Wind profile information can be used to study high-altitude background wind field or to monitor near-surface strong winds, which plays an important role in revealing the characteristics of large-scale weather systems. Velocity-azimuth display (VAD) is the most widely used method for wind profile retrieval in Doppler radar, in which the linear wind field is assumed and the Doppler velocity information uniformly sampled at equal azimuth intervals is used. However, for severe convective weather systems such as typhoons and tornadoes, the spatial distribution of wind fields is complex and the assumption of linear wind field may be not satisfied. Besides, when the weather system is not evenly distributed around the radar or concentrated in a certain direction, the uniform sampling at equal azimuth intervals cannot be guaranteed, resulting in the decrease of retrieval accuracy. In this paper, a wind profile retrieval method based on non-uniform sampling VAD (nVAD) technique is proposed. By establishing a nonlinear spatial distribution model of complex wind fields and using the radial Doppler velocity information non-uniformly sampled at azimuth, the traditional VAD can be modified and the wind profile can be obtained using the numerical analysis where the radar observations are acquired at a set of different elevation angles. The nVAD technique is not limited by the truncated Taylor expansion and the nonlinear wind field can better represent the real complex wind field structure, and the non-uniform sampling method is more suitable for the case with missing measurement. The verification results show that the nVAD technique can effectively retrieve the wind profile for complex wind field when the Doppler velocity is sampled non-uniformly and has better performance than the traditional VAD technique.

As indispensable ecological assets, grasslands play a crucial role in preserving biodiversity, sequestering carbon, and maintaining ecological balance, thereby underscoring their essential contribution to environmental well-being. In grassland ecosystems, aboveground biomass (AGB) serves as a pivotal indicator for ecosystem management and a fundamental component in the terrestrial carbon cycle. Building on this, the integration of remote sensing technology and cloud computing platforms, such as Google Earth Engine (GEE), represents a significant advancement in the extensive and rapid assessment of grassland AGB. This technological evolution provides innovative methods for enhanced environmental monitoring and more sophisticated data analysis.In this study, multiple inversion models were developed based on the relationship between AGB and vegetation indices. Each model was subjected to hyperparameter tuning through Leave-One-Out Cross-Validation (LOOCV) and adjustments in feature selection to enhance their specificity. Subsequently, a thorough evaluation of each model's performance was conducted, establishing a benchmark for rapid AGB inversion in grassland productivity analysis. This methodology not only improves the precision of the models but also offers critical insights for advancing the field.

The surface reflectance product is the most crucial and fundamental quantitative product in optical remote sensing, serving as the source for various land surface parameter products. This study initially conducts the retrieval of aerosol optical depth (AOD) from GF1/WFV data. The 6SV model, combined with dark object method and histogram matching, is utilized to achieve AOD inversion at a 16-meter resolution on a per-pixel basis. Subsequently, atmospheric correction is performed using the radiative transfer equation to obtain surface reflectance. The culmination of our efforts involved a comprehensive quantification of uncertainties throughout the production process of the proposed surface reflectance product, allowing for a robust assessment of its reliability, accuracy, and applicability. A thorough evaluation was conducted to ascertain the efficacy and potential of the proposed methodology.

Due to global warming and overgrazing, grassland degradation and alkaline problems are becoming more and more serious [1]. Monitoring grassland growth dynamically is helpful to take protective measures in time. Grassland productivity is a quantitative evaluation index for representing grassland growth. CLM5.0 (Community Land Model 5.0) can realize spatial-temporal continuous grassland productivity simulation, the most promising land surface process model[2]. Compared with previous versions, CLM5.0 has improved soil evaporation, vegetation canopy interception, and so on[3], which makes the simulation closer to reality, but also increases the complexity of the model. The parameters must be optimized when the model is applied to the target region.In this study, the parameters of CLM5.0 for the simulation of grassland productivity in Hulunbuir were optimized. Firstly, parameter sensitivity analysis was carried out. Secondly, parameter optimization was carried out for sensitive parameters using DREAM (Differential Evolution Adaptive Metropolis), and grouping experiments were conducted for key phenological parameters. Finally, precision analysis was carried out for optimization results.

