A Simplified Scheme of the Generalized Layered Radiative Transfer Model

In this paper, firstly, a simplified version （SGRTM） of the generalized layered radiative transfer model （GRTM） within the canopy, developed by us, is presented. It reduces the information requirement of inputted sky diffuse radiation, as well as of canopy morphology, and in turn saves computer resources. Results from the SGRTM agree perfectly with those of the GRTM. Secondly, by applying the linear superposition principle of the optics and by using the basic solutions of the GRTM for radiative transfer within the canopy under the condition of assumed zero soil reflectance, two sets of explicit analytical solutions of radiative transfer within the canopy with any soil reflectance magnitude are derived： one for incident diffuse, and the other for direct beam radiation. The explicit analytical solutions need two sets of basic solutions of canopy reflectance and transmittance under zero soil reflectance, run by the model for both diffuse and direct beam radiation. One set of basic solutions is the canopy reflectance αf （written as α1 for direct beam radiation） and transmittance βf （written as β1 for direction beam radiation） with zero soil reflectance for the downward radiation from above the canopy （i.e. sky）, and the other set is the canopy reflectance （αb） and transmittance βb for the upward radiation from below the canopy （i.e., ground）. Under the condition of the same plant architecture in the vertical layers, and the same leaf adaxial and abaxial optical properties in the canopies for the uniform diffuse radiation, the explicit solutions need only one set of basic solutions, because under this condition the two basic solutions are equal, i.e., αf = αb and βf = βb. Using the explicit analytical solutions, the fractions of any kind of incident solar radiation reflected from （defined as surface albedo, or canopy reflectance）, transmitted through （defined as canopy transmittance）, and absorbed by （defined as canopy absorptance） the canopy and other properties pertinent to the radiative transfer within the canopy can be estimated easily on the ground surface below the canopy （soil or snow surface） with any reflectance magnitudes. The simplified transfer model is proven to have a similar accuracy compared to the detailed model, as well as very efficient computing.

A Multi-Domain Compression Radiative Transfer Model for the Fengyun-4 Geosynchronous Interferometric Infrared Sounder (GIIRS)

Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.

Soil-Vegetation-Atmosphere Radiative Transfer Model in Microwave Region

The radiative transfer is one of the significant theories that describe the processes of scattering, emission, and absorption of electromagnetic radiant intensity through scattering medium. It is the basis of the study on the quan-titative remote sensing. In this paper, the radiative characteristics of soil, vegetation, and atmosphere were described respectively. The numerical solution of radiative transfer was accomplished by Successive Orders of Scattering (SOS). A radiative transfer model for simulating microwave brightness temperature over land surfaces was constructed, de-signed, and implemented. Analyzing the database generated from soil-vegetation-atmosphere radiative transfer model under Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) configuration showed that the atmospheric effects on microwave brightness temperature should not be neglected, particularly for higher frequency, and can be parameterized. At the same time, the relationship between the emissivities of the different channels was developed. The study results will promote the development of algorithm to retrieve geophysical parameters from mi-crowave remotely sensed data.

Microwave Simulations of Precipitation Distribution with Two Radiative Transfer Models

Two microwave radiative transfer models of precipitating cloud are used to simulate the microwave upwelling radiances emerging from precipitating clouds. Comparison of the simulation results shows that significant difference of microwave upwelling radiances exists between these two radiative transfer models. Analysis of these differences in different cloud and precipitation conditions shows that it is complicated but has certain trend for different microwave frequencies. The results may be useful to quantitative rainfall rate retrieval of real precipitating clouds.

