Clouds have a significant influence on radiation propagation in atmospheres. In atmospheric remote sensing, band radiative transfer (BRT) models in cloudy atmospheres are crucial and are widely used in climate change, environmental monitoring, weather forecasting, and other research fields. Although the line-by-line (LBL) model is widely acknowledged as the most accurate BRT calculation method, its widespread usage is constrained by its high cost. Recently, correlated k-distribution (CKD) methods have progressed significantly and have emerged as the most promising alternatives in BRT calculations. They provide a better balance of accuracy and efficiency compared to other methods. However, most CKD methods tend to optimize quadrature parameters based on spectral distributions of absorption coefficients in clear atmospheres and use band-averaged cloud optical properties (COPs), ignoring the influence of COP changing with wavenumber. When COPs vary greatly in the wavenumber space, such treatment will cause significant errors. This paper proposes a CKD method suitable for BRT calculation in cloudy atmospheres that takes into account the effect of spectral parameters other than absorption coefficient on radiation.

Given the high cost of band radiative transfer calculations for cloudy atmospheres in remote sensing applications, a maximum correlated k-distribution optimal algorithm for single-scattering parameters (SSP-MCKD) is proposed. First, the CKD theory is extended to multiple spectral parameters for cloudy atmospheres, such as single albedos, asymmetry factors, etc., after analyzing the correlation of their spectral distributions under different environmental conditions. Further, based on the influence of spectral parameters on the single-scattering source function, the maximum correlated parameter of each spectral line is confirmed. A maximum correlation parameter group is formed by spectral lines with the same maximum correlated parameter. Based on the proportion of spectral lines within groups, the quadrature intervals are divided into each maximum correlation parameter group. Further, spectral parameters within the group are rearranged in the order of the maximum correlated parameter. The average equivalent parameters and quadrature weights are then calculated between and within groups. Finally, experiments were conducted to verify the applicability of the proposed method under different conditions.

Fig. 7 shows the mean relative errors of radiation calculated using the Δlog k method, correlated k-distribution with parameterization of cloud optical properties (PCOP-CKD), and the SSP-MCKD method under different number of quadrature intervals. With increasing quadrature intervals, the results of the three methods gradually converge to those calculated using the LBL model. The Δlog k method mainly considers the influence of the extinction coefficient, and the convergence curves are relatively stable. However, when the extinction coefficient is not the maximum correlated parameter in a given band, poor results are obtained, as shown in Fig. 7(c). The PCOP-CKD method ranks COPs based on the atmospheric absorption coefficients, where the correlation in the spectral distributions between the atmospheric absorption coefficients and COPs is maintained. This method ignores the influence of cloud scattering on BRT calculations. When the number of quadrature intervals is small, considerably desirable results are obtained. However, when the number of quadrature intervals increases, its calculation accuracy improves slowly. Hence, this method cannot meet the demand for practical engineering. The method proposed herein comprehensively considers the influence of spectral parameters on BRT calculations. As the number of quadrature intervals increases, its calculation accuracy has the fastest convergence speed.

Table 6 lists the mean relative errors of radiation calculated using the Δlog k, PCOP-CKD, and SSP-MCKD methods in clouds with different heights. The PCOP-CKD method considers cloud atmosphere as two parts: cloud and atmosphere. This method is more accurate in the atmosphere part. This method in scene 1 is more accurate because of the lower cloud and stronger gas absorption. The average error obtained using the SSP-MCKD method is 1.68%, which is improved by 26.92 percentage points and 5.63 percentage points compared with that of the Δlog k and PCOP-CKD methods, respectively. Thus, the proposed method is suitable for BRT calculations in clouds at different heights.

Table 8 lists the mean relative errors of radiation calculated using the three methods for different cloud types. The average error calculated using the SSP-MCKD method is 5.54%, which is improved by 17.73 percentage points and 3.31 percentage points compared with that of the Δlog k and PCOP-CKD methods, respectively.

The proposed SSP-MCKD method can be effectively employed in BRT calculations for cloudy atmospheres in remote sensing applications. This method has faster convergence in absorption, semi-absorption, and transmission bands compared with the other two methods. When the cloud average extinction coefficient and standard deviation are lower than 45 and 15 km^-1, respectively, the proposed method shows good results. Even when the cloud extinction coefficients or its standard deviation increase significantly, this method still outperforms the other two methods. The idea of optimizing and grouping according to maximum correlation parameters can be used as a reference to solve BRT calculation problems in other mixtures containing both gases and particles.

Space vehicles, deep space explorers, long-endurance aircraft, and other equipment have increasingly high requirements on navigation accuracy. In this aspect, a major issue to solve is how to improve the space navigation accuracy of aircraft, achieve fully autonomous interference-free navigation, and reduce the cost of expensive equipment. Starlight atmospheric refraction navigation technology neither involves the transmission and exchange of information with the outside world nor depends on the navigation and positioning by ground equipment. It is thus characterized by remarkable concealment and strong resistance to the external environment. However, due to the complex atmospheric environment in the near-Earth space, the bending of starlight toward the center of the Earth after entering the atmosphere will affect the accuracy of starlight atmospheric refraction navigation. For this reason, building an accurate starlight atmospheric refraction model is crucial for improving navigation accuracy. In related studies of starlight atmospheric refraction models, analysis is mostly based on the data from the United States Standard Atmosphere (USSA) parameter model, the COSPAR International Reference Atmosphere (CIRA) model, and the Neutral Atmosphere Empirical Model-2000 (NRLMSISE-00). However, the data from these universal models have a low resolution. Therefore, a corrected starlight atmospheric refraction model is constructed using the data from the National Centers for Environmental Prediction (NCEP) in this paper. NCEP data are recorded four times a day to give due consideration to the effect of the day and night temperature difference, and the resolution is 1°×1° in latitude and longitude. The model built on this basis will be more accurate than the traditional models with CIRA as the typical representative.

The accuracy of the commonly used USSA and CIRA atmospheric reference models is low. To solve this problem, this study builds a spatiotemporally varying atmospheric parameter model using the high-resolution NCEP atmospheric parameter data and employing the Fourier interpolation algorithm. The atmospheric refractive indices at different altitudes, latitudes, and longitudes are used to calculate the propagation path of starlight in the atmosphere, and a corrected starlight atmospheric refraction model is constructed.

The comparison among the corrected starlight atmospheric refraction model and the existing models shows that the spatiotemporally varying atmospheric temperature model developed in this study has a relative error smaller than 2% and an average absolute error of 1.86 K when fitting measured data (Fig. 1) and a relative error below 4.39% when fitting the atmospheric density (Fig. 4). Moreover, the relative error between the refractive spatiotemporal model and the traditional single-point model at low, middle, and high latitudes in January is 37.64%, 9.79%, and 28.78%, respectively [Fig. 9(a)]. The relative error between the two models at low, middle, and high latitudes in July are 27.95%, 26.89%, and 39.10%, respectively [Fig. 9(b)]. Therefore, the proposed starlight atmospheric refraction model considering spatiotemporal variations has higher theoretical accuracy.

In this study, high-resolution data from the NCEP are selected for the reanalysis of the atmospheric parameter data. The reanalysis data are further used to build a spatiotemporally varying atmospheric parameter model. Model simulation results are presented, with due consideration given to the effects of temporal, horizontal, and vertical atmospheric parameters on starlight atmospheric refraction. The propagation path of starlight in the atmosphere is calculated, and the corrected starlight atmospheric refraction model is constructed on the basis of the spatiotemporally varying atmospheric parameter model. The changes in the refraction angle with height at different time, longitudes, and latitudes are calculated, and the deviations of the refraction angle with height at different time, longitudes, and latitudes are obtained through analysis. The results show that the relative errors between the refractive spatiotemporal model and the traditional single-point model at low, middle, and high latitudes in January are 37.64%, 9.79%, and 28.78%, respectively. The relative errors between the two models at low, middle, and high latitudes in July are 27.95%, 26.89%, and 39.10%, respectively. Finally, the apparent height is obtained by inverting the refraction angle, and its relative deviations from the traditional apparent height at low, middle, and high latitudes are 6.27%, 5.10%, and 5.42%, respectively. Because the corrected model takes into account the spatial and temporal variations in the atmosphere, the simulation results are closer to the changes in the real atmosphere.

