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Validation of land surface emissivity (LSE) is very important for assessing the accuracy of remote sensing products and understanding the potential and limitations of retrieval methods. Currently, ...

Mathematically, once the measured radiance has been corrected for atmospheric effects, the only issue when determining the land surface temperature and emissivity (LST and LSE) is solving the ill-posed problem in the radiative transfer equation (RTE). Recently, based on the wavelet transform theory, a so-called wavelet-transformed temperature and emissivity separation (WTTES) method has been proposed for retrieving LST and LSE from hyperspectral data. Although, in a previous article, an initial suggestion was provided after analyzing the uncertainties under the conditions of several typical errors, it was also noted that considerable work was still necessary for achieving a reliable method for driving the WTTES algorithm, particularly under different situations. To complement the previous analysis of the WTTES algorithm, this paper presents a more detailed and comprehensive evaluation in which we changed the wavelet functions, varied the wavelet levels, and biased the atmospheric profiles. The results in this paper showed that the WTTES algorithm was insensitive to the choice of wavelet function. In addition, the WTTES algorithm could stay stable in most circumstances. A wavelet level of n = 3 was more recommend when the NEΔT was approximately 0.2 K. When a higher level of noise was found, a level of n = 4 could be then used to better overcome the noise. When a lower level of noise was found, a level of n = 2 could be used to further refine the spectral features. Additionally, we also found that the WTTES algorithm could have problems when atmospheric effects were inaccurately compensated for, especially for wet-warm profiles.

The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.

Daily evapotranspiration (ET) is of great significance among various practical applications including water management, drought monitoring, and climate change study. The ET estimations from remotely sensed models are usually instantaneous values. Various upscaling methods have been developed to extrapolate the instantaneous ET to daily scale. In the applications of these methods, the accuracy of daily ET estimation relies on both the accuracy of instantaneous ET calculation and the upscaling methods. This paper used the decoupling model to estimate daily ET directly, according to the constancy of the decoupling coefficient (Ω) in the model in a diurnal cycle, without the calculation of instantaneous ET. The estimated daily ET was compared with the Eddy covariance measurements which were corrected by the Bowen ratio method to close the energy imbalances. The result from field data alone showed that the coefficient of determination (R2) of daily ET estimation was 0.860, with a root-mean-square error (RMSE) of 18.2 W/m2, and a bias of −4.7 W/m2. Combining MODIS data and field data, the estimated daily ET had a R2 of 0.860, a RMSE of 21.6 W/m2, and a bias of −5.0 W/m2 . Therefore, it is feasible and effective to obtain daily ET using remote-sensing based instantaneous Ω to replace daily value in the decoupling model.

International audience; Studying the effect of cloud cover on the temporal upscaling of instantaneous evapotranspiration (ET) is significant in the effort toward a more accurate and widely applied upscaling method to obtain the exact ET on a daily or longer time scale, thereby benefiting the practical applications. In this article, the authors concentrated on the effects of cloud cover in different amounts and for varying time durations, with three commonly used upscaling approaches including the constant evaporative fraction (EF) method, the constant reference evaporative fraction (EFr) method, and the constant global solar radiation (Rg) method. Transient cloud cover and persistent cloud cover were defined according to the occurrence time, namely, the cloud that appeared 1 h before or after the upscaling moment and the cloud lasting the whole day except during the upscaling time, respectively. The different cloud cover amounts were indicated by the different losses of downwelling shortwave irradiance. The instantaneous fluxes were simulated from the atmosphere-land exchange (ALEX) model, which was driven by the meteorology measurements at the Yucheng station in China. The results showed that (1) the cloud caused the deterioration of the underestimation or overestimation of the daily ET upscaling in comparison with the results of clear days. Specifically, the persistent cloud cover had a more significant effect on the three upscaling methods; for the transient cloud cover, the upscaling results had larger deviations when the cloud appeared before the upscaling moments than when it appeared after them; (2) the effects on the upscaling factors and the upscaling results both increased proportionally with the growth of the cloud cover; and (3) the constant EFr method performed best for both clear and cloudy situations, with a minimal bias less than 4.7 W/m2 (5.5%) and a root-mean-square error (RMSE) less than 8.9 W/m2 (20.6%); the EF method was most severely affected, with a bias up to 24.1 W/m2 (28.3%) and an RMSE up to 24.9 W/m2 (57.7%); the Rg method had an intermediate accuracy with a bias less than 20.9 W/m2 (24.6%) and an RMSE less than 20.3 W/m2 (47.1%); and (4) all three approaches were influenced most significantly around noontime.

Daily Evapotranspiration (ET) is of great significance among various practical applications in the fields of water management, drought monitoring and climate change study. This paper utilized instantaneous decoupling coefficient to estimate daily LE (used interchangeably with ET in this paper) with atmospheric and surface resistances calculated from MODIS data. The field data were used only at first to identify the errors induced by the parameter retrieval from remote sensing data. The estimated daily LE was compared with measured data and the result showed that the coefficient of determination (R2) was 0.960, with a root mean square error (RMSE) of 12 W/m2 and a bias of −4 W/m2. When MODIS data were involved in the calculation of decouple coefficient and resistances, the R2 of the estimated daily LE was 0.949, with a RMSE of 33.1 W/m2 and a bias of −17.9 W/m2. Therefore, it is feasible and effective to obtain daily LE using instantaneous decoupling coefficient from remote sensing data.

This paper presents a complement analysis for the wavelet transform method for separating temperature and emissivity (WTTES) with different wavelets, wavelet levels and biased atmospheric downwelling radiance. According to the results, the WTTES algorithm is quite insensitive to the choice of the wavelet. By comparing the retrievals with different wavelet levels, a wavelet level of n=3 or n=4 is more recommended in most cases. In addition, compared with the white noise, the WTTES algorithm is more sensitive to the atmospheric downwelling radiance with bias errors. For the profile with a bias error of 10%, the RMSE of the emissivity retrievals can be increased approximately 0.17%-2.33%, which depends on the specified water vapor content of the profile. However, different from the obvious errors on emissivity, the overall accuracies of the temperature retrievals under different atmospheric profiles are all less than 0.7K, which means the WTTES algorithm is still feasible to retrieve the temperature under the condition of biased moisture profiles.