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

An accurate and efficient radiative transfer model for simulating all-sky images from Fengyun satellite radiometers

Forward radiative transfer models(RTM)are an indispensable tool for quantitative applications of satellite radiometers,e.g.,for data calibration,instrument development,retrieval,and so on.In this study,we develop an accurate and efficient RTM for radiometers onboard Fengyun satellites,namely FYRTM(RTM for Fengyun Radiometers).Correlated k-distribution models are developed to improve the computational efficiency for gas absorption,and the effects of cloud and aerosol multiple scattering and emission are accelerated with pre-computed look-up tables.FYRTM is evaluated with a rigorous simulation based on discrete ordinate radiative transfer model(DISORT)as well as a popular fast forward model,i.e.,the Community Radiative Transfer Model(CRTM).Results indicate that FYRTM-based simulations are two to three orders of magnitudes faster than the DISORT-based simulations.Compared to the rigorous model,FYRTM relative errors are within 2%at solar channels,and brightness temperatures(BT)differences are within 1 K at infrared channels.Compared with CRTM,FYRTM is computationally similar at solar channels,but three times faster at infrared channels.Furthermore,simulated reflectances/BTs using FYRTM are in a good agreement with the satellite observations.Overall,FYRTM is capable to simulate satellite observations under different atmospheric conditions,and can be extended to other radiometers onboard the Fengyun satellites(both geostationary and polarorbiting satellites).It is expected to play important roles in future applications with Fengyun observations.

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).

Terahertz band simulations using two different radiative transfer models

A high-resolution dual-band terahertz(THz) radiometer was designed to measure vertical distributions of chemical elements in the middle atmosphere of the Tibetan Plateau. A forward simulation, which always should be conducted firstly for the development of a matching retrieval algorithm, has not been done before. We use two radiative transfer models, ARTS and AM, to simulate the water vapor, ozone and carbon monoxide spectra on the plateau based on the spectral design of the THz radiometer. The emission line characteristics of the three gases in this spectral band are identified. Reasons for the differences in the spectral simulations between the two models are analyzed for individual gases. The impact of several different spectral parameter settings on the simulations are evaluated through a series of sensitivity experiments. This study suggests that the ARTS is more suitable for the development of the THz radiometer retrieval algorithm. An optimal parameter setting of the ARTS for the three elements are given.

NUMERICAL MODELING OF RADIATIVE TRANSFER FOR MICROWAVE REMOTE SENSING

An overall vector radiative transfer theory was developed for numerical modeling, in both active and passive microwave remote sensing. The Theory and approaches are briefly summerized.To quantitatively understand scattering and thermal emission from targets in active and passive remote sensing, we have developed an overall vector radiative transfer theory for a set of theoretical models of discrete scatterer and continuous random media for the earth terrain (wet soil, vegetation, snow, sea-ice, etc.) and atmosphere, and numerical approaches for simulation, data analysis, and parameter sensitivity test. Our numerical results favorably agreed with experimental data in microwave re mote sensing of various earth surfaces. Main approaches are briefly summerized herewith.

A Comparison of Two Canopy Radiative Models in Land Surface Processes

This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model （developed by the authors） in land surface processes -for different canopies under direct or diffuse radiation conditions. The comparison indicates that there are significant differences between the two models, especially in the near infrared （NIR） band. Results of canopy reflectance from the two-stream model are larger than those from the generalized model. However, results of canopy absorptance from the two-stream model are larger in some cases and smaller in others compared to those from the generalized model, depending on the cases involved. In the visible （VIS） band, canopy reflectance is smaller and canopy absorptance larger from the two-stream model compared to the generalized model when the Leaf Area Index （LAI） is low and soil reflectance is high. In cases of canopies with vertical leaf angles, the differences of reflectance and absorptance in the VIS and NIR bands between the two models are especially large. Two commonly occurring cases, with which the two-stream model cannot deal accurately, are also investigated. One is for a canopy with different adaxial and abaxial leaf optical properties; and the other is for incident sky diffuse radiation with a non-uniform distribution. Comparison of the generalized model within the same canopy for both uniform and non-uniform incident diffuse radiation inputs shows smaller differences in general. However, there is a measurable difference between these radiation inputs for a canopy with high leaf angle. This indicates that the application of the two-stream model to a canopy with different adaxial and abaxial leaf optical properties will introduce non-negligible errors.

Atmospheric radiative transfer simulation for at mospheric correction of remote sensing data

The radiance leaving the earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the earth surface and each atmospheric level that are transmitted to the top of the atmosphere. The radiation emission from the earth surface and the radiance of each atmospheric level can be separated from the radiance at the top the atmospheric level measured by a satellite borne radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models have been developed to provide many options for modeling atmospheric radiation transport, such as LOWTRAN, MODTRAN, 6S, FASCODE, LBLRTM, SHARC, and SAMM. Meanwhile, these models can support the detailed detector system design, the optimization and evaluation of satellite mission parameters, and the data processing procedures. As an example, the newly atmospheric radiative transfer models, MODTRAN will be compared with other models after the atmospheric radiative transfer is described. And the atmospheric radiative transfer simulation procedures and their applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented.