Atmospheric turbulence causes laser intensity fluctuation, beam drift, and beam spreading, which necessitates the determination of its intensity. Refractive index structure constant (Cn2) profile and atmospheric coherence length (r0) are usually used to describe the atmospheric turbulence in the whole layer. The Cn2 profile in the whole layer is difficult to measure in real time economically in some cases, and researchers estimate atmospheric turbulence in different ways. Cn2 profile can be estimated using conventional meteorological parameters or artificial neural networks. Nevertheless, such methods either perform poorly in real time or require a considerable amount of measured data. An atmospheric coherence length monitor is usually employed to measure atmospheric coherence length and the isoplanatic angle, which can be further used for the real-time inversion of Cn2 profile. However, this instrument is easily affected by bad weather because it needs to track stars continuously. The study proposes a method to estimate the whole-layer atmospheric optical turbulence with multi-source measurement data from the microwave radiometer, wind profiler radar, meteorological sensor, micro-thermometer, and radiosonde. Being real-time and weather-proof, the proposed method is effective in engineering applications.

Specifically, a real-time atmospheric parameter profile is constructed with multi-source measurement data from the microwave radiometer, wind profiler radar, meteorological sensor, and radiosonde. Real-time ground-based data and radiosonde data are spliced together in accordance with the coefficients of correction at different heights. Then, this study distinguishes the atmospheric stratification state and calculates boundary layer height according to the distribution characteristics of the potential temperature gradient with the data from the microwave radiometer. After that, the Cn2 profile in the boundary layer is estimated by applying the exponential decline model, using real-time data from the micro-thermometer. The exponential decline index is -3/4 during the daytime and -2/3 at night. The Cn2 profile in the free atmosphere is estimated by employing the Dewan outer-scale model, using the previously constructed real-time atmospheric parameter profile. Furthermore, the Cn2 profiles in the two layers are spliced together to estimate r0 according to the integral relationship between the two layers. Finally, the estimated r0 is compared with the value measured by the atmospheric coherence length monitor.

The calculated boundary layer height varies from hundreds of meters at night to more than three thousand meters in the afternoon, and it is based on the previously constructed real-time atmospheric parameter profile data from August 3 to August 5 (Fig. 2). The estimated Cn2 profiles in the boundary layer and free atmosphere are spliced together, and the results show that Cn2 decreases with fluctuations, as altitude increases from ground level to 25 km. The order of magnitude of the estimated Cn2 decreases from 10^-15 to 10^-19 at night and in the morning and from 10^-14 to 10^-19 during the daytime (Fig. 3). The estimated r0 has the same order of magnitude and daily variation trend as those of the measured values. The consistency between them is fair in unstable atmospheric stratification but poor in the case of stable and near-neutral atmospheric stratifications (Fig. 4). The deviation is maximum in near-neutral atmospheric stratification, when the atmospheric turbulence near the ground is weak (Fig. 5). The root-mean-square error (RMSE) between the estimated r0 and the measured r0 is 2.988 in unstable atmosphere stratification, 6.858 in near-neutral atmospheric stratification, and 5.088 in stable atmosphere stratification. The correlation in unstable atmospheric stratification is much better than that in stable or near-neutral atmospheric stratifications (Table 2). In addition, the estimated r0 in the two component layers is compared with that in the whole layer obtained by applying the Dewan model. The RMSE between the estimated r0 and the measured r0 shows that the estimated r0 in the whole layer is slightly more consistent than that in the two component layers. Nevertheless, the standard deviation shows that the estimated r0 in the two component layers is much less fluctuant than that in the whole layer (Fig. 7). The deviation of the estimated r0 from the measured r0 is caused by several reasons. First, the atmospheric turbulence model has a technical route different from that of instrument measurement. Second, the applicability of the atmospheric turbulence model is doubtful in the sense that the similarity theory of turbulence is probably false in stable or near-neutral atmospheric stratification. Last but not least, data fusion and processing may also cause estimation errors.

Multi-source atmospheric measurement data are used to estimate Cn2 profile and r0 in real time. The results show that the estimated r0 has the same order of magnitude and daily variation trend as those of the measured r0. Moreover, the RMSE is minimum in unstable atmospheric stratification and maximum in stable atmospheric stratification, and the correlation in unstable atmospheric stratification is better than that in stable or near-neutral atmospheric stratification. Analysis proves that the whole-layer atmospheric optical turbulence can be estimated in real time by estimating the Cn2 profile in the two component layers with multi-source measurement data. The proposed method provides better real-time performance in estimating the whole-layer atmospheric optical turbulence and can validate instrument measurement in some cases. Therefore, it has great engineering application significance. Since the key to this method is to estimate the Cn2 profile in the boundary layer accurately, modifying the atmospheric turbulence model for the boundary layer is important for improving estimation accuracy.

Optical OFDM index modulation (O-OFDM-IM) is a new multicarrier modulation technique that can achieve remarkable improvements in transmission rate and bit error rate (BER) performance by carrying additional information through the index of subcarriers. Currently, in the field of optical communication, O-OFDM-IM has triggered a research boom for potential improvements in system error performance and spectrum efficiency. However, existing O-OFDM-IM schemes require complex channel estimation at the receiver to obtain channel state information, which not only increases the complexity of the receiver but also brings a large spectrum resource overhead. This study proposes a differential index shift keying DC-bias optical OFDM (DISK-DCO-OFDM) scheme that avoids complex channel estimation while ensuring BER performance. Additionally, a multiclassification detector based on radial basis function (RBF) neural network is suggested to address the high complexity of the receiver.

By considering a single subcarrier block as an example at the transmitter side, an initial transmission matrix that does not carry information is first prepared at the transmitter before the differential operation is performed. Then, the input binary bits are mapped into a time-frequency dispersion matrix that satisfies the difference operation, i.e., the matrix has only one non-zero element in each row and column. For difference operation, the time–frequency dispersion matrix of the current moment is multiplied with the transmission matrix of the previous moment to obtain the true signal matrix of the current moment. Next, the real signal matrix is transmitted by the laser after the Hermitian symmetry and inverse Fourier transform. On the receiver side, the received signal matrix of the previous moment was first inverted and then multiplied with the received signal matrix of the current moment, and the characteristic matrix of the received signal can be obtained. Then, the real and imaginary parts of the feature matrix were used to construct a one-dimensional feature vector, which was used as the input of the RBF neural network. Finally, the trained neural network was used as a multiclassification detector to complete the decoding work at the receiver side. The proposed scheme completely avoids complex channel estimation.

The DISK-DCO-OFDM system was established in this study and the BER performance of the system was simulated under different turbulence intensity and received aperture conditions. First, we derived an upper bound of the average bit error rate (ABER) of the system and compared the simulated BER with the ABER (Fig. 2). The two curves asymptotically coincided at high signal-to-noise ratios, which demonstrated the accuracy of the derived ABER. Then, we compared the BER performance of the proposed scheme with that of the conventional subcarrier index shift keying DCO-OFDM (SISK-DCO-OFDM) system, and the corresponding results are shown in Fig. 3. The BER performance of the proposed scheme is substantially better than that of the SISK-DCO-OFDM system when the subcarrier block length is 2 under weak turbulence condition. When the subcarrier block length is 4, the BER curves of the proposed scheme and the SISK-DCO-OFDM system coincided at high SNR. Therefore, the proposed scheme guarantees the BER performance while effectively avoiding the channel estimation. The computational complexity reduction rate and BER performance of the proposed multiclassification detector for the receiver side compared with the differential maximum likelihood (DML) detection algorithm are shown in Fig. 6 and Fig. 7, respectively. The computational complexity of the proposed detector is reduced by 16.67% and 70% for subcarrier block lengths of 2 and 4, respectively, compared with the DML. The difference in the BER performance between the two detection algorithms does not exceed 2 dB under weak turbulence.

