This work addressed the simultaneous retrieval of Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from time-series thermal infrared data. On basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST and LSE from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by algorithm assumption, instrument noise, initial emissivity, etc. The modeling errors of the proposed method from the simulated data are less than 0.04 K for temperature and less than 6.76E-4 for emissivity. The proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the RMSE of LST is observed to be only 0.13K, and that of LSE is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods.
In this study, an improved linear emissivity constraint temperature and emissivity separation (I-LECTES) method was first proposed to overcome the discontinuities problem of the retrieved land surface emissivities (LSEs) in the former linear emissivity constraint temperature and emissivity separation (LECTES) method. Consequently, the hyperspectral thermal infrared data were carefully simulated according to the configuration of Designs & Prototypes microFTIR Model 102, and were used to evaluate the performance of the I-LECTES method. Meanwhile, the I-LECTES method was also compared with the LECTES method. Different the atmosphere and surface circumstances were considered, as well as the different levels of noise equivalent temperature difference (NEΔT). The results showed that the proposed I-LECTES method is of a better accuracy compared with the LECTES method and has the characteristic of keeping the retrieved LSEs continuous, which sounds more reasonable. Because the noises in the ground measured radiance may have more effects on the accuracies of land surface temperature (LST) and LSEs than those in the atmospheric downwelling radiance, the noise in the ground measured radiance should be removed as much as possible to improve the accuracies of retrieved LST and LSEs. Furthermore, taken into account the lower retrieval accuracies for the cold and dry atmosphere, both the I-LECTES method and the LECTES method should be taken a full consideration. The proposed method is regarded to be promising because of its holding continuity and noise-immune.
Land surface emissivity (LSE) is a critical parameter for retrieving land surface temperature (LST) from remotely sensed data. Due to its non-uniformity and a change through vegetation and physical parameters such as texture, composition, surface moisture, roughness, and view angle, the measurement of LSE in laboratory cannot reflect the real world conditions that material interacts with its background and the environment. The filed measurement currently observed by a thermal sensor is radiance, which is a function of many contributing parameters. To accurately obtain the LSE, this paper devotes to develop a scheme for deriving the spectral emissivity from field measured radiance observed by a hand portable FT-IR spectroradiometer Model 102F. A piecewise linear spectral emissivity constraint method is used to decouple the LST and LSE. The results show that the trends of the derived emissivity spectra for different natural surfaces of sand, bare soil and grass are reasonable. Comparisons of several field and laboratory collected LSE spectra for different natural surfaces show that the root mean square errors (RMSEs) are below 0.02, which indicates that the proposed method is accurately to derive LSE spectrum from the measurement of field natural surface with 102F field spectroradiometer.
We evaluate the land surface temperature (LST) generated from the spinning enhanced visible and infrared imager (SEVIRI) onboard the MSG-2 satellite, which was retrieved using the split-window method where the land surface emissivity (LSE) was estimated from the day/night temperature-independent spectral indices-based method. The SEVIRI-derived LST was compared with the MODIS-derived LST extracted from the MOD11B1 V5 product during 7 clear-sky days. The results show that (1) discrepancies exist between the two LST products, with a maximum average difference of 4.9 K; (2) these differences are considered to be time-dependent, since higher discrepancies are observed during the daytime; (3) these differences are land-cover dependent, e.g., bare areas generally present larger differences than vegetated areas; and (4) these differences are inversely proportional to view zenith angle differences. Finally, the main sources of LST differences are investigated and identified in terms of LSE, instrumental noise equivalent temperature difference (NEΔT), and misregistration of the two LST products. The LST differences arising from NEΔT and misregistration are within 0.4 K. Therefore, these discrepancies may mainly result from errors in LSE, which are caused primarily by the atmospheric correction error for the SEVIRI-derived LST.
Leaf area index, which is the most basic biophysical parameter in the description of vegetation canopy structure, has
become an important input parameter in land surface process. Along with the intensive study of LAI retrieval in remote
sensing, the scale effect problem related to LAI has attracted more and more attention and has become a focus in
quantitative study of remote sensing. Based on the mechanism analysis of scaling effects, the scale effect of LAI is
discussed. Then, the relevant scaling model is proposed under the hypothesis of a normal distribution of surface
observations. The result shows that the relative error for LAI retrieval is less than 1%. So, it validates the reliability of
this model in one aspect.
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