With tens of thousands of channels, infrared ultra-spectral data are expected to improve the accuracy of trace gas profiles, especially carbon monoxide (CO), which possesses a weak adsorption intensity and strong interference signals at the adsorption band. The greatly increased number of channels will generate a considerable amount of redundant information. It is necessary to develop a new method to exclude redundant information, thereby enhancing the retrieval accuracy of ultra-spectral data. In this paper, regarding the ill-posed nature induced by these interference and strong correlation of multi-factors, an improved statistical regression retrieval method is proposed. The ratios of the retrieved gas signal to the interfering signals and the noise equivalent temperature differences (NEΔT) are first analyzed as the weighting factors for the selected channels. By applying the weighting factors in the retrieval process, the proposed method amplifies the contribution of the channels whose information content are more efficient. The proposed method is assessed by the application on the retrieval CO profiles from the simulated ultra-spectral data. The result shows that the root mean square errors (RMSE) of the proposed method for CO profiles is smaller than traditional statistical regression method, and the accuracy is improved by 2.85%.

Land Surface Temperature (LST) is one of the important parameters in thermal environment monitoring. Satellite thermal remote sensing is the major way to obtain spatial-temporal information of LST. However, limited by the cloud contamination and the trade-off between spatial and temporal resolution, current temperature products are difficult to provide all-weather LST. In this paper, a method is proposed to obtain all-weather hourly LST. It consists of two main steps: 1) reconstruction of LST under cloudy-sky by using enhanced annual temperature cycle (ATCE) model and 2) establishment of relationship between LST and air temperature which is used for the acquisition of hourly LST. In the end, the performance of the method is analyzed through the artificial data which is created by masking the origin images. And the results show that the proposed method is valuable for generating all-weather hourly LST.

We present a statistical analysis of how residual error from satellite remote sensing reflectance ($R_{\mathrm {rs}}$) depends on the environmental factors. Our results indicate that image coverage affects the data quality, and the residual error in high-quality data correlates significantly with the residual error in data heavily contaminated with stray light. Due to imperfectly corrected bidirectional reflectance, we found many residual error anomalies in the covarying relationship between residual error and illumination–observation geometry even though high-quality$R_{\mathrm {rs}}$data appeared more homogeneous than low-quality data. Additionally, we found underestimates of$R_{\mathrm {rs}}$in very oligotrophic tropical waters, and these residual errors positively covaried with sensor zenith angles at the edge of the scans because of strong atmospheric multi-scattering effects. Furthermore, we found that due to their strong correlation with$R_{\mathrm {rs}}$, the spectral relationship of residual errors should be dynamically reinitialized with the image, which is key to an inherent optical properties (IOPs) data processing system (such as IDAS) removing the residual errors from the$R_{\mathrm {rs}}$data. Doing so, and applying the IDAS algorithm, stray-light-contaminated$R_{\mathrm {rs}}$was recovered, obtaining comparable results to those achieved considering high-quality measurements. This permits to relax the corresponding quality check for generation of the Level-3 global area coverage$R_{\mathrm {rs}}$, largely improving the satellite spatiotemporal coverage and consequently the product accuracy.

Directional brightness temperature (DBT) acquired by remote sensing instruments plays a significant role in characterizing the directional anisotropy of land surface, especially for row canopies. The difference between shaded vegetation and sunlit vegetation is ignored in the existing models. In this article, a four-component parameterized directional thermal radiance model (FCPMod) has been proposed to describe the DBT of the row canopy by considering the four components including the sunlit/shaded soil and sunlit/shaded leaf, the improved multiple scattering within the canopy, and the sensor’s field of view (FOV). First, the sensor’s FOV is divided into many tiny rectangles along the row direction and the probabilities of four components in each tiny rectangle are estimated based on the radiative transfer (RT) theory and the bidirectional gap probability. Second, the DBTs are weighted by the four components’ probabilities and brightness temperatures of tiny rectangles. Third, a modified multiple scattering model is proposed to improve the modeling accuracy by considering the contribution of the multiple scattering radiance between soil and canopy. The sensitivity analysis results show that the proposed method performed well compared to the FRA97 model proposed by Françoiset al.(1997) over continuous canopy and the RT model (FovMod) proposed by Renet al.(2013) over row canopy. Finally, the field validations on a maize row canopy show that the proposed FCPMod performed better than about 0.4 K compared with the FovMod.

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%.

This paper presents a series of temporal vicarious radiometric calibration results of ZY3-MUX sensor over 2016–2019 using the reflectance-based approach. The synchronous ground measuring data have been collected from Baotou sandy site in China given that its high-frequency standard product including surface reflectance and atmospheric parameters. The results show the average difference of radiometric calibration coefficients between calculated results and the official coefficients in each year of ZY3-MUX sensor are 4.6%, 4.6%, 0.64%, 6.28%, respectively. The long-term stability of the radiometric calibration using the data of four years shows a good consistent, and the average difference is 0.92%, 0.64%, 0.50% and 0.65% for ZY3-MUX sensor, respectively. In addition, uncertainty analysis shows that the overall uncertainty for ZY3-MUX radiometric calibration is 4.42%, 4.44%, 4.66% and 3.92%, which also confirms the credibility for radiation quality of Baotou sandy site.