This study proposes a DISK-DCO-OFDM scheme. The main feature of this scheme is the use of a time-frequency dispersion matrix that satisfies the differential process. Simulation results show that the proposed scheme not only effectively avoids the channel estimation process but also guarantees better BER performance than all current optical OFDM index modulation systems in a weak turbulence environment. Meanwhile, the proposed multiclassification detector can considerably reduce the decoding complexity at the receiver side, and the difference in BER performance compared with DML does not exceed 2 dB. In particular, the method of constructing the received signal feature vector provides an effective reference for future decoding using machine learning or deep learning methods at the receiver side for differential-type systems. Therefore, the proposed scheme can provide a reference for the application of optical OFDM index modulation in complex channel environments, and the proposed multiclassification detector can contribute to future research on reducing the decoding complexity at the receiver side.

In recent years, quantum communication has become a research hotspot in China and abroad for its better data transmission security. As the core of the quantum information theory, quantum communication can be more secure and reliable for information transmission and is an important direction of inquiry. With the development of underwater wireless communication, it is important to research underwater quantum communication for marine and military fields. Bubbles are ubiquitous in the ocean, and the scattering and refraction effects of bubbles on light can cause certain losses in optical quantum transmission, which exerts a certain impact on the performance of underwater optical quantum communication. However, the research on the effect of bubbles on the channel performance of underwater quantum communication has not been conducted. By building the particle size distribution and scattering coefficient models of marine bubbles, the paper analyzes the effects of different condition parameters on link attenuation, entanglement, channel capacity, and channel bit error rate (BER) in the marine bubble environment to investigate the influence of these bubbles on the channel performance of underwater quantum communication. This is of great significance to improve the efficiency of underwater quantum communication.

Marine bubbles are mainly generated by wind-driven wave breaking. With an aim to study the influence of marine bubbles on the channel performance of underwater quantum communication, the particle size distribution function of the bubbles is firstly derived and established. The scattering characteristics and extinction coefficient of the bubbles are studied according to the particle size distribution model of marine bubbles. Additionally, according to the extinction characteristics, the relationship between marine bubble parameters and link attenuation is firstly established, and then the effects of different depths and bubble radii on channel entanglement are analyzed. Then, the effects of different bubble concentrations and transmission distances on three channel capacities of amplitude damped channel, depolarized channel, and bit-flip channel are analyzed respectively. Finally, the effects of different bubble concentrations and transmission distances on channel BER are studied and analyzed. At the same time, the effects of different bubble concentrations and transmission distances on the channel BER are studied and simulated. The theoretical analysis and simulation results can provide a reference for the design of underwater quantum communication in the marine bubble environment.

The simulation results show that the density of bubbles decreases with the increasing depth from the sea level, whereas increases with the rising wind speed. The scattering coefficient of bubbles has the same trend as the bubble density number under the same parameter conditions. Under short transmission distance and small bubble concentration, the link attenuation caused by marine bubbles is also small. With the increase in the transmission distance of optical quantum signals and the bubble concentration, the link attenuation grows rapidly. The channel entanglement increases with the rising depth from the sea level and the decreasing bubble radius. For the amplitude damping channel, depolarization channel, and bit-flip channel, the channel capacity decreases to different degrees with the increasing transmission distance and bubble concentration. The capacity of the depolarization channel and the bit-flip channel is more affected by the transmission distance, and the transmission distance exerts less effect on the channel capacity. The BER in the marine bubble environment is also affected by the transmission distance and bubble concentration. When the bubble concentration is small with a short transmission distance, the system BER changes slowly. When the bubble concentration is large with a long transmission distance, the optical quantum signal attenuates seriously and the BER value rises rapidly.

To investigate the effect of marine bubbles on the channel performance of underwater quantum communication, this paper studies the scattering characteristics of the bubbles according to the particle size distribution model of marine bubbles. In addition, the effects of different condition parameters on link attenuation, entanglement, channel capacity, and channel BER are analyzed according to the extinction coefficient of bubbles, and simulation experiments are conducted. The results show that the increase in bubble concentration and transmission distance increases the link attenuation and BER, and the channel capacity decreases for amplitude damping channel, depolarization channel, and bit-flip channel. The channel entanglement decreases with the increasing bubble radius and decreasing depth, and the impact of marine bubbles on communication performance cannot be ignored. Meanwhile, parameters related to underwater quantum communication should be adjusted appropriately according to the concentration of marine bubbles to reduce the impact of the marine bubble environment on the communication system and improve the reliability of the communication system in practical application.

Scattering and absorption of light by suspended particles in scattering media can lead to significant degradation of image quality. In the imaging environment of turbid water, the interference of backscattered light leads to image degradation, and the light is partially polarized. In order to obtain clear images, it is necessary to suppress backscattered light, which is also the core of underwater polarization imaging technology. Most of the existing underwater polarization imaging methods distinguish scattered light from signal light in the spatial domain based on their polarization information. However, overlapping in the spatial domain makes it difficult to fully suppress the backscattered light only by using the polarization characteristic difference between the target signal light and the backscattered light. In fact, the two kinds of lights are distributed differently in the spectrum of a linearly polarized image. The backscattered light, which is the main cause of image degradation, is relatively concentrated in the low-frequency components of the spectrum, while the information of the target signal light is mainly distributed in the high-frequency components. In this paper, an underwater polarization imaging method based on polarization image spectrum processing is proposed, which effectively suppresses backscattered light by the frequency domain filtering and polarization correlation processing of orthogonally polarized images and improves the contrast and clarity of underwater images. The method proposed in this paper and the results are of great significance to the research on polarization image enhancement and underwater clear imaging.

The proposed method mainly utilizes the spectrum distribution differences between the target signal light and backscattered light. With the help of frequency domain filtering and polarimetric recovery, this method separates and suppresses scattered light in steps and finally achieves high-quality image recovery. First, the cross-linear image in orthogonal polarization images is processed by a high-pass filter, which contains more target information, so as to automatically search for the optimal cut-off frequency of the filter. By extracting high-frequency components, the target signal light and backscattered light are preliminarily separated. With the high-frequency components of the cross-linear image, polarization-related processing is performed on the co-linear image. In fact, there is a polarization relation between the two orthogonal polarization images, which can be represented by the degree of polarization (DOP). In order to maintain this intrinsic polarization relation, it is necessary to keep DOP constant and process the co-linear image based on the high-frequency component of the cross-linear image filtered by the optimal filter. Then the processed orthogonal polarization images are employed to estimate parameters including the DOP of backscattered light, the value of backscattered light from infinity in turbid media, and the transmission based on the improved traditional polarimetric recovery method, and then the recovered image of the target objects in underwater can be finally obtained.

The results of polarization imaging experiments of different objects in water with different turbidity show that the proposed method can efficiently restore the images of target objects and improve the underwater imaging quality. In experiments of the panda-pattern target, search results of the optimal cut-off frequency of each filter are visualized in Fig. 4. By analyzing the intensity histogram at the dash of the result images (Fig. 5), it is found that the proposed method is superior to the Schechner's method. Several methods are used to recover a series of images of another target, and the result shows that the proposed method displays better performance in restoring object details and improving the overall visual effect (Fig. 6). Meanwhile, according to the value of enhancement measure evaluation function(Fig. 7), the proposed method has achieved the highest value representing the most significant improvement in image quality. Further experiments are carried out to verify the effectiveness of the proposed method for different objects in highly turbid water. Compared with other methods, the proposed method can better suppress scattered light. Therefore, the result images by the proposed method are more evenly illuminated, and the details are clearer (Fig. 8).

In this paper, in order to obtain clear images, a turbid underwater polarization image enhancement method is proposed based on frequency domain processing and polarization preservation, and the difference between the scattered light and the signal light in the frequency domain is utilized. By applying a high-pass filter on the orthogonal polarization image pair in the frequency domain, the backscattered light concentrated in the low-frequency component is initially separated and removed. Here, the optimal parameters of each filter in different concentrations and scenes are automatically searched to obtain more accurate target object information. Then the polarization degree is maintained to correlate more accurate orthogonal polarization image pairs. Finally, on the basis of the traditional physical model, the images of objects have been successfully recovered. This paper has carried out a series of experiments on different objects in underwater environments with different turbidity, and the results show that the proposed method based on frequency domain processing can effectively suppress the impact of scattered light on polarization imaging and highlight the signal light of objects. Compared with the traditional underwater polarization imaging methods, the proposed method can improve the uneven illumination problem in turbid water and significantly enhance the contrast and clarity of images, especially for highly turbid water.

