Yonggang Qian

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.

Estimation of Atmospheric Profiles From Hyperspectral Infrared IASI Sensor

A physics-based regression algorithm was developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) observations to estimate atmospheric temperature and humidity profiles. The proposed algorithm utilized three steps to solve the ill-posed problems and to stabilize the solution in a fast speed regression manner: 1) a set of optimal channels was selected to decrease the effect of forward model errors or uncertainties of trace gases; 2) the principal component analysis technique was used to reduce the number of unknowns; 3) a ridge regression procedure was introduced to improve the ill-conditioned problem and to lessen the influence of correlation. To determine the optimal coefficients of the algorithm, a simulated dataset was generated with the spectral emissivities and atmospheric profiles fully covering all the possible situations for clear sky conditions. Then, the accuracy of the algorithm was evaluated against with both simulated and actual IASI data. The root mean squared error (RMSE) of atmospheric temperature profile for the simulated data is about 1.5 K in troposphere and stratosphere and is close to 4 K near the surface with no biases. The RMSE of atmospheric humidity profile for the simulated data is about 0.001-0.003 g/g at low altitude. Although the retrieval accuracy for the actual IASI data is not as good as those for the simulated data, the vertical distribution of atmospheric profiles can be well captured. Those results showed that the proposed algorithm is promising when the profile bias errors could be removed.

Land Surface Temperature and Emissivity Retrieval From Time-Series Mid-Infrared and Thermal Infrared Data of SVISSR/FY-2C

This work addressed the retrieval of land surface emissivity (LSE) and land surface temperature (LST) by using Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the full disk with a temporal resolution of 30 minutes. The LST and LSE retrieval procedures can be shown as follows. Firstly, taking into the fact that land surface is non-lambertian characteristics, the time-series bi-directional reflectances in SVISSR/FY-2C MIR channel 4 (3.8 μm ) were estimated from the combined MIR and TIR channels with day-night SVISSR/FY-2C data. A diurnal temperature cycle (DTC) model was used to correct for the atmospheric effects. The atmospheric profile data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were adopted with the aid of the radiative transfer code (MODTRAN 4.0). Secondly, a Bidirectional Reflectance Distribution Function (BRDF) model named as RossThick-LiSparse-R model was used to estimate the hemispherical directional reflectance in MIR channel from the time-series bi-directional reflectance data. Then, the LSE in MIR channel can be retrieved according to Kirchhoffs law. The LSEs in TIR channels can be estimated based on the Temperature Independent Spectral Indices (TISI) concept. And the LST can be retrieved using the split-window algorithm. Finally, a cross-validation method was used to evaluate the retrieval accuracies with the Moderate-resolution Imaging Spectroradiometer (MODIS) MOD11B1 LST/LSE V5 product. The results demonstrated that the emissivities in 11 μm and 12 μm were underestimated approximately 0.003 and 0.004 compared with MOD11B1 LSE product over the study area. The FY-2C LST were overestimated approximately 1.65 K and 2.87 K during the night-time and day-time, respectively, compared with MOD11B1 LST product over the study area.

Evaluation of Temperature and Emissivity Retrieval using Spectral Smoothness Method for Low-Emissivity Materials

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. Along with the development of civil applications, increasing numbers of man-made low-emissivity materials can be found around our living environment. In addition, the characteristics and variation in properties of those materials should also be concerned. However, there are still few TES methods for low-emissivity materials reported in the literature. This paper addresses the performance of the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) method proposed by Borel (2008) for the retrieval of temperature and emissivity from hyperspectral TIR data for low-emissivity materials. The results show that those modeling errors are less than 0.11 K for temperature and 0.3% for emissivity as shown in the ARTEMISS algorithm if atmospheric parameters and the mean emissivity of material spectra are known. A sensitivity analysis has been performed, and the results show that the retrieval accuracy will be degraded with the increase of instrument noises, the errors of the atmospheric parameters, and the coarser spectral resolution. ARTEMISS can give a reasonable estimation of the temperature and emissivity for high- and low-emissivity materials; however, the performance of the algorithm is more seriously influenced by the atmospheric compensation than by the instrument noises. Our results show that the errors of temperature and emissivity become approximately three times than that when the instrument spectral properties are 1 cm-1 of sampling interval and 2 cm-1 of FWHM, and 4 cm-1 of sampling interval and 8 cm-1 of FWHM, respectively.

Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data

Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from Airborne Hyperspectral Scanner (AHS). The estimation is conducted after eliminating reflected direct solar radiance with the aid of water vapor content (WVC), the view zenith angle (VZA), and the solar zenith angle (SZA). The results demonstrate that the LST can be well estimated with a root mean square error (RMSE) less than 1.0 K. Furthermore, an error analysis for the proposed method is also performed in terms of the uncertainty of LSE and WVC, as well as the Noise Equivalent Difference Temperature (NEΔT). The results show that the LST errors caused by a LSE uncertainty of 0.01, a NEΔT of 0.33 K, and a WVC uncertainty of 10% are 0.4~2.8 K, 0.6 K, and 0.2 K, respectively. Finally, the proposed method is applied to the AHS data of 4 July 2008. The results show that the differences between the estimated and the ground measured LST for water, bare soil and vegetation areas are approximately 0.7 K, 0.9 K and 2.3K, respectively.

A method to retrieve subpixel fire temperature and fire area using MODIS data

Methods for retrieving subpixel fire temperature and fire area have been developed over several years, but the retrieval accuracies of these methods require further improvement. In this study, a channel of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor centred at 2.1 μm and associated with the MODIS 4.0 2.1 μm channel is used to retrieve the temperature and area of fires. To test the feasibility of using the 2.1 μm channel for retrieval, the fire contribution ratios of MODIS 2.1, 4.0 and 11.0 μm channels are first examined using simulated surface radiance. Considering the difficulties in obtaining real-time validation data and in evaluating the retrieval accuracies, simulated MODIS data are used for this study. A modified method, which combines MODIS 2.1 and 4.0 μm channels, is introduced and described in detail. Compared with the traditional method, which utilizes a combination of 4.0 and 11.0 μm channels (Dozier 1981), the results show that the 2.1 μm channel is more sensitive to active fires and the large area of fires than the 11.0 μm channel, but is less sensitive to smouldering fires and small fires. The modified method that we propose has better performance and higher accuracy in active fires (temperature ≥ 800 K) and in large fires (area ≥ 0.5%). However, the traditional method is more accurate for smouldering fires and small fires. Finally, a sensitivity analysis is performed to estimate the uncertainty in assessing fire temperature and area. Experimental results indicate that under realistic conditions (fire temperatures of approximately 1000 K and a fire fractional area greater than 0.005), the retrieval errors for fire temperature and fire area are ±35 K and 20%, respectively.

The assessment of in-flight dynamic range and response linearity of optical payloads onboard GF-1 satellite

Dynamic range and response linearity are two key parameters for impacting the quality of remote sensing image and subsequently the quantitative applications. Due to the space radiation and the degrading of electronic devices, the inflight dynamic range and response linearity of remote sensing payload are subject to change, and is essential to be assessed. Therefore, in this paper, with the aid of the permanent artificial target located in the AOE Baotou site in China, the two parameters for pan-chromatic camera (Pan) and the multi-spectral camera (Band 1-4) onboard GF-1 satellite are assessed with an extrapolation method using the in situ measurements and corresponding images acquired on November 4, 2013. The results show that the low point of the dynamic range for Pan band, Band 1, Band2, Band3 and Band4 is -24.08 W•sr-1m-2μm-1, -52.22 W•sr-1m-2μm-1, -35.20 W•sr-1m-2μm-1, -31.92 W•sr-1m-2μm-1, -24.07 W•sr-1m-2μm- 1 respectively; while the corresponding high point is 271.77 W•sr-1m-2μm-1, 401.58 W•sr-1m-2μm-1, 287.46 W•sr-1m- 2μm-1, 237.33W•sr-1m-2μm-1, 307.49W•sr-1m-2μm-1, respectively; meanwhile, it is demonstrated that all the sensors have a response linearity error of lower than 1%. Moreover, an analysis for this assessment is performed in terms of the uncertainties for surface reflectance measurement (1%), aerosol optical depth (10%), column water vapor (10%), MODTRAN model (1%) and solar irradiance (1%) using a simulation method with the aid of MODTRAN 4.0 model, and a total uncertainty of 2.12% is acquired.