CFD study of non-premixed swirling burners: Effect of turbulence models

This research investigates a numerical simulation of swirling turbulent non-premixed combustion.The effects on the combustion characteristics are examined with three turbulence models:namely as the Reynolds stress model,spectral turbulence analysis and Re-Normalization Group.In addition,the P-1 and discrete ordinate(DO)models are used to simulate the radiative heat transfer in this model.The governing equations associated with the required boundary conditions are solved using the numerical model.The accuracy of this model is validated with the published experimental data and the comparison elucidates that there is a reasonable agreement between the obtained values from this model and the corresponding experimental quantities.Among different models proposed in this research,the Reynolds stress model with the Probability Density Function(PDF)approach is more accurate(nearly up to 50%)than other turbulent models for a swirling flow field.Regarding the effect of radiative heat transfer model,it is observed that the discrete ordinate model is more precise than the P-1 model in anticipating the experimental behavior.This model is able to simulate the subcritical nature of the isothermal flow as well as the size and shape of the internal recirculation induced by the swirl due to combustion.

Consistency of Tropospheric Water Vapor between Reanalyses and Himawari-8/AHI Measurements over East Asia

High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor(WV),thereby enhancing our understanding of the deficiencies of WV in reanalysis datasets.Based on daily measurements from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite in 2016,the bias features of multilayer WV from six reanalysis datasets over East Asia are thoroughly evaluated.The assessments show that wet biases exist in the upper troposphere in all six reanalysis datasets;in particular,these biases are much larger in summer.Overall,we find better depictions of WV in the middle troposphere than in the upper troposphere.The accuracy of WV in the ERA5 dataset is the highest,in terms of the bias magnitude,dispersion,and pattern similarity.The characteristics of the WV bias over the Tibetan Plateau are significantly different from those over other parts of East Asia.In addition,the reanalysis datasets all capture the shift of the subtropical high very well,with ERA5 performing better overall.

Four-stream Radiative Transfer Parameterization Scheme in a Land Surface Process Model

Accurate estimates of albedos are required in climate modeling. Accurate and simple schemes for radiative transfer within canopy are required for these estimates, but severe limitations exist. This paper developed a four-stream solar radiative transfer model and coupled it with a land surface process model. The radiative model uses a four-stream approximation method as in the atmosphere to obtain analytic solutions of the basic equation of canopy radiative transfer. As an analytical model, the four-stream radiative transfer model can be easily applied efficiently to improve the parameterization of land surface radiation in climate models. Our four-stream solar radiative transfer model is based on a two-stream short wave radiative transfer model. It can simulate short wave solar radiative transfer within canopy according to the relevant theory in the atmosphere. Each parameter of the basic radiative transfer equation of canopy has special geometry and optical characters of leaves or canopy. The upward or downward radiative fluxes are related to the diffuse phase function, the G-function, leaf reflectivity and transmission, leaf area index, and the solar angle of the incident beam. The four-stream simulation is compared with that of the two-stream model. The four-stream model is proved successful through its consistent modeling of canopy albedo at any solar incident angle. In order to compare and find differences between the results predicted by the four- and two-stream models, a number of numerical experiments are performed through examining the effects of different leaf area indices, leaf angle distributions, optical properties of leaves, and ground surface conditions on the canopy albedo. Parallel experiments show that the canopy albedos predicted by the two models differ significantly when the leaf angle distribution is spherical and vertical. The results also show that the difference is particularly great for different incident solar beams. One additional experiment is carried out to evaluate the simulations of the BATS land surface model coupled with the two- and four-stream radiative transfer models. Station observations in 1998 are used for comparison. The results indicate that the simulation of BATS coupled with the four-stream model is the best because the surface absorbed solar radiation from the four-stream model is the closest to the observation.

Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor

Three-dimensional(3-D)Monte Carlo-based radiative transfer(MCRT)models are usually used for benchmarking in intercomparisons of the canopy radiative transfer(RT)simulations.However,the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests,due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales.Fortunately,some of important tree structure parameters such as canopy height and tree density distribution have been available globally.This enables to run the intermediate complexities of the 3-D MCRT models.We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density.It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms.The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA,respectively.Results demonstrated that the simulations of bidirectional reflectance factor(BRF)based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error(RMSE)and relative RMSE(rRMSE)ranging from 0.002 to 0.006 and from 0.7%to 19.8%,respectively.Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%,respectively.Although the results from the current study are limited in two boreal forest stands,our approach has the potential to generate stand structures for different forest biomes.

Evaluation of Spectral Scale Effects in Estimation of Vegetation Leaf Area Index Using Spectral Indices Methods

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R^2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Modeling Study of the Global Distribution of Radiative Forcing by Dust Aerosol

To quantitatively understand the dust aerosol effects on climate change, we calculated the global dis-tribution of direct radiative forcing due to dust aerosol under clear and cloudy skies in both winter and summer, by using an improved radiative transfer model and the global distribution of dust mass concentra-tion given by GADS （Global Aerosol Data Set）. The results show that the global means of the solar forcing due to dust aerosol at the tropopause for winter and summer are -0.48 and -0.50 W m-2, respectively; the corresponding values for the longwave forcing due to dust are 0.11 and 0.09 W m-2, respectively. At the surface, the global means of the solar forcing clue to dust are -1.36 W m-2 for winter and -1.56 W m-2 for summer, whereas the corresponding values for the longwave forcing are 0.27 and 0.23 W m-2, respectively. This work points out that the absolute values of the solar forcing due to dust aerosol at both the tropopause and surface increase linearly with the cosine of solar zenith angle and surface albedo. The solar zenith angle influences both the strength and distribution of the solar forcing greatly. Clouds exert great effects on the direct radiative forcing of dust, depending on many factors including cloud cover, cloud height, cloud water path, surface albedo, solar zenith angle, etc. The effects of low clouds and middle clouds are larger than those of high clouds. The existence of clouds reduces the longwave radiative forcing at the tropopause, where the influences of low clouds are the most obvious. Therefore, the impacts of clouds should not be ignored when estimating the direct radiative forcing due to dust aerosol.

An improved QuikSCAT wind retrieval algorithm and eye locating for typhoon

This paper proposes a rain considered geophysical model function （GMF）, to be noted as GMF plus Rain. GMF plus Rain is based on the basic raidative transfer model with attenuation and scattering effects of rain on radar signal considered. Combined with the NSCAT2 GMF and the rain correction model, the GMF plus Rain model is used to retrieve the ocean wind vectors from the collocated QuikSCAT and SSM/I rain rate data for typhoon Melor. The resulting wind speed estimates of typhoon Melor show improved agreement with the wind fields derived from the best track analysis of Japan Meteorological Agency （JMA）. The results imply that compared with the GMF model, the GMF plus Rain model can improve the precision of wind retrieval under the rain condition. Then, a new general algorithm of locating the eye of typhoon through the normalized radar cross section （NRCS） is proposed. The implementation of this algorithm in the ten QuikSCAT observations of typhoon Melor suggests that this algorithm is effective.

Efficient Application of the Radiance Enhancement Method for Detection of the Forest Fires Due to Combustion-Originated Reflectance

The existing methods for detection of the cloud scenes are applied at relatively small spectral range within shortwave upwelling radiative wavelength flux. We have reported a new method for detection of the cloud scenes based on the Radiance Enhancement (RE). This method can be used to cover a significantly wider spectral range from 1100 nm to 1700 nm by using datasets from the space-orbiting micro-spectrometer Argus 1000. Due to high sunlight reflection of the smoke originated from the forest or field fires the proposed RE method can also be implemented for detection of combustion aerosols. This approach can be a promising technique for efficient detection and continuous monitor of the seasonal forest and field fires. To the best of our knowledge this is the first report showing how a cloud method can be generalized for efficient detection of the forest fires due to combustion-originated reflectance.