In the context of intelligent surveying and mapping, the quality of remote sensing imagery directly determines the intelligent interpretation level, the accuracy of image products, and ability of application services. Before processing and application, effective evaluation of remote sensing imagery or payload is required. Spatial resolution is an important index to measure the imaging performance of optical remote sensing payload, which is usually detected by three-bar targets or fan-shaped targets deployed in the remote sensing calibration. The three-bar detection method features simplicity and directness. However, due to discrete deployment, the detection results are easily affected by the phase difference factor, with low accuracy of the method. Fan-shaped targets are usually deployed on a two-dimensional plane. Due to the influence of perspective imaging and geometric distortion, the spatial resolution detection method should be carried out under the condition of vertical photography or geometric correction. Thus, during imaging at a large angle, the method cannot be directly employed for resolution detection due to large geometric deformation. Even under the condition of geometric correction, the resolution detection accuracy will be greatly reduced due to edge reduction or pixel mixing effect. Therefore, according to the isotropic characteristics of the three-dimensional spherical surface, this paper proposes to construct a fan-shaped resolution detection target on a spherical surface, which is called a spherical target. It also hopes that the spherical target can be helpful in solving the problems brought by three-bar or fan-shaped radial targets, and can be directly adopted for spatial resolution detection of high-resolution optical imaging loads under non-vertical imaging and non-geometric correction conditions.

Firstly, this paper builds a mathematical model of the spherical target and analyzes the design modes of the spherical target under equal and unequal conditions, with deployment strategies of spherical targets being given. Secondly, based on the traditional two-dimensional target image resolution determination method, an ideal spherical target image resolution determination method is given, and the resolution detecting range is analyzed for different types of spherical targets. Thirdly, the imaging characteristics of spherical targets are analyzed. Additionally, based on the building of the strict imaging model, the central projection deformation rule of the spherical target is studied by parameter simulation. The conclusions are obtained as follows. For multiple concentric circles with the center of the fan-shaped target strip as the origin, imaging ellipses on the two-dimensional images have the same eccentricity. The centers of the imaging ellipses are on the same straight line, while the proportional relationship between the long half axes of the ellipses is approximately equal to that of the image resolution. Finally, an image resolution determination method based on directional moving ellipse fitting is innovatively proposed for the spherical targets, and technical steps of the method are given.

Under the condition of central projection, images are simulated for the concentric circle of the spherical target (Fig. 5). Both the imaging characteristics and ellipse fitting characteristics are analyzed through the simulated data. Simulation results show that the distance ratio error of the long half-axis of the spherical target imaging ellipse is low, and the maximum deviation of the simulated data is not more than 1% (Table 4), which is suitable for spatial resolution detection. By employing the actual spherical target fixed in the China (Songshan) satellite remote sensing calibration field, the edge extraction (Fig. 6) and ellipse fitting (Fig. 7) process on a unmanned aerial vehicle (UAV) sensor target image is conducted, and then the ellipse eccentricity (Table 5) is calculated. Based on multi-scale ellipse construction and sampling point acquisition (Fig. 8), the gray curve of the target strip image obtained by the fixed center ellipse fitting method (Fig. 9) is intercepted and drawn and is compared with the intercepted and drawn results obtained by the proposed method (Fig. 10). Then, the optimal long half axis size of UAV image target ellipse is analyzed (Table 8), and the spatial resolution of UAV imaging load is calculated and evaluated to verify the validity and accuracy of the proposed method.

This paper aims at improving the imaging performance evaluation effectiveness of aerospace remote sensing payloads. Based on analyzing the advantages and disadvantages of the two-dimensional fan-shaped target in the remote sensing calibration field, it proposed a method for determining the spatial resolution of optical imaging payload directly on the spherical target image. Then, it also conducts experiments on both simulated data and actual UAV spherical target images and verifies the feasibility and correctness of the proposed method for remote sensing payload under tilt imaging conditions. Compared with the traditional two-dimensional fan-shaped target, the proposed method avoids the image correction processing based on ground control points or digital elevation model data and does not need the image geometric positioning model to be involved in the correction solution. In addition, the spherical target image can be directly employed to determine the load imaging performance, with a more accurate spatial resolution determination effect. The methods and experimental results in this paper can provide technical support for the construction of the spherical target in the remote sensing calibration field and the spatial resolution determination of high-resolution optical remote sensing payload.

As indicators of the ecological health of water bodies, planktonic algae are important primary producers in water ecosystems. The density monitoring of planktonic algae is of great significance to the diagnosis of water quality and the warning of algal blooms. Due to the presence of small individual and large numbers of planktonic algae, suspended impurities and other factors, traditional methods are difficult to achieve rapid and accurate measurements. Flow cytometric fluorescence method counts by detecting single cell fluorescence. This method features rapid, accurate, and highly efficient measurement, but it is not suitable for miniaturized field rapid measurement because of its complex injection structure and cumbersome focusing mode. Microfluidic chip technology realizes the functions of feeding, focusing, and sorting by constructing micro-channel pipelines on a square centimeter chip. This technology can simplify the complex feeding structure of the flow fluorescence method and has been widely employed in pharmaceutical and life science fields. Based on chlorophyll fluorescence in the characteristic band emitted by excited algal cells, this paper combines microfluidic chip technology and microfluorescence detection technology. It aims to realize rapid and accurate density detection of planktonic algal cells by detecting the number of single algal cell fluorescence peaks at a specific volume under a simple structure.

The experimental system consists of a sample feeding module, a fluorescence excitation module, and a fluorescence detection module. The excitation light from the monochromatic high-brightness LD is focused on the surface of the microfluidic channel by the drop-in microscopic optical structure. The algal cells in the microfluidic channel pass through the excitation window at a uniform speed under the propulsion of the syringe pump, and the cells are excited to emit fluorescence. Each cell flow across the microscopic field of view corresponds to a fluorescence peak, and then the density of algal cells in the sample can be calculated by recording the number of fluorescence peaks for a specific volume of the sample.

A method of detecting the planktonic algae density based on microfluidic and microfluorescence technology is studied to realize rapid and accurate density measurement of planktonic algal cells. By microfluidic chips, injection pumps, objective lenses, and photomultiplier tubes, an experimental system is established to measure the fluorescence signals of algal cells with different densities. Combined with optical simulations, this method can accurately measure the fluorescence signals of algal cells with low and medium densities. The relative errors of the counting results at low densities are less than 3.49% compared with those of microscopy and Coulter counting (Table 1), and the results of testing algal cells of different species and particle sizes show that the relative errors of the method in the density range of 1.3×10⁶ L^-1 are less than 3.96%. All of them were less than 3.96%, and the accuracy is not affected by the suspended matter, algal cell species, and size (Fig. 5). With the expanding range of testing algal density samples, the measurement error shows an increasing trend, which means that the measurement accuracy of the method and algal density is negatively correlated (Fig. 6). This is consistent with the simulation results, and the upper limit of the detection density is increased to 5×10⁶ L^-1 within the allowable error range of 10% (Fig. 7).

Due to the existence of small individual and large numbers of planktonic algae, suspended impurities and other factors, it is difficult to accurately detect algal density through the currently available algal density rapid detection technology. This paper proposes a microfluidics-microfluorescence-based method for planktonic algae cell density detection. This method is also based on microfluidics for rapid quantitative sample injection, confocal microfluorescence structure for high signal-to-noise acquisition of algal cell characteristic fluorescence signals, and the analysis of fluorescence peak information for planktonic algae cell counting. The results show that the relative measurement error in the density range of 1.3×10⁶ L^-1 is less than 3.96%, and the accuracy is not affected by the suspended matter, algal cell type, and size. The upper limit of algal density detection can be increased to 5×10⁶ L^-1 with the allowable error range of 10%, which can meet the demand of natural water bodies. The proposed microfluidic-microfluorescence technology that employs the unique chlorophyll fluorescence information of algae effectively overcomes the interference of suspended matters and features simple feeding module and optical structure. It is a new way for rapid and accurate detection of algal cell density.

