Model MODTRAN Research Articles

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data

As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.

Calculation of the absorption coefficient of air at different heights using the HITRAN and ACE-FTS databases

Subject of study. Increasing requirements for the accuracy of modeling the brightness characteristics of the atmosphere at different heights motivate the development of more detailed methods for the calculation of the absorption coefficient of air. Method. Modern databases of satellite sounding of the atmosphere that contain information on the volume fractions of air components at different heights in combination with a database of the parameters of the spectral lines of gases can be used in this task to calculate the spectral absorption coefficient. The possibility of specifying the geographic position of the height profiles of the air components (by specifying latitude and longitude) is an additional advantage of using the databases of satellite sounding. This method enables the differences in air compositions above different regions of the Earth to be considered. These databases usually provide information starting from a certain height; thus, databases of satellite sounding must be combined with the data obtained either from ground-based meteorological stations or using model distributions. The ACE-FTS database was used in this study, and it was combined with the standard atmosphere MODTRAN models at low heights. A filter for the ACE-FTS database was implemented using C++. The HITRAN database was used to calculate absorption coefficients. The ACE-FTS database provides data for 43 components of air, and the HITRAN database contains data for line-by-line calculation for 27 of these components and data on spectral absorption cross-sections for the remaining 16 components. Main results. A program for the calculation of the absorption coefficient of a multicomponent gas mixture was developed using C++. A Voigt profile was used in the line-by-line calculation, the effects of pressure and temperature on the line shift were considered, and line broadening owing to the following processes was considered: self-broadening, broadening resulting from interaction with water and carbon dioxide molecules, and broadening owing to interaction with other components of the air. The software explicitly considers the isotopic composition of molecules, thus enabling the calculation of not only the air mixture but also an arbitrary mixture with specified isotopic composition, information on which is in the HITRAN database. Moreover, the data of the high-temperature version HITEMP are considered. Practical significance. The combination of the ACE-FTS and HITRAN databases enables the calculation of a detailed profile of the spectral absorption coefficient of air for the specified month, temperature at the surface of the Earth, and geographic latitude and longitude, thus increasing the information content of the input data for the calculation of the brightness and transmission of the atmosphere at different heights. The data readout rate (parsing) and calculation speed of the developed program and the program HAPI implemented using Python are compared.

Simulation of solar-induced chlorophyll fluorescence by modeling radiative coupling between vegetation and atmosphere with WPS

Recent advances in instruments and retrieval methods enable measurements of solar-induced chlorophyll fluorescence (SIF) across a wide range of scales. Radiative transfer (RT) models for simulating scattering and (re-)absorption of SIF provide a powerful tool to study the upscaling of SIF signal from leaf level to terrestrial ecosystems. Based on the Monte Carlo ray-tracing (MCRT) model, WPS (Weighted Photon Spread), we made major extensions with new functionalities and systematic evaluation of the new modules. By modeling the radiative coupling between atmosphere and land surface with the same MCRT method, the non-fluorescent and SIF radiance received by sensors can be simulated at levels from top-of canopy to top-of-atmosphere (TOA) in a coherent manner. New extension to represent the three-dimensional (3-D) canopies with geometrical primitives composed of turbid medium makes the hyperspectral simulation (especially SIF) for a sensor with medium spatial resolution at kilometer-scale feasible and practical. Evaluations through ROMC (Radiation transfer model intercomparison Online Model Checker) show that the accuracy of the new module of 3-D structure representation in WPS is within 1% of the reference solution. The spectra of TOA radiance and SIF and their components simulated at nadir by WPS agree closely with those simulated by the coupled SCOPE and MODTRAN models with the coefficient of determination (R2) higher than 0.99 and the average absolute relative error (AARE) lower than 6.39%; for angular distributions of TOA radiance and SIF at 685 nm and 740 nm, R2 is higher than 0.81 and AARE is lower than 6.94%. Comparisons of the spectra of TOA radiance and SIF and their components simulated at nadir by WPS and the DART model give R2 higher than 0.99 and AARE lower than 3.5%; R2 is higher than 0.92 and AARE is lower than 5.92% for the TOA angular simulations. The WPS model was also evaluated by hyperspectral measurements through unmanned aerial vehicle at different altitudes, which shows that WPS can reproduce the spectral features of a rapeseed crop. WPS can be used as a versatile tool to assess the impacts of various factors on the SIF signal and to evaluate the SIF retrieval methods under different conditions.

