Land surface temperature (LST) is a crucial parameter driving water and heat exchange at the surface-atmosphere interface. Satellite-derived LST require rigorous validation to ensure its reliability in Earth system modeling and climate change research. To address validation accuracy degradation caused by cloud contamination artifacts and satellite-ground spatiotemporal mismatch errors, conventional mean- and median-based outlier detection methods were commonly used in the validation of satellite-derived LST products using in situ measurements. However, both methods are based solely on the degree of deviation within statistical data itself, without considering the uncertainties associated with satellite-derived and ground-based LST. This limitation could result in biased identification of outliers in satellite-derived LST validation. In this study, an uncertainty-based method was proposed to detect outliers in the validation of MODIS-derived LST using in situ measurements. This method quantifies total LST uncertainty budgets to flag anomalous data points by integrating uncertainties from both satellite retrievals and ground observations. Validation results across SURFRAD sites demonstrate the method’s efficacy when compared with those without outlier detection. Daytime implementation achieves significant root mean squared error (RMSE) reductions, notably at the BND site with a 3.1 K improvement, while nighttime applications yield marginal enhancements (< 0.4 K), reflecting diminished thermal contrast and uncertainty components during nighttime. The uncertainty-based method consistently outperforms conventional mean- and median-based methods during daytime, with RMSE improvements ranging from 0.2 K at DRA to 2.6 K at BND. Site-specific variations highlight the method’s sensitivity to surface heterogeneity and vegetation dynamics. All methods exhibit comparable performance at night (ΔRMSE < 0.15 K).

Land surface temperature (LST) is a crucial variable in the Earth’s surface system, playing a key role in understanding the exchanges of material and energy between the surface and the atmosphere. Hyperspectral thermal infrared (TIR) data provide new opportunities for developing methods to retrieve LST from satellite observations. However, the typical physical hyperspectral TIR LST retrieval methods are limited by their reliance on accurate atmospheric correction and specific assumptions, which would introduce complexity and reduce applicability. To address these challenges, this study presents a novel LST retrieval framework that combines a Deep Residual Regression Network (DR2N) with the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm, refined by Hampel filtering. In this framework, DR2N is trained on simulated data to efficiently retrieve atmospheric parameters, including upwelling radiance, downwelling radiance and transmissivity, and after that the refined ISSTES algorithm is applied to simulated data covering various surface types, yielding an overall RMSE of 1.89 K and a bias of -0.19 K. Subsequently, to further verify the performance of the proposed algorithm, LSTs over four study areas-Spain, North Africa, Hulunbuir, and the Yellow Sea are retrieved, and are compared with IASI L2 surface temperature products originating from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), showing an RMSE of 0.51 K and a bias of -0.25 K. This confirms the efficacy of the proposed LST retrieval approach.

Land surface temperature (LST) is a critical indicator of the interactions between the Earth’s surface and atmosphere and has long been available from satellite observations in the thermal infrared (TIR) region. Recognized as a primary way to evaluate the accuracy of LSTs, in situ validation is still a challenging task because of uncertainties in ground measurements, spatial scale mismatch between ground and satellite-based measurements, the heterogeneity of natural land surfaces, etc., leading to a lack of consistency among sets of validation results; therefore, to improve robustness against uncertainties, an optimized approach for LST validation via the Kalman filter is presented, and prediction of comprehensive validation estimate (CVE) which is close to “true” value, and more precise than those based on a single measurement alone is obtained. After the uncertainties involved in the validation process are constrained, this method is applied to FengYun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II) LSTs with ground measurements from four sites in China. The results indicate that the CVE is 1.11 K, with an uncertainty of 0.07 K. Additionally, a comparison is performed with the weighted average method, and the efficacy of the Kalman filter approach in enhancing the validation accuracy is confirmed.

MOTIVATION: Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data. RESULTS: We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes. AVAILABILITY AND IMPLEMENTATION: The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