Rapid industrialization in China has gradually led to long-term accumulated soil environmental problems. In addition, pollution caused by industrial and agricultural production processes constantly deteriorates soil environmental quality. Polycyclic aromatic hydrocarbons (PAHs) are a class of persistent organic pollutants that are mainly derived from man-made pollution and migrate globally with biogeochemical cycles. Residual PAHs in soil have a profound impact on environmental quality and human health. Therefore, it is of great practical significance to monitor organic pollutants in soil and timely grasp the pollution situation of the regional topsoil. At present, PAHs are detected using field sampling and laboratory instrument analysis. Trace analysis methods based on chromatographic separation have the advantages of low detection limit and high accuracy, but they usually require complex sample pretreatment, complicated operation, and long detection cycle. Thus, studies have developed a light-emitting diode (LED)-induced fluorescence methods using newly-invented luminescent materials and advanced production technologies. An LED can make up for the deficiencies of traditional excitation light sources, facilitating the production of small instruments. In this paper, the feasibility of using an LED-induced fluorescence spectroscopy technology for the rapid determination of PAHs in soil is discussed based on the fluorescence detection system of UV LED array excitation and a theoretical basis is provided for the application of LED-induced fluorescence in the rapid, accurate, and real-time detection of soil organic pollutants.

In this experiment, four types of PAHs were selected as the research objects. Two different types of soil, standard soil and actual soil, were selected to prepare soil samples with PAHs. An LED-induced fluorescence detection system mainly comprises an excitation light source, sample pool, optical fiber spectrometer, fluorescence signal acquisition device, and computer control unit. The UV LED array beam irradiates the soil sample, forming a moderately sized circular light spot on the surface of the sample. After filtering, the fluorescence signal generated via excitation was collected using the fluorescence collection device and transmitted to the spectrometer using the UV fiber for beam splitting and detection. Furthermore, the spectral data was stored and analyzed by the computer control unit, which is connected to the spectrometer. The Savitzky–Golay convolution smoothing method was used to preprocess the fluorescence spectrum of the detected PAHs to improve the accuracy of fluorescence signal extraction and effectively retain the useful information in the spectrum while filtering the noise.

In this study, a 255-nm UV LED-induced fluorescence detection system was designed to rapidly detect the fluorescence spectra of different PAHs in soil. The authenticity of the fluorescence spectra obtained by the experimental system was verified by comparing the LED-induced fluorescence spectra of PAHs in the soil with the three-dimensional fluorescence spectra of PAHs in the solution obtained by standard fluorescence instruments, which resulted in a reliable optical support system for the follow-up experiments. Our results show that the number of fluorescence bands and the position of characteristic peaks of PAHs in different soil types are basically the same, while the shape and intensity of the spectral peaks are slightly different. The fluorescence intensity and concentration of PAHs in two different standard soils show a good linear relationship within a certain concentration range, and the linear correlation coefficients are greater than 0.98. Under similar conditions, the system can effectively detect PAHs at lower concentrations in kaolin, and its detection limit is generally lower than PAH samples of loess. Thus, these findings indicate that soil microstructure characteristics and soil matrix complexity, and the combination of soil minerals and PAHs affect the detection of LED-induced fluorescence. The quantitative analysis of PAH soil samples under the actual complex soil background based on the fixed-point wavelength concentration inversion model reveal that the relative errors of the model for the concentration prediction of PAH soil samples in the test set are within the ideal range, except for some low concentration PAH samples. In addition, the average relative errors of different PAH samples are no more than 15.5%.

The LED-induced fluorescence method provides a new approach for detecting PAH organic pollutants in soil and makes up for the limitations of traditional chromatography and fluorescence spectroscopy. Based on the established UV LED-induced fluorescence detection system, this paper analyzed the LED-induced fluorescence characteristics of PAHs in different types of standard soil and further discussed the quantitative analysis of actual soil contaminated with PAHs. The experimental results verify the feasibility of the LED-induced fluorescence method for the rapid detection of PAHs in soil and its good applicability in different soil types. This also provides a technical reference for the rapid insitu detection of PAH organic pollutants in soil.

To meet the performance indicator requirements of long focal length, far detection distance, and high agility, modern optoelectronic equipment usually adopts the discrete optical-mechanical design to integrate a series of complex optical-mechanical components and sensors such as off-axis three mirrors, scanning reflector, fast steering mirror, and inertial measuring unit (IMU). These optical-mechanical components and sensors dramatically lead to more complex optical paths, making the optical axis pointing accuracy increasingly more sensitive. On one hand, the relative positions between the optical and mechanical components are changed easily under the excitation of vibration and shock mechanical environment and thermal environment. On the other hand, there are also measurement errors in IMU sensors. Both of them will ultimately lead to the out-of-tolerance of optical axis pointing accuracy. Frequent reassembly will not only consume huge manpower and material resources but also seriously interface with the program progress. Additionally, the influence of the assembly and adjustment residuals for the discrete optical system on the pointing accuracy cannot be ignored. Compared with the traditional mechanical assembly and adjustment, the digital correction can realize the rapid calibration of optical axis pointing through the pointing error modeling and the sample data obtained by tests. Therefore, it has become more and more urgent for the discrete optical system to develop research on digital correction technology.

A digital correction method of optical axis pointing error based on star measurement is studied to realize the fast correction of the optical axis pointing error of discrete optical systems. A discrete optical system containing the scanning reflector, fixed reflector, and IMU sensor is taken as an example to conduct the studies. At first, the optical axis pointing model in the geodetic coordinate system is derived by the quaternion method. In the pointing model, a total of 11 error parameters caused by structural processing assembly errors and sensor measurement errors are taken into account. Then, the equations are linearized by the first-order approximation of Taylor series expansion of the error parameters trigonometric function terms, and the calibration model of the error parameters is further deduced through the least square fitting. In addition, the calibration datum for star targets is established and the position of the star datum in the geodetic coordinate system is computed based on astronomical navigation principles. Finally, the digital correction technology of the optical axis pointing error is verified through the star measurement experiment.

A total of 11 error parameters of the discrete optical system and the fitting accuracy are all obtained by combining the calibration model of error parameters and data of test samples (Table 2). The error angles between the calculated and theoretical optical axis vectors before and after correction are computed for all samples and are compared (Fig. 7). The root mean square (RMS) value of the pointing errors after calibration is 11.61″, which equals the fitting accuracy, indicating the fitting accuracy can represent the optical axis pointing accuracy after correction. Through comparative analysis, the RMS value of the pointing errors decreases from 398.15″ to 11.61″ and the pointing accuracy is improved by 97.1% after the digital correction is developed (Fig. 7). The qualitative and quantitative verification tests are carried out respectively to evaluate the improvement effect of the pointing accuracy. The location values of the building feature point obtained through the gyro theodolite are taken as the given to adjust the optical axis. There is a large deviation between the optical axis pointing and the building feature target before correction, while the deviation almost disappears after pointing error correction (Fig. 8). After correction, the missing distance of the target star can be obtained by adjusting the optical axis pointing to the target star in the target pointing mode (Fig. 9). The RMS value of the missing distance is 10.78″ (Fig. 10). Both qualitative and quantitative verification test results show a significant increase in the pointing accuracy after digital correction.

We propose a digital correction method for the optical axis pointing error to improve the pointing accuracy of the discrete optical system. The theoretical modeling and experimental verification are carried out carefully. In theoretical modeling, the optical axis pointing model of the discrete optical system is built by the quaternion method, and the calibration model of the error parameters is deduced through the Taylor series first-order approximation of the trigonometric function terms of the error parameters and the least square fitting method. In addition, the star calibration datum is established based on astronomical navigation principles. The verification experiments indicate that the optical axis and building feature point almost coincide after correction, and the pointing error between the optical axis and star datum reduces from 398.15″ to 11.61″ after correction, with the pointing accuracy being improved by more than 97.1%.

Radiation calibration is a necessary prerequisite for the quantification of remote sensing data from laboratory calibration before launch to on-orbit calibration after launch throughout the entire life cycle of remote sensors. Site calibration is a common method for on-orbit alternative calibration of remote sensors with large fields of view. When a remote sensor is operating normally, it is calibrated with a large area of uniform ground objects as the calibration source, which has the advantages of a high calibration frequency and no need for synchronous measurement. The Greenland ice sheet (-75°S, 123°E) and the Antarctic ice sheet (73.375°N, -40°W) are commonly used as targets for snow and ice scenes, whose surfaces are covered with evenly distributed snow. Due to their high altitudes (usually greater than 2 km), they are less affected by the atmosphere and help obtain calibration samples with better data quality. Furthermore, ice and snow have a relatively flat spectrum in the visible range, which makes band transfer easier with other calibration methods. Thus, using the Greenland ice sheet and the Antarctic ice sheet as the calibration source of snow and ice scenes has many advantages for the calibration and verification of remote sensors.