A consistency method of discharge detection results at solar‐blind ultraviolet based on a radiation transmission model

Solar-blind ultraviolet (UV) discharge detection based on UV signals in the solar-blind band from 240 to 280 nm is an effective non-contact discharge detection method for electrical equipment, but there is still a problem that the detection results of different types of UV imagers are not comparable. To this end, by using standard light source and MODTRAN model, the relationship between the facular area and the radiant flux received by UV imagers was investigated by establishing a radiation transmission model for UV detection under different environmental conditions. Through analysis, it was found that the influence of humidity on the transmittance of the solar-blind band in atmosphere can be neglected, and the transmittance is slightly affected by atmospheric pressure, temperature, and detection distance under conditions suitable for UV detection. Moreover, a consistency formula for UV detection results was proposed, thus forming a consistency method for detection results suitable for various types of UV imager. Finally, the feasibility of the consistency method was verified by UV discharge detection tests with a rod-plate model and the absolute irradiance at discharge points of the rod-plate model measured by a grating spectrometer. The consistency method is conducive to the promotion of UV discharge detection technology.

Mapping of thermal power plant emitted atmospheric carbon dioxide concentration using AVIRIS-NG data and atmospheric radiative transfer model simulations

Our study demonstrates the potential of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral data and radiative transfer modeling to retrieve the atmospheric carbon dioxide (CO2) concentration from point source emission of one of India’s major coal-fed super thermal power plants at Kota, Rajasthan, India. AVIRIS-NG data with synchronous in situ measurements were collected as a part of the first ISRO-NASA joint airborne hyperspectral science campaign in India. The method utilized in our study includes theoretical simulations of at-sensor radiance in AVIRIS-NG spectral bands through the atmospheric radiative transfer model MODTRAN. Simulations were performed for the specific scene-sensor-atmospheric conditions pertaining to AVIRIS-NG overpass over Kota site. To eliminate the interfering effect of atmospheric water vapor with CO2 concentrations, simulations pertaining to variable water vapor values varying from 0.25 to 4.5 g / cm2 were also carried out, which in turn were utilized to normalize the water vapor effects. Based upon the simulation results pertaining to the specific absorption bands of CO2 in the shortwave infrared spectral range of AVIRIS-NG radiance data, a theoretical model was established between continuum interpolated band ratio (CIBR) and CO2 concentration. The CIBR − CO2 model coefficients for each of the water vapor subranges were then applied to AVIRIS-NG L1 radiance data to obtain the CO2 concentration map of the study area. A distinct CO2 plume could be detected from the coal-fed Kota Super Thermal Power Station with the CO2 concentration (XCO2) of the order of more than 500 ppmv near the power plant stacks. A range of XCO2 from 400 to 550 ppmv was observed within the scene. Our study has provided promising results in mapping the atmospheric CO2 content from the point source using the high-resolution airborne hyperspectral AVIRIS-NG data.

Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions

Due to the complexity and heterogeneity mechanism of aerosol scattering and absorption, there is no systematic operational net surface shortwave radiation (NSSR) retrieval algorithm for considering the forces of different aerosols. This study is to estimate NSSR under dust aerosol conditions. A parameterized model is proposed to quantify the net shortwave radiative forcing (ASRF) of aerosol. Considering the particularity of dust aerosol on shortwave radiation and its mechanisms, aerosol optical properties and atmospheric water vapor content (WVC) are used to quantify the ASRF, and the previous Tang’s parameterization model for estimating NSSR is refined to estimate NSSR under dust aerosol conditions. On this basis, a more accurate radiative transfer model MODTRAN 5 is primitively utilized to simulate and fit the complex relationship. Moreover, the coefficients for parameterization are recalculated by an improved algorithm under diverse types of aerosols. Finally, the NSSR measurements in the study areas and the NSSR products from the MODIS satellites are utilized to verify the retrieved NSSR. The root-mean-square error (RMSE) is below 11 W/m2 under various land cover types and the research findings can significantly increase the estimation accuracy of NSSR around 40 W/m2 under dust aerosol conditions.