This paper describes an SNO (Simultaneous Nadir Overpass) method based on observation elements matching for radiometric cross-calibration of China HJ-1B and MODIS thermal infrared channels. Firstly, a DTC (Diurnal Temperature Cycle) model with four parameters and ECMWF data are introduced for time matching. Then a BRDF model updated to the TIR domain is built for angle matching. Combining matching coefficients with the spectral matching factor, the TOA radiance of MODIS B31 can be converted into TOA radiance of HJ-1B. Finally, the radiometric calibration results using the images of Qinghai Lake show that the proposed method is effective. Compared with the strict SNO method, the accuracy is improved by 0.73K.

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.

The Radiometric Calibration Network (RadCalNet) provide SI-traceable Top-of-Atmosphere (TOA) spectrally-resolved reflectance for automated radiometric calibration of optical satellite sensors. Each RadCalNet site is equipped with automated ground instrumentation in order to provide continuous measurements of both surface reflectance and local environmental/atmospheric conditions needed for the derivation of TOA reflectance values. It is very valuable to use these data in calibrating and motoring optical satellite sensors with the recent launches of very large number of satellites. The work presented here shows the results of vicarious calibration of SuperView-1 satellite sensor using RadCalNet TOA reflectance product. The results also indicated that there was a good consistency among the vicarious calibration coefficients from different RadCalNet sites.

In this study, the ozone column retrieval algorithm is described using the ultraviolet solar radiation from space. The algorithm is based on differential optical absorption spectroscopy (DOAS) technique. Firstly, the differential slant column densities (SCD) of trace gases are retrieved. Secondly, SCDs are subsequently converted to vertical column densities (VCD) by radiative transfer model. Then, the ozone column is retrieved by this algorithm and the sample results show a good correlation with R2of 0.91 and RMSE of 0.89 mg/m2between retrieved and true values. It also can be shown the potential of the algorithm on further atmospheric molecule retrieval in hyperspectral quantitative remote sensing.

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.

This paper describes a four-component parameterized directional thermal radiance model for row crops, which consists of the thermal radiance of the sunlit/shaded soil and sunlit/shaded leaf, multiple scattering effects of canopy and sensor field of view. The light transmission process of the row crops canopy has been full depicted and the accuracy and sensitivity of the proposed model are discussed in detail. Finally, compared with the FRA97 and FovMod models, the results show that the root mean square error(RMSE) is 0.18K and 0.36K, respectively.

When using field calibration site to perform on-orbit radiometric calibration for a space-borne remote sensor, the observed signal of the sensor may contain energy from adjacent pixels, due to existence of the earth atmosphere and sensor viewing characteristics. Hence the accuracy of radiometric calibration will be decreased to some extent. When calibration of high-resolution sensor is concerned, the non-uniformity of the ground target will be relatively more obvious. In addition, if the brightness contrast of neighboring targets is not small, the impact of adjacency effect will be more outstanding. How to quantitatively analyze and eliminate the influence of this kind of adjacency effect, becomes an actual demand to reduce the uncertainty of on-orbit radiometric calibration. Aiming at the adjacent effect caused by atmospheric multiple scattering, this paper analyzed radiation transfer mechanism first, then constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data, developed an adjacency effect correction method used in on-orbit vicarious calibration, which considers background reflectance spectral information. Test on Sentinel-2A imagery indicates that the proposed correction method can effectively alleviate the influence of adjacency effect in vicarious calibration.

Radiometric characteristics monitoring of optical remote sensing data is a very essential step that enables further quantitative study and application of the data. In this paper, long term vicarious radiometric calibration using ground reflected radiance-based approach was introduced in this study. A case study of Sentinel 2A multispectral imager (MSI) vicarious radiometric characteristics monitoring based on our proposed approach with an automatic observation system was conducted over a large scale desert at the Baotou calibration site in Inner Mongolia, China. And, twelve clear Sentinel 2A MSI scenes as well as ground measurements were successfully acquired during the year of 2017. Top of Atmospheric (TOA) radiance and reflectance were predicted with introduced approach from ground reflected radiance and atmospheric data. The long term radiometric calibration results suggests that the Sentinel 2A MSI display a stable radiometric performance over the calibration period. Vicarious and onboard radiometric calibration results were also cross compared with average relative error about 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 3.5% due to the atmospheric characteristics, surface characteristics, and the calibration model uncertainties sources.