The research method in this paper is based on the previous study of polar scene calibration methods. First, we choose Greenland as the calibration target of snow and ice scenes and select the calibration sample in the directional polarimetric camera (DPC) level 1 data. The area interfered with by cloud pixels is then eliminated after spectral channel traversal and angle traversal through the calibration sample's rows and columns. After substituting the surface bidirectional reflectance distribution function (BRDF) and atmospheric parameters (aerosol, water vapor, ozone, and other profiles) of the snow and ice scene into the radiative transfer model to obtain the zenith reflectivity and radiance, we test the on-orbit radiation response changes of the payload of the DPC on Gaofen-5 satellite. In addition, the obtained conclusions are in good agreement with the calibration results of the desert and ocean scenes, and the dispersion of the calibration results is smaller.

We compare the measured results of the DPC with the calibration results and obtain the following findings.

1) When the view zenith angle is less than 50°, the measured reflectance values of each band of the DPC have good consistency with the calibrated reflectance values. The standard deviations of their ratios are within 3%, and the root-mean-square errors were both lower than 2% (Fig. 4 and Table 3).

2) When the view zenith angle is less than 30°, the measured top-of-atmosphere (TOA) radiance values in each band of the DPC are compared with the calibrated TOA radiance values. The results show that the DPC data in the visible light band are relatively stable and have low uncertainty (Fig. 5 and Table 4).

3) The influence of the atmosphere (aerosol, water vapor, and ozone) and the surface BRDF on TOA reflectivity is analyzed. The uncertainty of the BRDF model of the snow and ice scene is 2%. Finally, we synthesize the uncertainty of each factor, and the synthetic uncertainty of each band is 2% (Table 5).

4) The research on the size of ice and snow shows that the average relative error of fine snow in each band is within 4%, and the relative error standard deviation of bands from 443 nm to 765 nm is within 2%, so the data of fine snow in each band is relatively stable (Fig. 9 and Table 6).

5) The results obtained in this paper are compared with the calibration results of the desert scene and the ocean scene. The comparison results show that the calibration coefficient of the Greenland snow and ice scene deviates from the average value of the calibration coefficients of the ocean scene and the desert scene within 5%, and the standard deviation of the Greenland polar scene is within 2% (Table 7 and Table 8).

Inspired by the idea of on-orbit alternative calibration, we propose an on-orbit radiometric calibration method based on the glacier scenes in the North and South Poles. We choose Greenland for research and analysis and conduct radiometric calibration of the remote sensor DPC with a large field of view. The measurement results of the DPC are compared with the calibration results. The comparison results show that the measured value and the calibrated value in each band of DPC are in good agreement. The standard deviation of the ratio of the measured value to the calibrated value is within 3%, and the root-mean-square error is lower than 2%, which proves that DPC has a good performance in snow and ice scenes. Moreover, through the error analysis of the atmosphere and the surface, it is shown that the BRDF on the surface has the greatest influence, and the final synthetic uncertainty in the visible light band is 2%. Finally, the comparison of the calibration results of this paper with the calibration results of the desert scene and the ocean scene also proves the stability of the calibration results of the Greenland snow and ice scene and the validity and reliability of the calibration method. The method described in this paper can provide long-term monitoring and calibration of the detection data while the payload is on orbit and contribute to the quality improvement of products for operational application.

In a satellite-based spectroscopic instrument, the telescope system forms the ground-based image on the slit, which is divided by a dispersive element and finally imaged on a surface detector, with the spectral information represented in one dimension and the spatial information in the other. The spectral and spatial information together is used to quantify the changes in the composition of the atmosphere. In the spectrometer system, the telescope system forms the image of the detection section at the slit, which is spectroscopically separated by the dispersive elements and finally re-imaged by the imaging system on the focal plane array (FPA). The intensity pattern in the spectral direction produced is called the instrumental spectral response function (ISRF). However, changes in the atmosphere and below (clouds, aerosol layer, and ground height) can cause changes in ground albedo or a large number of measured gas emission points. Such changes may lead to inhomogeneous illumination at the slit, which will cause the ISRF distortion and then the detection data deviation. As a result, the detection accuracy of content in atmospheric composition is affected. With the increasing requirements for atmospheric exploration and monitoring, the spectral accuracy of high-resolution spectrometers for Earth observation has increased significantly, but detection data deviation is more obvious when the spectral resolution of the instrument is higher. The concept of slit homogenizer is proposed during the Sentinel-5/UVNS imaging spectrometer development, and it can reduce the date error brought by the spectral signal distortion in the heterogeneous calibration scene.

The main work of this study is to analyze and study the principle of slit homogenizers and the systematic factors and external causes influencing ISRF. In addition, a geometric model and near-field diffraction principle are combined to establish the model structure from the telescope system to the slit homogenizer and from the slit homogenizer to the detector image plane. The equivalent simulation of parallel light is proposed to establish a more concise optical model, which reduces unnecessary calculations while retaining accuracy. Firstly, the rationality of the established model is briefly demonstrated. Based on the optical model, this paper analyzes the optical properties of the slit homogenizer. Through mathematical software and optical software simulation and mathematical analysis, the paper made concrete representations of the homogenizing effect of the slit homogenizer and the influence of the parameters of the slit homogenizer in a homogeneous incoherent scene and makes clearer the working principle and influencing factors of slit homogenizers. Then, the effect of the slit homogenizer is comprehensively evaluated from the entrance pupil scene: Analysis is made on the location of the strong emission point of trace gas and the influence of the coverage area on the ability of the slit homogenizer to maintain ISRF stability. The homogenization effect of the slit homogenizer is studied in different scenes under 25% illumination. The influence of different scenes on the spectrometer system ISRF is quantified. Finally, a method is proposed to improve detection accuracy.

The rationality of the optical model of the slit homogenizer and the ability of the slit homogenizer to calibrate the contrast of different scene cases are demonstrated from the simulation and experimental points of view (Figs. 8 and 11). In the mathematical model, the factors influencing ISRF can be summarized as the spectrograph pupil intensity distribution and the spectrometer system. On the one hand, the object-image relationship of different systems influences the point spread function (PSF), which also has a corresponding effect on the final result of ISRF (Table 1). On the other hand, the spectrograph pupil intensity distribution influences slit illumination, and the homogenization effect of the slit homogenizer based on the established optical model greatly depends on the entrance pupil scene. The difference in cloud and rain positions, the difference in the emission point position, and the size of the coverage area in the entrance pupil scene will affect the ISRF stability (Fig. 13), thus affecting the ability of the slit homogenizer to improve data detection accuracy (Table 2). To improve the ability of the slit homogenizer to calibrate the contrast of the different scene cases, this paper proposes a solution of adjusting the tilt angle of the extension mirror below the slit homogenizer (Fig. 14).

As the new generation of spectroscopic instruments have higher requirements in terms of spatial resolution and radiation accuracy, slit homogenizers are employed to reduce the errors of measurement data from the imaging spectrometers for Earth observation. In this paper, the principle of the slit homogenizer is elaborated using the principle of near-field diffraction. In addition, a simple and reasonable optical model is established, and a more concise and calculable mathematical expression is given. The systematic factors and external causes influencing ISRF are discussed. The ability of the slit homogenizer to keep ISRF stability is found to be limited in the investigation with different positions of emission points and changes in the size of the coverage area, and the error of ISRF may affect data inversion accuracy. Finally, this paper proposes a solution of adjusting the tilt angle of the extension mirror below the slit homogenizer to make up for the deficiency of the slit homogenizer in reducing the influence of extreme scene cases.

