No abstract

No abstract

No abstract

No abstract

No abstract

[1] The influence of soil water content in thermal infrared emissivity is a known fact but has been poorly studied in the past. A laboratory study for quantifying the dependence of emissivity on soil moisture was carried out. Six samples of surface horizons of different soil types were selected for the experiment. The gravimetric method was chosen for determining the soil moisture, whereas the emissivity was measured at different soil water contents using the two-lid variant of the box method. As a result, the study showed that emissivity increases from 1.7% to 16% when water content becomes higher, especially in sandy soils in the 8.2-9.2 mm range. Accordingly, a set of equations was derived to obtain emissivity from soil moisture at different spectral bands for the analyzed mineral soils. Moreover, results showed that the spectral ratio decreases with increasing soil water content. Finally, the study showed that systematic errors from 0.1 to 2 K can be caused by soil moisture influence on emissivity.Citation: Mira, M., E. Valor, R. Boluda, V. Caselles, and C. Coll (2007), Influence of soil water content on the thermal infrared emissivity of bare soils: Implication for land surface temperature determination,

Abstract-Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer Opportunity Mission from the European SpaceAgency with a launch date in 2007. Its goal is to produce global maps of soil moisture and ocean salinity variables for climatic studies using a new dual-polarization L-band (1400-1427 MHz) radiometer Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). SMOS will have multiangular observation capability and can be optionally operated in full-polarimetric mode. At this frequency the sensitivity of the brightness temperature ( ) to the sea surface salinity (SSS) is low: 0.5 K/psu for a sea surface temperature (SST) of 20 C, decreasing to 0.25 K/psu for a SST of 0 C. Since other variables than SSS influence the signal (sea surface temperature, surface roughness and foam), the accuracy of the SSS measurement will degrade unless these effects are properly accounted for. The main objective of the ESA-sponsored Wind and Salinity Experiment (WISE) field experiments has been the improvement of our understanding of the sea state effects on at different incidence angles and polarizations. This understanding will help to develop and improve sea surface emissivity models to be used in the SMOS SSS retrieval algorithms. This paper summarizes the main results of the WISE field experiments on sea surface emissivity at L-band and its application to a performance study of multiangular sea surface salinity retrieval algorithms. The processing of the data reveals a sensitivity of to wind speed extrapolated at nadir of 0.23-0.25 K/(m/s), increasing at ( ) is found to be correlated with the measured sea surface slope spectra. Peaks in ( ) are due to foam, which has allowed estimates of the foam brightness temperature and, taking into account the fractional foam coverage, the foam impact on the sea surface brightness temperature. It is suspected that a small azimuthal modulation 0.2-0.3 K exists for low to moderate wind speeds. However, much larger values (4-5 K peak-to-peak) were registered during a strong storm, which could be due to increased foam. These sensitivities are satisfactorily compared to numerical models, and multiangular data have been successfully used to retrieve sea surface salinity.

Abstract. A split-window algorithm for deriving land surface temperatures (LSTs) from advanced very high resolution radiometer (AVHRR) channels 4 and 5 is proposed and validated with in situ measured temperatures. On the basis of the radiative transfer theory the algorithm defines a set of surface-independent coefficients which are equivalent to the classical split-window coefficients for sea surface temperature (SST). These coefficients are calculated using SST matchups (coincident AVHRR and buoy measurements) provided by the National Oceanic and Atmospheric Administration (NOAA)-NASA Pathfinder Database of worldwide measurements. Thus calibration of the split-window coefficients is done using real data. The variability of atmospheric attenuation is represented in the proposed algorithm by a quadratic dependence on the brightness temperature difference. For LST determination the emissivity effect is modeled through an additive coefficient which depends on surface emissivity in the AVHRR channels 4 and 5. The algorithm is validated for both SST and LST by using independent ground-based and AVHRR data. The database used in the validation of LST was obtained for a wide range of surface types in a semiarid environment. The same databases are used to compare the accuracies of other published split-window algorithms. The proposed algorithm yields standard errors of temperature estimate between _ 1.0 and +_ 1.5 K, and no significant biases are observed. Although results are encouraging, more validation is required principally for moist atmospheric conditions. For land surface an empirical methodology cannot be ap-16,697