Surface Temperature Retrieval From Gaofen-5 Observation and its Validation

Surface temperature (ST) plays a great role in urban heat island effect, environment monitoring, earth resources monitoring, and water balance at local and global scales. The Chinese Gaofen-5 (GF5) satellite can capture Earth’s thermal infrared information for use in national high-resolution Earth observations. In this article, the nonlinear split-window algorithm and the refined generalized split-window method are used to retrieve sea surface temperature (SST) and land surface temperature (LST) from GF5 observations. For different atmospheric and surface conditions, the algorithm coefficients are calculated using a statistical regression method from the numerical values simulated with the atmospheric radiative transfer model MODTRAN 5.0. The simulation results show that the root mean square error (RMSE) for SST and LST retrieval ranges from 0.09 K to 0.46 K and 0.19 K to 0.69 K, respectively, with increasing water vapor content (WVC). To validate the retrieved STs, Moderate Resolution Imaging Spectroradiometer (MODIS) STs extracted from the MYD11A1 product are used. Note that the RMSEs of both the LST and SST are less than 3.3 K. The RMSE for SST retrieval varies from 1.2 K to 1.45 K, with a mean value of 1.33 K; the RMSE for LST retrieval ranges from 1.57 K to 3.3 K, with a mean value of 2.41 K.

DART: Improvement of thermal infrared radiative transfer modelling for simulating top of atmosphere radiance

Land surface temperature (LST) is increasingly needed for studying the functioning of the Earth's surface at local to global scale. Radiative transfer (RT) models that simulate top of atmosphere (TOA) radiance are essential tools to derive accurate LST from thermal infrared (TIR) signals of Earth observation (EO) satellites. DART (Discrete Anisotropic Radiative Transfer) is one of the most accurate and comprehensive three-dimensional models that simulate RT in the Earth-atmosphere system. Up to version 5.7.3, the mean absolute error (MAE) of DART atmospheric TIR radiance of six standard atmospheres (USSTD76, TROPICAL, MIDDLATSUM, MIDDLATWIN, SUBARCSUM, SUBARCWIN) over 3.5 μm - 20 μm was 3.1 K compared to the reference atmospheric RT model MODTRAN, which is much larger than the 1 K accuracy needed by most LST applications. Also, the radiance error reached 2.6 K for some TIR bands whereas the noise equivalent differential temperature (NeDT) of satellite TIR sensor is usually less than 0.4 K. Recently, the DART atmospheric RT modelling was greatly improved by (1) introducing the equivalent absorption cross-section of five most absorbing gases (H2O, CO2, O3, CH4, N2O), and (2) implementing a double-layer thermal emission method. The MAE of DART atmospheric TIR radiance of six standard atmospheres and actual atmospheres over France and the Mediterranean Sea is now better than 1.0 K. The band radiance error is less than 0.2 K in the EO satellite TIR bands. DART is still accurate if the temperature profiles of standard atmospheres are offset by less than 6 K and if the viewing zenith angle is less than 50°. In short, the improved DART meets the requirements of both LST applications, and present and future TIR EO satellite missions. It is already available to scientists (https://dart.omp.eu).

Multiple geometry atmospheric correction for image spectroscopy using deep learning

The goal of this research is to develop a general deep learning solution for atmospheric correction and target detection using multiple hyperspectral scenes. It is assumed that the scenes differ only in range and viewing angles, that they are acquired in rapid sequence using an airborne sensor orbiting a target, and that the target and the atmosphere remain invariant within the time scale of the collection. Several hundred thousand hyperspectral simulations were performed using the MODTRAN model and were used to train the deep learning solution, as well as to validate the proposed method. The input to the deep learning solution is a matrix of the simulated radiances at the sensor as function of wavelength and elevation angles. The output is atmospheric upwelling, downwelling, and transmission. This solution is repeated for all or a subset of pixels in the scene. We focus on emissive properties of targets, and simulations are performed in the longwave infrared between 7.5 and 12 μm. Results show that the proposed method is computationally efficient and it can characterize the atmosphere and retrieve the target spectral emissivity within one order of magnitude errors or less when compared with the original MODTRAN simulations.

Radiometric Cross-Calibration of GF-4/VNIR Sensor With Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS for Monitoring Its Degradation

Optical satellite sensor generally suffers from drifts and biases relative to their prelaunch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transferring radiometric values between satellite sensors via a stable target. The data quality of visible and near-infrared (VNIR) sensor onboard Gaofen-4 (GF-4) satellite seriously depends on vicarious calibration, which is performed once every year, thus, a cross-calibration approach is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly have similar variation trend as a sinusoidal function during 2017 and 2018, varying from 0.00023 to 0.00079. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of referenced sensors, the uncertainty of the MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%–6.64%.