Mie scattering lidar is a remote sensing instrument with a high spatial and temporal resolution, and it has received extensive attention in research since it has successfully been applied in the fields such as atmospheric aerosol observation, cloud and fog characteristic analysis, and pollution emission monitoring. However, most of these ground-based lidars belong to the monostatic structure with the same transmitting and receiving sites. While they are been utilized to detect tropospheric or boundary layer, the transmitting and receiving coaxial systems (limited by the reflective telescope structure) or the transmitting and receiving systems with different axes (also known as off-axis) are almost affected by the near-field blind area and overlap factor, which results in the loss of near-field signals of the lidar or the reduction of accuracy. Therefore, the data of near-field point-type probing instruments (such as horizontal visibility meter, PM2.5 meter, and particle spectrometer) are usually required for data correction, compensation, or verification in these applications of lidar. However, the near-field atmospheric characteristics are usually essential data in meteorological, environmental, and other fields, which greatly limits the engineering and application process of the lidar. Therefore, overlap factor correction of lidar near-field signals and blind area have always restricted the practical process of lidar.

Based on the assumption of horizontal atmosphere uniformity, a multi-angle scanning near-field signal adaptive correction method for lidar is proposed using the vertical scanning function of the developed two-dimensional scanning lidar system. Firstly, according to the overlap factor model of an off-axis lidar system, the influence of laser beam divergence angle, telescope field angle, optical axis distance, and optical axis angle on the overlap factor is analyzed. Secondly, using the Fernald aerosol retrieval algorithm and taking the advantage of lidar vertical scanning, a multi-elevation angle correction scheme dependent on signal-to-noise (SNR) threshold is proposed to achieve adaptive scanning control of different atmospheric states. Combined with the ground extinction coefficient retrieved by the Collis method and the multi-angle scanning remote sensing data, the aerosol extinction coefficient without a blind area profile is retrieved, and the effectiveness of the correction scheme under different weather conditions is compared.

Based on the stratification assumption of a uniform atmosphere, the multi-elevation angle scanning control and correction steps (Figs. 5 and 6) are designed to realize the adaptive scanning control for different atmospheric states. Using multi-angle scanning remote sensing data and taking the result of Collis retrieval by horizontal detection as the ground extinction coefficient, the blind area profile of the atmospheric aerosol extinction coefficient is obtained [Fig. 7 (c) and Fig. 8 (c)]. When the horizontal visibility is better than 18 km and the SNR threshold is 20, the average relative deviation of the overlap factor correction curve and the correction curve of the horizontal detection method is about 20% [Fig. 7 (d) and Fig. 8 (d)], and the average relative error of the overlap factor curve is 4.2% (Fig. 9). Finally, a group of data with 24-h observation is shown to verify the scanning correction method (Fig. 10), and the retrieval without blind area of atmospheric aerosol can be realized.

In order to correct the near-field aerosol extinction profile of ground-based Mie scattering lidar, a vertical scanning correction method and multi-angle retrieval algorithm are proposed for near-field signals of Mie scattering lidar, and then the adaptive control of the atmospheric state is probed based on the assumption of horizontal atmosphere uniformity. Owing to the scattering lidar equation, a model of the overlap factor and blind area in the near field of the lidar is constructed, and the characteristics of the overlap factor and the blind area are analyzed and simulated based on the developed two-dimensional scanning lidar. In combination with the Fernald retrieval algorithm, a multi-elevation scanning control and correction scheme dependent on SNR is presented to realize adaptive scanning control for different atmospheric conditions. The profile of atmospheric aerosol extinction coefficient without blind area under different weather conditions is obtained by merging multi-angle scanning remote sensing data and the ground extinction coefficient obtained by the Collis method of horizontal detection, and the effectiveness of this scheme is verified. The data analysis results show that the adaptive correction of the lidar overlap factor can be achieved through seven elevations of about 15-min observation with the SNR ratio of 20 as the threshold when the horizontal visibility is better than 18 km. The mean relative deviation of the obtained calibration curve from horizontal correction is about 20%, and the mean relative error of the correction results of the overlap factor curves is 4.2%, which can realize the retrieval of atmospheric aerosol without blind area.

With the deepening research on space exploration, the photoelectric reconnaissance technology of various space targets has made great progress. Although this technology has been gradually improved, the problem of high-precision reconnaissance for long-range space targets has not been well solved. At present, the mainstream space photoelectric detection system is still based on the traditional optical system, which is limited by its diffraction limit, effective aperture, and other factors. This space photoelectric reconnaissance technology based on the traditional optical imaging theory is usually unable to perform in the field of remote accurate detection and imaging. Laser reflective tomography imaging (LRTI), as a new remote and high-precision space target detection method, has more advantages in high-precision imaging of remote targets. 1) LRTI is easy to implement in principle and easy to construct system structure. 2) The imaging resolution of LRTI is mainly related to the pulse width of the emitted light, the performance of the detector, and the signal-to-noise ratio, but is relatively unrelated to the detection distance and receiving aperture. 3) LRTI adopts the method of direct detection. It mainly receives the energy information from the laser echo signal reflected by the target, which is relatively less interfered with by atmospheric turbulence and other factors. Thus, LRTI has better applicability and practicability, and better application prospect in remote detection. Additionally, the imaging optimization method as the core of LRTI becomes the focus of many researchers. At present, the optimization schemes for LRTI quality usually start from the reconstruction algorithm, rotation center registration, subsequent image processing, and so on, but cannot eliminate the influence of image artifacts caused by noise and waveform distortion caused by turbulence. The noise and distortion doped in the laser echo signal greatly reduce the image quality optimization effect of the above schemes, and result in more complicated algorithm processing. Therefore, it is of great significance to develop a LRTI optimization method that can start from the echo signals.

We study the related problems of LRTI quality optimization, apply the echo decomposition method to the LRTI optimization method, and propose a LRTI quality optimization method based on echo decomposition. This method aims to suppress the influence of waveform distortion and noise by adjusting and optimizing the waveform of laser echo signals. The proposed method employs layer by layer stripping method to decompose the laser echo signal into several waveform components and filter the waveform components containing target information through a preset noise threshold. After obtaining the wave components that meet the conditions, these wave components are combined to obtain a more real laser echo signal, and then the target image is reconstructed through the reconstruction algorithm.

We build an experimental platform for LRTI and collect the projection data of the cube at a distance of 200 m (Fig. 6). The method put forward in our study is adopted to optimize the laser echo signal, and then the target image is reconstructed through the filtered back projection (FBP) algorithm (Fig. 9). The results show that when the same set of projection data is utilized, the peak signal to noise ratio (PSNR) before and after optimization by the proposed method is 16.5 and 17.8 respectively, with improved quality of the reconstructed image. At the same time, in terms of subjective visual perception, the artifact and noise of the optimized image are significantly less than those of the original image. This shows that the optimization method of LRTI based on waveform decomposition can effectively improve the quality of the reconstructed image, and better eliminate the influence of most ring artifacts and noise in the reconstructed image. This conclusion is still applicable under different sampling angle intervals (Fig. 10).

We propose a method of LRTI based on laser echo waveform decomposition. By applying the waveform decomposition processing method to the LRTI technology, this method realizes the clutter elimination and waveform correction in the laser echo signal. In addition, a LRTI verification experiment with a detection distance of about 200 m is carried out to verify the application effect of the laser echo waveform decomposition method in LRTI. The experimental results indicate that in the LRTI technology applied in more complex environments, the introduction of the waveform decomposition method can better eliminate the influence of ring artifacts and most of the noise caused by clutter, and has sound application effects at different sampling angle intervals. This will help to improve the quality of the reconstructed images of LRTI technology.

The FY-3D medium resolution spectral imager (MERSI-II) has been in orbit for more than five years. Quantitative application inversion shows that the radiometric response of some channels has deteriorated significantly, seriously affecting the accuracy of satellite quantitative remote sensing products. Therefore, it is necessary to evaluate the radiometric response of the MERSI-II and update the operation calibration according to the evaluation results. Compared with traditional targets such as deserts and glaciers, deep convective cloud (DCC) features a higher signal-to-noise ratio, isotropic reflection characteristics close to Lambertian, and minimum water vapor absorption. In-orbit calibration methods based on DCC target are widely employed to monitor the radiation performance of satellite sensors. However, the uncertainty factors in the DCC calibration method will affect the accuracy and stability, such as DCC bidirectional reflectance distribution function (BRDF) correction model, reflectivity probability density function (PDF) eigenvalues, and seasonal fluctuations. In this study, the basic DCC calibration method is optimized. Firstly, the BRDF correction effects of the Hu model and CERES thick ice cloud model are compared, as well as the stability of PDF mode and the mean of DCC reflectivity. Secondly, a deseasonal method based on reflectivity fitting residual and the moving average is proposed as the BRDF correction model of DDC is invalid in the SWIR band. Finally, the radiation performance of FY-3D/MERSI-II reflected solar bands is quantitatively evaluated by the optimization method. The evaluation results will be adopted as an important basis for the service calibration update of MERSI-II reflected solar bands.