In this study, the automatic reflectance-based method is used to vicarious radiometrically calibrate the satellite optical sensors using the desert target located in the Baotou site in China. The ground reflected radiance of the desert target were collected automatically using an automatic observation system. The reflectance of the desert target was calculated with the radiance collected with the automatic observation system and the total irradiance simulated from MODTRAN code based on the atmospheric parameters. Then, the TOA radiance can be predicted with MODTRAN code based on the calculated desert reflectance. The automatic reflectance-based approach was applied to the Landsat 8/OLI sensors, and the TOA radiances calibrated by our method were also compared with the observed TOA radiance calibrated with on-board calibrator. Preliminary results show a good consistent and the mean relative difference of the multispectral channels is less than 5%. Uncertainty analysis also show that the TOA radiance overall uncertainty is less than 4% due to the source including the atmospheric characteristics, surface characteristics, and the selected calibration model.

This paper addressed the retrieval of land surface temperature (LST) from combined mid-infrared and thermal infrared data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP). To efficiently remove the effect of the direct solar radiance, a relationship between direct solar radiance and water vapor content, view zenith angle and solar zenith angle is proposed to improve the retrieve accuracy. Then, a split-window algorithm from combined mid-infrared and thermal infrared data is used to correct for the atmospheric effects and retrieve the LST with the aid of emissivity provide by VIIRS product. Finally, comparison of the standard VIIRS LST product and the retrieved LST from the proposed algorithm, a good agreement was shown. Analysis indicated the root mean square error (RMSE) of the LST over these land cover types is 2.04K for desert and 1.84K for vegetation, respectively.

The characterization and understanding of microwave remote sensing image quality is essential to the monitoring performance of sensors and the proper usage of the acquired image. Point targets (e.g., passive corner reflectors and active transponders) have been widely used for microwave image resolution analysis. However, the analysis results based on point targets do not include the effects of speckle and thermal noise that are rare for point targets but common for distributed targets. Since distributed targets are common in remote sensing imaging scenes and their distinguishability is of interest in practice, the concept of an optical bar pattern target was extended to the microwave band, and a microwave bar pattern target was designed and permanently built at the National Calibration and Validation Site for High-Resolution Remote Sensors. In this letter, the main design idea is first introduced. Different backscatter coefficients of the bars were achieved for different surface roughness made by black rough gravel and a white smooth concrete plate, where the gravel size was designed per the Rayleigh roughness criterion. The experimental results using C-band airborne SAR and X-band KOMPSAT-5 SAR images are presented. A quantitative analysis shows that this target could roughly evaluate the image resolution of high-resolution microwave imaging sensor and would be a good complement to point targets.

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact, this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to snail breeding and reproduction, then can also provide the efficient information to determine the location, area, and spread tendency of snail. But the complex relationship between snail and various environmental factors limit its development. To solve above problem, in this study, the fuzzy information theory was employed to analyze the relationship between snail density and environmental factors. A model for predicting snail distribution and density was developed and validated with field data of Dongting Lake. The validation results demonstrated the success of the developed model in predicting the distribution of Oncomelania hupensis.

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.

Trihedral corner reflectors have been widely used as standard point targets for synthetic aperture radar (SAR) calibration. The RCS accuracy of the trihedral corner reflector is vital especially for future high-precision SAR radiometric calibration. In order to reduce the interaction between corner reflector and the ground on which the reflector deployed, and also reduce the edge diffraction through minimizing the panel external edge length, improved trihedral corner reflector was developed and it had smaller edge length than the trihedral corner reflector with the polygonous shape for a given panel area. General expression for the panel area and panel external edge length of arbitrarily self-illuminating corner reflectors was presented by parameter equation firstly. Then the edge of reflector panel was assumed as circular arc and through a numerical approximation approach, improved corner reflector of circular arc panel geometry with smaller edge length was obtained.

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.

As a novel imaging theoretical, compressive sensing (CS) hyperspectral imaging utilizes the sparse property of the earth objects to efficiently obtain the hyperspectral cube with much less data volume. The construction of sparse basis is of great importance for CS hyperspectral imaging. In this paper, a spectral sparse basis construction method based on earth object's spectral library and redundant dictionary training is proposed. Compared with traditional DCT and wavelet basis, the sparse basis constructed by our method performs much better in simulation experiments.

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 paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, including iterative spectrally smooth temperature emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI) and linear spectral emissivity constraint method (LSEC) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.