Supplement of the radiance-based method to validate satellite-derived land surface temperature products over heterogeneous land surfaces

Land surface temperature (LST) retrieved from satellite remote sensing data has become a key parameter in research on global environmental change; therefore, the acquisition of accurate satellite-derived LST information is crucial for the diagnosis and analysis of global change. However, it is relatively difficult to obtain the true value of a pixel due to the scale mismatch between in situ measurements and satellite-based observations, especially for commonly heterogeneous and nonisothermal land areas, which greatly increases the difficulty in estimating pixel-representative LST values from in situ measurements for validation of satellite-based LST products. In this study, a supplemented radiance-based (SR-based) validation method was developed to evaluate the latest moderate resolution imaging spectroradiometer (MODIS) Collection 6 Level 2 daily LST/land surface emissivity (LSE) products over a heterogeneous and nonisothermal region of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, West China. In the SR-based framework, pixel-representative LST values are simulated by the MODTRAN model from the corresponding in situ measurements, such as LSE and atmospheric profile measurements, to evaluate the MODIS LST products. The validation results show that the MODIS daytime LST products from the Aqua satellite (MYD11_L2) have a greater accuracy than those from the Terra satellite (MOD11_L2). Analyses of the effect factors indicate a strong correlation between the errors in the MOD11_L2 LST product and the corresponding difference in the MODIS brightness temperature between bands 31 and 32. Although the requirement of synchronous or quasisynchronous in situ measurements for the validated LST products may limit the applicability of the SR-based method, it is still an effective and simple method for validating satellite-derived LST products over mixed pixels. Our method is an indispensable supplement for the validation methods of satellite-derived LST products, and it can be applied in West China and other areas with heterogeneous land surfaces.

Land surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform.

We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, with which the aerosol optical thickness @550 nm (AOT@550nm), columnar water vapor (CWV) could also be estimated from the hyperspectral data collected over UAV platform. Then, the method was tested on both the synthetic and field campaign-collected hyperspectral data by an UAV-VNIRIS (UAV visible/near-infrared imaging hyperspectrometer) with the spectral range covering from 400 to 1000 nm. The retrieval results were validated with theoretical values from synthetic data and truth values from field campaign measurements. The results show that the averaged MAE (mean absolute error) and RMSE (root mean squared error) of measured and retrieved surface reflectance based on estimated AOT@550nm and CWV is 0.0134 and 0.0130. Meanwhile, the averaged MAE and RMSE of measured and retrieved surface reflectance based on ground measured AOT@550nm and CWV is 0.0101 and 0.0112. The results show that our introduced method has good agreement with the method based on ground-measured AOT@550nm and CWV. These encouraging results also indicate that the introduced physical-based atmospheric approach provides a quick and reliable way to acquire the land surface reflectance from UAV platform-observed hyperspectral data for further quantitative remote sensing applications.

Pwv_kpno: A Python Package for Modeling the Atmospheric Transmission Function Due to Precipitable Water Vapor

We present a Python package, pwv_kpno, which provides models for the atmospheric transmission due to precipitable water vapor (PWV) at user-specified sites. Using the package, ground-based photometric observations taken between 3000 and 12,000 Å can be corrected for atmospheric effects due to PWV. Atmospheric transmission in the optical and near-infrared is highly dependent on the PWV column density along the line of sight. By measuring the delay of dual-band GPS signals through the atmosphere, the SuomiNet project provides accurate PWV measurements for hundreds of locations around the world. The pwv_kpno package uses published SuomiNet data in conjunction with MODTRAN models to determine the modeled, time-dependent atmospheric transmission. A dual-band GPS system was installed at Kitt Peak National Observatory (KPNO) in the spring of 2015. Using measurements from this receiver we demonstrate that we can successfully predict the PWV at KPNO from nearby dual-band GPS stations on the surrounding desert floor. The pwv_kpno package can thus provide atmospheric transmission functions for observations taken before the KPNO receiver was installed. Using PWV measurements from the desert floor, we correctly model PWV absorption features present in spectra taken at KPNO. We also demonstrate how to configure the package for use at other observatories.

常用遥感影像大气校正辐射传输模型对比研究

研究区域选在AERONET在中国地区代表性较强且观测时间序列较长的SACOL站点。运用该站点的Landsat5 TM影像数据，用6S模型、MODTRAN模型、ATCOR模型在没有参数的条件下分别对这一影像进行大气校正，再用影像实地测量的参数算出更接近真实地表反射率的的地表反射率对3种不同模型算出的地表反射率进行比较研究，结合NDVI值比较。结果显示FLAASH能更好地获得地表反射率，6S能使植被更突出，ATCOR处理速度快。在今后大气校正中可结合3种模型特点校正图像。 The study area is selected for the SACOL site with an important representation of AERONET in China and a longer observation time series. The Landsat5 TM image data of the site, the 6S model, MODTRAN model and ATCOR model are used for atmospheric correction of this image without measured data. The surface reflectance which is closer to the true is calculated with the parameters measured. Then the surface reflectance calculated by 3 different models is compared combined with NDVI value. The results show that FLAASH can get the surface reflectance better, 6S can make the vegetation more prominent, and ATCOR’s treatment is fast. In future atmospheric correction, the image can be corrected by combining the characteristics of 3 models.

