Rongyuan Liu

Performance evaluation of four directional emissivity analytical models with thermal SAIL model and airborne images.

Land surface emissivity is a crucial parameter in the surface status monitoring. This study aims at the evaluation of four directional emissivity models, including two bi-directional reflectance distribution function (BRDF) models and two gap-frequency-based models. Results showed that the kernel-driven BRDF model could well represent directional emissivity with an error less than 0.002, and was consequently used to retrieve emissivity with an accuracy of about 0.012 from an airborne multi-angular thermal infrared data set. Furthermore, we updated the cavity effect factor relating to multiple scattering inside canopy, which improved the performance of the gap-frequency-based models.

Atmospheric water vapor retrieval from Landsat 8 thermal infrared images

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split-window covariance-variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm2 for dry atmosphere (wv <2 g/cm2) conditions and better than 0.5 g/cm2 for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground-measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.

Evaluation of Radiometric Performance for the Thermal Infrared Sensor Onboard Landsat 8

The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images.

Determination of Optimum Viewing Angles for the Angular Normalization of Land Surface Temperature over Vegetated Surface

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors.

Noise Evaluation of early images for Landsat 8 Operational Land Imager

This study performed an on-orbit evaluation of noise level for the Operational Land Imager (OLI) onboard Landsat 8 using early images over ground homogeneous sites. The signal-to-noise ratios (SNR) were higher than 160 of OLI nine bands at typical radiance level, while the noise equivalent radiance difference (NE∆L) and the noise equivalent reflectance difference (NE∆ρ) were respectively lower than 0.8 W/m(2)/µm/sr and 0.002. Compared to pre-launch predictions, the on-orbit low noise and high SNR almost satisfied requirements for OLI bands, and can provide a prior knowledge for uncertainty analysis of OLI images in monitoring land surface, oceanic, and atmospheric status.

Atmospheric water vapor retrieval from Landsat 8 and its validation

This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45 g/cm2 for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.

Spectral Recalibration for In-Flight Broadband Sensor Using Man-Made Ground Targets

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor <; 2 g/cm2 and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the cameras spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

Evaluation of MODIS, POLDER and CYCLOPES global FPAR products

Fraction of Absorbed Photosynthetically Active Radiation (FPAR), determined from remote sensing data, can vary with the spatial resolution, the different retrieval algorithms and viewing angels of the used data. This paper aimed at evaluating MODIS, POLDER and CYCLOPE global FPAR products, and found that the MODIS FPAR was larger than CYCLOPES, and their difference ranged within 0.1~0.2, especially at the forest area where MODIS product always presented seasonal variation in this area while CYCLOPES products kept relatively stable. For other vegetation covers, their difference was less than 0.1. Furthermore, the comparison of MODIS and POLDER FPAR products shown that the MODIS FPAR was also larger than the POLDER and their difference was up to 0.1 to 0.2.

DIRECT ALGORITHM FOR MAPPING LAND SURFACE FPAR FROM MODIS APPARENT REFLECTANCE AT TOP OF ATMOSPHERE

Fraction of abstracted Photosynthetically Active Radiation (FPAR) is a fundamental terrestrial state variable in most ecosystem productivity models and is also one of the key terrestrial products. This paper proposed a new Direct-Algorithm to retrieve FPAR from apparent reflectance of MODISs seven bands in the visible, near-infrared and short-wave wavelengths. The Direct-Algorithm developed from the dataset simulated by radiative transfer models of canopy and atmosphere with different canopy structures and atmosphere conditions, estimated direct FPAR (FPARdir), and scattering FPAR (FPARsct), and total FPAR of the canopy (FPARtot) by using linear equations of TOA reflectance. Result showed that the estimated FPAR product were close to that of MODIS products except the forest, perhaps because the homogenous canopy of the SAIL model is not suitable for the forest canopy.