Retrieving forest parameters, such as forest height, biomass from polarimetric and interferometric SAR (Pol-InSAR) images has been investigated and well demonstrated via airborne experiments for different types of forest. Terrain slope is a factor that always prevents the wide application of SAR technique due to its slant-looking imaging geometry. It induces changes of backscattering intensity, polarimetric response, etc. Regarding Pol-InSAR forest height retrieval, the effects of terrain slope on retrieval accuracy have been analyzed. The relation between terrain slope and forest height retrieval bias was established through theoretical analysis and simulation procedures based on the PolSARpro software. Based on these results, a simple topographic correction method for forest height retrieval from Pol-InSAR images was presented. This method could alleviate forest retrieval error in rugged areas easily.

In this paper, a methodology for maize LAI estimating is proposed by assimilating remotely sensed data into crop model based on temporal and spatial knowledge. Firstly, the spatial knowledge is extracted from MOD09A1 based on its multi-scale feature, and then the spatial knowledge is used for correcting the bias of inversion results. Secondly, the phenology information is extracted from MOD13A1 and used as prior temporal knowledge for building a cost function, and then, based on the cost function the sensitive parameters of WOFOST (WOrld FOod STudies) are calibrated. At last, the calibrated WOFOST model used as forecast operator and remote sensing inversion results used as observation operator, the Kalman Filter (KF) algorithm is used to realize the assimilation of MODIS data into crop model. The experiment results indicate that the methodology proposed in this paper is reasonable and accurate for estimating maize LAI.

In order to meet the various demands of remote sensing applications, it is still worth being discussed that whether higher resolution is necessarily better. Actually, the spatial resolution should be carefully chosen according to application demands, for optimizing the balance of effectiveness and cost. In this paper the concept of “appropriate spatial resolution” is used, which should be the spatial scale with least data volume and most interested information, while not the highest precision of information. Characterization of spatial heterogeneity is the basis of analyzing spatial structure of the image under the given spatial resolution, and it is also the basis of selection of appropriate spatial resolution. Based on the wavelet variance method to characterize spatial heterogeneity, appropriate spatial resolutions of albedo, NDVI and surface radiation temperature on the same research area are given. Results show that appropriate spatial resolutions to various retrieved physical properties are different due to different grades of spatial heterogeneity. Proper scale range will be obtained to observe certain geographic quantity, by operation of the appropriate resolution selection.

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.

Microwave staring correlation (MSC) imaging is a new type of active high-resolution microwave imaging technique. Image quality assessment is of vital for developing and monitoring any remote imaging system. This paper presented the image quality analysis for this imaging technique by theoretical analysis and simulation. MSC imaging method was introduced firstly. Then, image properties were analyzed and compared with SAR image. Speckle noise effect and side lobe of point target effect, which are intrinsic properties of SAR image, do not exist in MSC image. The quality metrics of MSC image were presented. IRW-Staring time ratio was proposed to describe the image property that image resolution improves with the number of received signals used in the image reconstruction procedure. Finally, the effects of different system parameters on the image quality were investigated via simulation experiments. The analysis results could be helpful for future imaging algorithm development and system design.

Atmospheric correction plays an important role in the retrieval of land surface temperatures and emissivities from remotely sensed thermal infrared images. When imaging technology upgrades from multispectral to hyperspectral, an opportunity appears that atmospheric compensation can be resolved only according to hyperspectral thermal infrared data itself. A set of methods is now proposed to carry out atmospheric correction for the purpose of land surface temperature/emissivity separation: A segmental linear model is proposed to retrieve water vapor line absorption transmittance, a ¿H2O-CO2two channel groups¿ method is designed to retrieve water vapor continuum absorption transmittance, and a procedure to extract atmospheric upwelling radiance is presented. Tests with the simulated hyperspectral thermal infrared (TIR) data demonstrate that these techniques can provide good results for atmospheric compensation.

This paper addresses the intercalibration of the infrared channels 1 (10.9 mum), 2 (11.9 mum), and 4 (3.8 mum) of the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard China's geostationary satellite FengYun 2C (FY-2C) against the channels of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra and of the Atmospheric InfraRed Sounder (AIRS) onboard Aqua. The effects of the wide spectral range of SVISSR channels 1, 2, and 4 were evaluated. Three methods, including the ray-matching method, the radiative transfer modeling method, and the high spectral convolution method, were developed in a tropical region using the SVISSR/FY-2C, MODIS/Terra, and AIRS/Aqua measurements in December 2006 and 2007. The results reveal that the onboard calibration of SVISSR/FY-2C was stable during that period; however, calibration discrepancies exist between MODIS and SVISSR channels and between AIRS and SVISSR channels. The intercalibration coefficients of SVISSR/FY-2C channels were obtained for each of the three methods.