Potatoes, a crucial staple crop, face significant threats from late blight, which poses serious risks to food security. Despite extensive research using ground and unmanned aerial vehicle (UAV) hyperspectral data for crop disease monitoring, satellite-scale identification of diseases, such as potato late blight (PLB) remains limited. This study employs a multiscale analysis approach, integrating high-resolution Sentinel-2 multispectral satellite data with UAV and ground spectral data, to monitor and identify PLB. A key finding of this study is the general similarity in spectral patterns across different scales, with consistent valley values in bands of blue and red and peak values in bands of near infrared (NIR) and narrow NIR, accompanied by a consistent decrease in reflectance correlating with increasing disease severity. Furthermore, the study highlights scale-dependent spectral variations, with changes in bands of Vegetation Red Edge2, Vegetation Red Edge3, NIR, and narrow NIR being more pronounced at the ground scale compared to UAV and satellite scales. Based on the developed red edge index and disease stress index with a suite of machine learning algorithms, we proposed an XGBoost-based model integrating spectral indices for PLB monitoring (PLB-SI-XGBoost). Notably, the proposed model demonstrated the highest average evaluation score of 0.88 and the lowest root-mean-square error (RMSE) of 13.50 during ground-scale validation, outperforming other algorithms. At the UAV scale, the proposed model achieved a robust R-squared value of 0.74 and an RMSE of 18.27. Moreover, the application of Sentinel-2 data for disease detection at the satellite scale yielded an accuracy of 70% in the model. The results of the study emphasize the importance of scale in disease monitoring models and illuminate the potential for satellite-scale surveillance of PLB. The exceptional performance of the PLB-SI-XGBoost model in detecting PLB suggests its utility in enhancing agricultural decision-making with more accurate and reliable data support.

Surface temperature (ST) is a vital physical parameter influencing surface-atmosphere interactions. This study presents an uncertainty-based validation approach applied to Sentinel-3/SLSTR land surface temperature (LST) and sea surface temperature (SST) products.In situmeasurements were obtained from various sites in China, namely, the Dunhuang Gobi site (DHGS), Huailai Guanting Reservoir site (HGRS), Wuliangsuhai Lake site (WLSLS) and Yantai Ocean site (YTOS). The spatial representativeness ofin situmeasurements at each site was assessed using available clear-sky and high-quality ASTER LST products from April 2000 to June 2023. The four sites exhibited high spatial homogeneity, demonstrating suitability for validating STs. Therefore,in situmeasurements from these homogeneous sites were used to validate the Sentinel-3/SLSTR ST products during the daytime and nighttime using a temperature-based method. The results showed that the root mean square error (RMSE) values are lower than 1.6 K, except for those at DHGS. Furthermore, since ground-based ST validation is affected by the coupled effects of surface and atmospheric characteristics, the validation results are different under different atmospheric and surface conditions. Consequently, assessing the consistency among multiple validation results becomes challenging. To address this issue, by assuming the independence of the validation samples, we propose a method for obtaining the key comparison reference value (KCRV) from multiple validation results based on Sentinel-3/SLSTR ST products. The KCRV is close to the ‘true’ value, indicating the high quality of the validation results. For the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR LST products, the KCRVs are 1.91 K and 1.71 K, respectively, with corresponding uncertainties of 0.08 K and 0.08 K, respectively. Similarly, for the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR SST products, the KCRVs are 0.78 K and 0.71 K, respectively, with uncertainties of 0.08 K and 0.07 K, respectively.

Thermal infrared hyperspectral imagery presents a superior capability for capturing intricate spectral details of atmospheres and ground objects compared to multispectral images, thus offering a more nuanced dataset for land surface temperature (LST) retrieval. However, extensive inter-band correlations pose computational challenges and undesirable “dimension disaster” problem. To address this issue, this paper proposes a purpose-built framework of thermal infrared hyperspectral band selection using graph neural network for LST retrieval. Specifically, the thermal infrared hyperspectral data is firstly mapped onto a graph topology, followed by feeding it into a graph attention module with brightness temperature constraints to extract band features. Following this, the extracted band features undergo a comprehensive analysis through a multi-scale convolution module consisting of convolution kernels with multiple sizes, which has more variety and larger receptive fields for calculating the correlation between different bands features, assigning different weights to each band. Finally, a weight selection module is designed to filter the bands based on their assigned weights, creating a subset of bands with greater significance for LST retrieval. Training the designed model, 65100 observations are simulated utilizing MODTRAN, 80% allocated for training and 20% for testing. The experimental results validate the effectiveness of the proposed model, with a Root Mean Square Error (RMSE) of 1.85 K in practical applications on IASI imagery. This accomplishment substantiates the model’s capacity to reliably employ a judiciously selected subset of thermal infrared hyperspectral bands for LST retrieval applications, thus offering a promising contribution to the advancement of thermal infrared hyperspectral image processing methodologies.

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’s field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.

