appears in Plancks equation. The dependence of the effective wavenumber on temperature has been investigated and the advantages of working with large temperature ranges have been analysed. We have compared the effective wavenumbers obtained with this algorithm to the parameters provided by NOAA/NESDIS and the maximum difference obtained has been 0.5 cm-1. The method has been applied to Landsat-Thematic Mapper band-6 and the results indicate that the error in temperature determination of the method is less than 0.01 K. A comparison with other published algorithms is also included.

Simulation of a medium-scale-surface-temperature instrument from Thematic Mapper data

A methodology to simulate the medium-scale-surface-temperature (MUST) instrument from Landsat Thematic Mapper (TM) band 6 data is proposed. MUST is conceived as a medium scale sensor (spatial resolution of 240m at nadir), with two thermal bands (10.0-11.0 μm and 11.5-12.5 μm), and a revisit period between 1 and 3 days. The developed methodology is completely general and could be implemented in other similar cases.

Analysis of thermal infrared data from the Digital Airborne Imaging spectrometer

Thermal infrared data of the Digital Airborne Imaging Spectrometer (DAIS), whose channels 74-79 are in the 8-13 w m waveband region, were analysed with the aim of recovering land surface temperature (LST). DAIS images were acquired over an experimental site where field and laboratory emissivity measurements were performed, and these were used to recover the LST from the six DAIS thermal channels. Atmospheric correction of DAIS data was calculated by means of a nearby radiosounding and a radiative transfer model. DAIS derived LSTs were compared with ground measurements of LST made coincidentally for a few test fields, the central DAIS channels yielding temperatures up to 10°C higher than ground measurements. A linear calibration was performed using in situ measurements of temperature and emissivity for two reference fields, and the large differences in temperature were then considerably reduced. Temperatures obtained from DAIS channel 79 agreed with the in situ measurements within - 2°C. This channel seemed the most reliable for deriving accurate LSTs in the dataset analysed here.

Night-time cloud cover estimation

In this paper a method for cloud cover assessment at night-time (when only thermal infrared data are available) is presented. It is based on the analysis of long wave radiation transfer processes in partially cloudy areas, which led to the formulation of a simplified model of the surface–cloud–atmosphere system. The model was implemented in an operational and iterative algorithm to solve the radiative equations. The algorithm was validated using ground data collected at four meteorological stations in Argentina during November 1997 and May–June 1998, which were compared to cloudiness derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer thermal data. Differences between observed and calculated cloudiness were within ±1 okta in 78% of the cases studied, giving a bias of +0.5 oktas and a standard deviation of ±1.0 oktas in cloudiness estimation.

NEDT influence in the thermal band selection of satellite-borne instruments

This work focuses on the impact of noise in the channel configuration of thermal sensors, in two aspects. The first one is related to the selection of an optimal band configuration to calculate accurate land surface temperatures (LSTs). The second one addresses the impact of noise level on the absolute error in temperature estimates, for the chosen band combination. These issues are studied beyond the analysis performed in previous works, where spectrally constant low-level noise values were considered. The study is based on the comparison of LST errors yielded by different potential channel configurations. The sensitivity analysis is performed using a quadratic split-window model regressed by least-squares techniques on a database containing LST and brightness temperatures for a wide range of atmospheric and surface conditions. In this analysis all sources of uncertainty are taken into account, and the influence of the procedures used to remove the effects of the Modulation Transfer Function is also considered. The results reveal that: (i) the Noise Equivalent Difference of Temperature (NEDT) should not be higher than - 0.3 K at 300 K within the 10-12.5 w m window, and - 0.2 K at 300 K within the 8-9.5 w m spectral region, to assure an accuracy better than - 1.2 K in LST, and (ii) the combinations (10.1-10.8)/(11.7-12.4), (10.1-10.8)/(11.8-12.5) and (10.1-10.8)/(11.5-12.5) w m on the one hand, and (8.0-8.5)/(8.5-9.0) w m on the other hand, are the optimal configurations within the two specified windows, respectively.