Cloud and shadow detection and removal for Landsat-8 data

Since 1972, Landsat program has experienced six successful missions that have contributed to nearly 40 years record of Earth Observations for monitoring the land cover and change dynamics. The successful launch of the Landsat Data Continuity Mission (LDCM, now named Landsat 8) on February 11, 2013 continues the mission of collecting images of the Earth with an open (free) data policy. Landsat 8 carries two push broom sensors: the Operational Land Imager (OLI) will collect data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS) will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. However, cloud and associated cloud shadows frequently obscure the detection of land surface and restrict the the analysis of change trends over time. This paper presents a new method to detect and remove cloud and cloud shadows using the Landsat 8 first Image data (WRS2: Path/Row =33/32, acquired on March 18, 2013). The method uses six bands for transformation to calculate intensity of cloud and cloud shadows from the nine spectral bands and was further removed. The method takes advantage of spectral information. The validation demonstrates that cloud and cloud shadows contaminated pixels were accurately detected with overall accuracies of 98 and 97%, respectively. However, for thick cloud and cloud shadows, the performance of this method was limited. With further development there is potential for this method using for atmospheric corrections to improve landscape change detection.

Land surface temperature retrieved from airborne multispectral scanner mid-infrared and thermal-infrared data.

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes at local/global scales. In this paper, a LST retrieval method was proposed from airborne multispectral scanner data comparing one mid-infrared (MIR) channel and one thermal infrared (TIR) channel with the land surface emissivity given as a priori knowledge. To remove the influence of the direct solar radiance efficiently, a relationship between the direct solar radiance and water vapor content and the view zenith angle and solar zenith angle was established. Then, LST could be retrieved with a split-window algorithm from MIR/TIR data. Finally, the proposed algorithm was applied to the actual airborne flight data and validated with in situ measurements of land surface types in the Baotou site in China on 17 October 2014. The results demonstrate that the difference between the retrieved and in situ LST was less than 1.5 K. The bais, RMSE, and standard deviation of the retrieved LST were 0.156 K, 0.883 K, and 0.869 K, respectively, for samples.

Land Surface Temperature Retrieval Using Nighttime Mid-Infrared Channels Data From Airborne Hyperspectral Scanner

Compared with thermal infrared (8-14 μm) spectrum, mid-infrared (MIR) spectrum is less sensitive to land surface emissivity (LSE) for estimating land surface temperature (LST). This work addressed the retrieval of LST from two adjacent MIR (3-5 μm) night-time airborne hyperspectral imager (AHS) simulated data with a split-window method, which can be expressed as a linear combination of the brightness temperature measured in two adjacent MIR channels with coefficients depending on LSE, view zenith angle (VZA), and water vapor content (WVC). Meanwhile, the LST retrieval accuracy for various channel combination was investigated and it was noted that the AHS channels 66 (3.5-4.25 μm) and 68 (4.25-5.0 μm) were the optimal channels for LST retrieval with a root-mean-square error (RMSE) less than 0.4 K for dry atmosphere and less than 0.5 K for wet atmosphere. Finally, the sensitivity analysis in terms of the instrumental noise, the uncertainties of LSE, and WVC were performed. It is worth noting that the combination of CH66 and CH68 performed well, and the LST retrieval errors were less than 0.5, 0.2, and 0.3 K caused by an noise equivalent delta temperature (NEΔT) of 0.33 K, WVC error of 20%, and LSE error of 0.01, respectively.