Drought risk assessment is one of the most important basic research topics on the quantitative understanding of the mechanism of drought risk and scientifically reducing the adverse effects of drought, which is of great significance in the theory and practice of developing coping strategies and drought management plans. In this paper, the drought risk on a global scale was quantified according to the hazard, exposure, and vulnerability of drought from 2020 to 2099. In addition, the trends of drought risk variation under two different representative concentration pathways (RCP45 and RCP85) scenarios are analyzed and compared. According to the variation character of drought risk in different scenarios, it is divided into 7 types, and the specific differences of each type are discussed. The results show that (1) the areas with high drought risk are primarily concentrated in populated and high precipitation variability places, such as Pakistan, western India, and central North America. (2) When the greenhouse gas concentration rises from RCP45 to RCP85, the drought risk in about 36.88% of the world will worsen, which is primarily concentrated in southern North America, southeastern South America, southern Africa, southern Oceania, southern Asia, and western Europe.

Satellite-derived high-resolution LST observations are essential for environmental studies. However, the tradeoff between spatial and temporal resolutions largely restricts the application of current LST products. As a consequence, many spatial downscaling or spatiotemporal fusion methods were proposed to overcome this limitation. In this paper, we design a novel empirical weighting method to combine the results from the popular downscaling and fusion methods, thermal sharpening algorithm (TsHARP), and spatial and temporal adaptive reflectance fusion model (STARFM). Specifically, the error of the two methods are firstly estimated and the predictions are blended based on the inverse ratio of the corresponding error. Our method is tested with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing. Compared with the actual ASTER LST, the combining results could both enhance the accuracy and structure similarity, as our method utilizes spatial-temporal-spectral information. Moreover, our method also has the potential for generating more accurate daily high-resolution LSTs.

Due to the spatial heterogeneity within a relatively small distance of urban areas, it is necessary to consider the complex land cover types and three-dimensional geometric structure of urban surface. This study introduces the sky view factor (SVF) to calculate the equivalent emissivity of urban surface. In addition, the thermal radiation of adjacent pixels to target pixels is also considered to establish the urban radiative transfer model. The Landsat-8 collection-2 level-2 science product was taken to validate the proposed urban radiative transfer model. The area within the Fourth Ring Road of Beijing was regarded as the study area, then the land surface temperature retrieval algorithm was applied to estimate urban surface temperature (UST). The results of the UST retrieval algorithm were evaluated by comparing brightness temperature (BT) at the top of atmosphere (TOA) simulated by the Discrete Anisotropic Radiative Transfer (DART) model. The root mean squared error (RMSE) between brightness temperatures estimated by the urban radiative transfer model and those simulated by DART model was less than 0.21 K.

The advanced hyperspectral imager (AHSI) is one of the sensors aboard the Chinese Gaofen-5 (GF-5) satellite, possessing characteristics of high spatial and spectral resolution, as well as width swath. To better understand the radiometric performance of GF-5/AHIS after its launch, this article presents an on-orbit radiometric calibration approach for AHSI visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors from field automatic observations with a field spectrometer in the absence of SWIR measurements. A spectrum extension method was proposed to extend the retrieved surface hyperspectral reflectance in the VNIR spectral ranges to SWIR by incorporating the historical hyperspectral reflectance library. The radiometric calibration coefficients of GF-5/AHSI were calculated by linear fitting of the observed digital number (DN) values with GF-5/AHSI and predicted at-sensor radiances with MODTRAN 5 based on extended hyperspectral surface reflectance. Comparisons with onboard calibration results were also performed, and the averaged relative differences were within 5% with$1\delta $standard deviations less than 10% for most bands, except for those in the atmospheric absorption and low signal-to-noise ratio bands. The comparison results indicate that the on-site radiometric calibration results are consistent with the onboard results, and the operational on-orbit radiometric calibration approach is reliable in the case that there are no measurements in the SWIR spectra range. The on-orbit radiometric performance of GF-5/AHSI rapidly degraded during the first several months after its launch and then tended to be relatively stable.

When using a field calibration site to perform in-orbit radiometric calibration of a space-borne remote sensor, the measured signal by the sensor may contain radiation from adjacent pixels, due to scattering in the Earth’s atmosphere and the sensor viewing characteristics. If this is not accounted for in the modeling, the accuracy of the radiometric calibration will be reduced. Nonuniformities of the ground target are more significant for the calibration of high-resolution sensors. In addition, if the brightness contrast of neighboring targets is not small, the impact of the adjacency effects will be more significant. It is important to quantitatively analyze and estimate the influence of this kind of adjacency effects, to reduce the uncertainty of in-orbit radiometric calibration. To evaluate the adjacency effects caused by atmospheric multiple scattering, this article constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data and developed an adjacency effects simulation method, which considers background reflectance spectral information. Tests on Sentinel-2A and Worldview-3 imagery overpassing the Baotou calibration and validation site (Baotou C&V site) (China) indicate that the proposed modeling method can effectively account for the influence of the adjacency effects in vicarious calibration. Uncertainties of relevant parameters and their contributions to the calibration result were also analyzed, and uncertainty assessment results show that the vicarious radiometric calibration scheme considering adjacency effects correction can bring about a total uncertainty less than 7%.