Investigation of detecting biological aerosol by passive Fourier transform infrared spectroscopy technology based on MODTRAN model

The problem of detecting the biological aerosol at low elevation angle is analyzed and discussed by using MODTRAN model. First of all, the atmospheric model and profile of MODTRAN model are introduced for the biological aerosol detection by Fourier transform infrared (FTIR) spectroscopy. According to the passive detection of biological aerosol by FTIR spectroscopy, the radiation brightness difference L between the background and the target biological aerosol is calculated by using the radiative transfer theory and the simplest three-layer model. The signal value 2Lt under the actual circumstance is derived from the derivative of L combined with the noise equivalent spectral radiance value of the spectrometer. Finally, the detection limit of biological aerosol for each atmospheric mode is predicted with the passive remote sensing method. The limit concentration of detection for each atmospheric mode is different due to the differences in boundary layer temperature, transmittance, and background radiation brightness of atmospheric model, and it is also related to the absorption coefficient of biological aerosol. It is shown that the passive remote sensing of FTIR technology can detect the presence of the biological aerosol. Therefore, the detection of the biological aerosol is feasible. It presents a method of detecting the biological aerosol cloud under the actual circumstance.

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas.

Analysis of the Proportion of Surface Reflected Radiance in Mid-Infrared Absorption Bands

Image simulation of remote sensing systems is important for the development of new instruments and validations of data processing algorithms. In image simulation process, surface scene simulation is a fundamental issue and usually has the first priority. For two mid-infrared absorption bands near 2.7 μm and 4.3 μm, although there are a lot of applications in remote sensing field, relevant research on surface scene simulation is very limited. In these two mid-infrared ranges, surface radiance is a combination of reflected and emitted radiance. However, the radiance is generally reduced because of strong absorption by atmosphere. Therefore, analysis of surface reflected radiance is essential for simulation work. In this paper, we use a radiative transfer model MODTRAN to simulate proportions of surface reflected radiance for common ground materials under various observation conditions. The obtained results show that proportions of studied materials are 0.8%-99.8% in the band of 2.63-2.83 μm and 1.1%-94.8% in the band of 4.2-4.5 μm. The proportions of surface reflected radiance in both absorption bands are affected by surface reflectivity. In addition, in the band of 2.7 μm the proportion of surface reflected radiance is sensitive to solar geometry, water vapor content and surface temperature, whereas it is insensitive in the band of 4.3 μm. Based on these results, we conduct that both reflection and emission are important for surface scene simulations in the 2.7 μm and 4.3 μm absorption bands.

Global sensitivity analysis of the spectral radiance of a soil–vegetation system

We analyzed the sensitivity of Top-Of-Atmosphere (TOA) radiance and surface reflectance of a soil–vegetation system to input biophysical and biochemical parameters using the coupled Soil–Leaf-Canopy radiative transfer model SLC and MODTRAN. We applied variance-based global sensitivity analysis for different atmospheric conditions and observation configurations. Among 23 input parameters, crown coverage, leaf area index, leaf inclination distribution function and soil moisture were found to be the most influential parameters driving the output variance of the radiance between 400 and 2500nm with a few exceptions. Hapke's soil parameters and the canopy layer dissociation factor were recognized to have marginal influence on the output radiance. It is also found that a large portion of uncertainty in the output radiance is driven by the interaction effects among input parameters in the visible (~550nm), whereas the red-near infrared (~670nm), seems to have fewer interaction effects. The effect of solar/view direction is found to be significant on TOA radiance sensitivity to the input parameters. The results also confirmed that the sensitivities of surface reflectance are comparable to the TOA radiance sensitivities when the atmosphere is clear and visibility is high. Since coupled surface-atmosphere RT models can be computationally intensive, this work also introduces an improvement to the design and sampling of screening methods for efficient sensitivity analysis of computationally expensive models. The improvement is based on three elements: a) generating sample points by Sobol's sequence generator; b) variational analysis in the parameter space using the winding stairs method; c) use of mean and variance sensitivity measures. The results with 1200 model runs demonstrated high correlation (92%) with variance-based global sensitivity analysis using 49,152 model runs, in determining the most influential and non-influential parameters.