The Chinese Gaofen-1(GF-1) high resolution satellite loaded with four Wide Field of View (WFV) cameras provides observations with high temporal and spatial resolutions. However, the radiometric calibration accuracy of the GF1/WFV should be given when being used to monitoring the earth. In this study, radiometric calibration of the GF1/WFV was carried out first with automatic instrumented Baotou site. Then, the determined radiometric calibration coefficients were cross validated with the MultiSpectral Imager (MSI) onboard the Sentinel-2 satellite. The preliminary results show that the radiometric performances of the four WFV cameras are relatively stable with averaged relative difference less than -1.85%. The standard deviation of the radiometric calibration coefficients during the period from April 2019 to June 2020 is 0.0056, 0.0081, 0.0072, and 0.0076 with respect to the blue, green, red, and near infrared channel. The results of cross validation with Sentinel-2/MSI suggest that the averaged relative difference is -3.16%, -4.28%, -1.15%, and -3.22% with respect to the blue, green, red, and near infrared channel. The results of cross-validation demonstrate that radiometric calibration of the GF-1/WFV cameras using automatic instrumented Baotou site is feasible and operational. And, it is also essential and necessary to update the on-orbit radiometric calibration coefficients of GF-1/WFV cameras during its' entire lifetime for further quantitative application.

As the growing interest in the use of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) low light data, there is an urgent need to obtain high accuracy product at low radiances. Currently the low light calibration accuracy was previously estimated at a moderate 15% using extended sources while the long-term stability has yet to be characterized. This paper gives a new method to quantitative analysis DNB data by using a specialized ground light point source at night, which is active designed light sources at selected site (Baotou of Inner Mongolia, China). It presents a possibility to resolve the need for SI traceable active light sources to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB.

The DNA methyltransferases (DNMTs) (DNMT3A, DNMT3B and DNMT3L) are primarily responsible for the establishment of genomic locus-specific DNA methylation patterns, which play an important role in gene regulation and animal development. However, this important protein family's binding mechanism, i.e. how and where the DNMTs bind to genome, is still missing in most tissues and cell lines. This motivates us to explore DNMTs and TF's cooperation and develop a network regularized logistic regression model, GuidingNet, to predict DNMTs' genome-wide binding by integrating gene expression, chromatin accessibility, sequence and protein-protein interaction data. GuidingNet accurately predicted methylation experimental data validated DNMTs' binding, outperformed single data source based and sparsity regularized methods and performed well in within and across tissue prediction for several DNMTs in human and mouse. Importantly, GuidingNet can reveal transcription cofactors assisting DNMTs for methylation establishment. This provides biological understanding in the DNMTs' binding specificity in different tissues and demonstrate the advantage of network regularization. In addition to DNMTs, GuidingNet achieves good performance for other chromatin regulators' binding. GuidingNet is freely available at https://github.com/AMSSwanglab/GuidingNet.

Directional reflectance of Baotou sandy calibration site was measured in September 2017. Directional reflectance factors were collected using the Multi-Angles Observation System (MAOS) designed by Academy of Opto-Electronics (AOE), Chinese Academy of Sciences. The directional reflectance was measured at a few viewing and azimuth angles in the 0-30° and 0-360° angular ranges, respectively. Anisotropy and directional effect of surface reflectance were analyzed based on the measured directional reflectance factors. The bidirectional reflectance distribution function (BRDF) model of the calibration site was also modelled, and the model fitting error was approximated to be within 2%. The BRDF model was also used to correct angular difference between ground measurements and satellite measurements in vicarious radiometric calibration carried out in Baotou sandy calibration site, and calibration results with and without angular correction were shown in this work.

Accurate spatialized daily precipitation data plays an important role in meteorology, hydrology and ecology. Tropical Rainfall Measuring Mission (TRMM) precipitation data has been widely used in recent years for the relatively high resolution and large spatial coverage. Among them, two TRMM precipitation products are most commonly used: 3-hour scale (4B42) and monthly scale (3B43). The 3B42 product with a high temporal resolution but low accuracy, while the 3B43 product is the opposite. For hydrological modeling and water resource analysis, the acquisition of daily precipitation data is very important. In most cases, daily precipitation data is obtained by accumulating 3B42 product directly. However, this method ignores the change of precipitation rate. In the case of heavy rainfall, the daily precipitation data from 3B42 data shows a large deviation compared with the daily rainfall observed from rain gauges. Based on the analysis of ground measured daily precipitation and soil moisture data, this paper proposes a temporal disaggregation algorithm of TRMM monthly precipitation products using AMSR2 daily soil moisture data. The results show that this method is simple and feasible, which provide a new reference for the study of temporal downscaling of satellite-based rainfall dataset.

Passive microwave data are much less affected by clouds than TIR data for the retrieval of land surface temperature (LST), providing its unique advantages in global mapping of LST. In this study, a physically-based algorithm for LST retrieval was applied to AMSR2 global brightness temperature data. The performances of this algorithm applied on different land cover types were further evaluated against nighttime MYD11A1 thermal infrared LST products. The results showed that (i) the overall accuracy of the algorithm is about 5.42 K by root mean square error (RMSE) and 2.99 K by bias against MODIS LST during nighttime; (ii) the algorithm overestimates the LST over all land types. The overestimation is most evident over barren/sparsely vegetated surfaces. The algorithm shows that the algorithm has a robust performance comparing with MODIS LST and could be applied to estimate LST effectively.

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

The assessment of Synthetic Aperture Radar (SAR) image resolution is essential to characterize and improve sensor performance, and to make better application of the acquired SAR data. Trihedral corner reflectors and active transponders have been widely used as standard point targets for SAR image spatial resolution assessment. However, these point targets have limitations in straight-forward result reveal and tolerance in deployment and processing errors. In light of the bar-pattern target which widely used for optical image resolution assessment, and to assess the image resolution of the SAR sensors operating at different frequency, different platform (airborne and spaceborne), a permanent bar-pattern target was designed and realized by black gravel and greyish white concrete bars. Gravel size, bar direction and width were carefully calculated according to the requirement of long-term operation. The effectiveness of the target was preliminarily validated by C-band airborne SAR, X-band spaceborne SAR data and optical image, and the result shows that the target is suitable for the spatial resolution assessment of both high-resolution SAR and optical sensors.

This paper presents an improved algorithm for simultaneously retrieving both land surface emissivity (LSE) and land surface temperature (LST) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MSG-2 satellite. First, the temperature-independent spectral index-based method for LSE retrieval is reviewed and improved in terms of three aspects: atmospheric correction, fitting of the bidirectional reflectivity model, and retrieval of the LSE in SEVIRI channel 10. Then, the generalized split-window method with seven unknown coefficients is used to derive the LST. Finally, this improved algorithm is applied to several MSG-2/SEVIRI data sets over a study area with geospatial coverage of latitude 30 ° N-45 ° N and longitude 15 ° W-15 ° E, and using detailed cases, the modifications to the original LSE/LST retrieval methods are shown to be effective and reasonable. In addition, the SEVIRI-derived LSTs are cross-validated primarily using the Moderate Resolution Imaging Spectroradiometer-derived validated LST data extracted from the MOD11B1 product on two clear-sky days (August 22, 2009 and July 3, 2008). The validation results indicate that more than 70% of the differences are within 2.5 K and that the LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

In this paper, a generalized regression neural network (GRNN) is used for land surface temperature (LST) retrieval from advanced microwave scanning radiometer-earth (AMSR-E) passive microwave data. To make neural network method more representative of the real situations, the simulated data under various atmospheric and surface conditions is generated with the aid of monochromatic radiative transfer model and the advances integral equation model, and is used to train GRNN, combined with AMSR-E measurements and MODIS LST product on the same platform (Aqua satellite). Because of the lack of simultaneous ground LST measurements in large scale, MODIS LSTs are taken as actual ground LST measurements. Through detailed analysis, the datasets in AMSR-E channels 23.8 V, 36.5 V, 89.0 V and 89.0 H GHz with the smallest root mean square error (RMSE) are used for LST retrieval, and the results show that more than 70% of errors are within 3 K, and the RMSE is 4.66 K.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.

In this study the parameters identification problem of the fed batch glycerol fermentation process is investigated based on an impulsive dynamical system. Considering the abrupt increase of the glycerol and alkali in fed batch culture of glycerol by conversion to 1,3-propanediol, this paper proposes a nonlinear impulsive system of fed batch culture and its parameters identification problem. The existence and uniqueness of solution for the impulsive system and the continuous dependence of solution on the parameters are proved. Based on the compactness of the solution set of the system, identifiability of the system and the necessary condition of optimality for the system are obtained.