We utilize DCC targets to evaluate the trend of radiometric response changes in most of the MERSI-II reflected solar bands. Firstly, the DCC target pixels are identified according to the infrared 10.8 μm channel brightness temperature threshold, observation geometric conditions (latitude, solar zenith angle, observation zenith angle), and spatial homogeneity conditions. Secondly, the solar zenith angle and earth-sun distance corrections are performed on the identified reflectivity of each DCC pixel to obtain the apparent reflectivity of the DCC. Then, two DCC BRDF models are leveraged to correct the anisotropy of apparent reflectance and compare the correction effects. Thirdly, the monthly DCC reflectivity PDF is constructed. Finally, the radiometric response of the MERSI-II sensor is evaluated by tracking the monthly DCC reflectivity PDF mean or mode sequence. In this section, we investigate the seasonal characteristics of DCC and propose a deseasonal method to reduce the fluctuations of the reflectivity PDF mean or modal sequence.

For the VIS/NIR bands, the correction effect of the CERES thick ice cloud model is better than the Hu model (Fig. 2), the RSD of the reflectivity is reduced by 15%-40%, and the fluctuations are reduced by about 10%-30% (Fig. 3). However, for the SWIR band, the two BRDF models have no obvious correction effect. For the VIS/NIR band, the monthly DCC reflectivity PDF mode is more stable than the mean, while the mean is more stable in the SWIR band (Table 2). The DDC distribution area migrates from north to south seasonally, and the same month in each year has high similarity, while the distribution of different months in each year is different (Fig. 4). The annual variation range of monthly DCC reflectance in VIS/NIR and SWIR bands is about 1.5% and 6.4% respectively, and SWIR band has higher seasonal sensitivity (Fig. 5). The proposed deseasonal method has yielded significant results in the SWIR band (Fig. 7). The reflectance RSD of 1.38 μm, 1.64 μm, and 2.13 μm channels decreases by about 22.4%, 22.0%, and 23.9% respectively, and the fluctuations decrease by about 52.9%, 51.2%, and 54.5% respectively, which compensates for the inefficiency of the BRDF model in the SWIR band (Table 3). The optimized DCC calibration method is employed to quantitatively evaluate the radiation degradation of the MERSI-II reflected solar bands from 2018 to 2022 (Fig. 8). The annual average degradation rate of 0.65 μm channel is only 0.02826% and the 0.47 μm blue channel is greater than 1.3%. The annual degradation rate of the three water vapor absorption channels in the NIR band is between 0.5% and 1%. This reveals a law that the degradation rate increases with the wavelength, and the fluctuation index is less than 3%, proving the superiority of the DCC calibration method in the water vapor absorption channel. The SWIR band has the most significant degradation, of which 1.38 μm has the largest degradation and the strongest fluctuation. The annual decay rates of 1.64 μm and 2.13 μm channels are 3.114 % and 2.134 %, respectively (Table 5).

We adopt the DCC target to evaluate the radiometric response trend of the MERSI-II reflected solar bands, and optimize the basic DCC calibration method. Firstly, the DCC BRDF correction model and the selection of monthly PDF eigenvalues (mean/mode) are optimized. Secondly, the seasonal characteristics of DCC are investigated, and a deseasonal method based on reflectivity fitting residuals and moving averages is proposed to compensate for the ineffectiveness of Hu and CERES thick ice cloud BRDF models in SWIR bands. This method significantly mitigates the RSD and fluctuations of monthly DCC reflectance in the SWIR band from 2018 to 2022, which are reduced by about 23% and 53%, respectively. This method is not only applicable to MERSI-II but also provides references for the radiometric calibration of other satellite optical remote sensors. Finally, the optimized DCC calibration method is utilized to quantitatively evaluate the attenuation of the radiometric response of the FY-3D/MERSI-II reflected solar bands. The results show that the blue channel and the four SWIR channels have significant degradation.

Vector radiation transport in scattering media is a research hotspot. The exact analytical solution to the vector radiative transfer equation cannot be obtained without the simplified treatment, and it needs to be calculated by numerical calculation methods. The polarized Monte Carlo program simulates the transmission of mass photons. As it does not lead to computational errors due to finite dispersion, the program is usually employed as a standard to verify the computational accuracy of other methods. At present, this method can obtain the outgoing polarization state of each light wave after the solution is obtained, but it is difficult to know the photon transmission situation. This limits the analysis of the polarization state retention of scattering light. However, photons with good retention characteristics of the polarization state usually have a small information loss and a long transmission distance. The study and analysis of these photons can be potentially applied to extract good transmission signals.

This paper proposes a method to count the photon polarization states during forward transmission into scattering media on the basis of the polarization meridian Monte Carlo program. The original algorithm and improved algorithm are shown in Fig. 1. The white part is required for calculating both the original algorithm and the improved algorithm, and the blue part is the additional flow for calculating the improved algorithm. One hundred thousand parallelly polarized photons or right-handed circularly polarized photons are sent into a slab represented by one particular particle distribution for each environment, and photons are transmitted at a given distance. Then, the aggregated polarization is calculated from the photons that arrive in a given area, and photons on the front face of the slab are considered the transmitted photons. This process continues for all the launched photons, and the result is calculated. For the original algorithm, a photon completes forward transmission when it passes through a specific forward distance. The improved algorithm adds a link to output the polarization state of each photon. Moreover, the improved algorithm can count the total number of received photons and the number of photons similar to the polarization state of the initial photon.

This paper uses an example to compare the original algorithm and the improved algorithm. The following simulations are performed in the polystyrene suspension with a mass concentration of 2.08 μg/μm³. The wavelength of incident light is 532 nm, and polystyrene's refractive index is 1.597. The particle diameter of the polystyrene suspension is 1 μm in simulation, and the transmission distance is 10 cm. One hundred thousand parallelly polarized photons or right-handed circularly polarized photons are launched for simulations of the original algorithm and the improved algorithm separately. There are 66358 received photons after the transmission of parallelly polarized photons and 66367 after the transmission of right-handed circularly polarized photons. After that, the first 100 received photons are selected as samples (Figs. 3 and 4). Calculations show that for the sample data of parallelly polarized photons, the N_RoPS is 0.13, and for the sample data of right-handed circularly polarized photons, the N_RoPS is 0.53. The calculation results are both similar to the overall situation. The simulated calculation of polarized light transmission in the polystyrene suspension demonstrates that the optimized method can not only reflect the change in the photon polarization state but also count the percentage of photons with good retention characteristics of the polarization state. The comparison between the original results and the optimization results shows that the results of the optimized algorithm are less than the polarization degree value. This is because the optimized algorithm not only avoids the error introduced in the calculation of the intensity difference in the orthogonal component but also excludes photons that are not similar to the polarization state of the initial photon.

Compared with the polarized Monte Carlo program, the improved method can not only reflect the change in the photon polarization state but also count the percentage of photons with good retention characteristics of the polarization state. It reflects the change in the polarization state of the transmitted photons in multiple dimensions. This study can provide technical support for research on the extraction of excellent transmission signals.

The polarized volume scattering function of particles in water is the most basic and complete parameter describing their scattering characteristics as it reflects the types, particle size spectra, shapes, and refractive indexes of the molecules and large particles in water. Therefore, the polarized volume scattering function of particles in water is of great research significance. This function is the most critical inherent optical parameter in the study of active and passive ocean optical remote sensing. Nevertheless, it is also the inherent parameter most difficult to measure. To solve the problem that no instrument is currently available in China for measuring the polarized volume scattering function of particles in water in a large angle range, this paper develops a measurement system for the polarized volume scattering characteristics of particles in water on the basis of a periscope-like optical path structure and the detection method of the rotating polarization detector. It further verifies the applicability of an output prism obtained by the half attenuation bonding method to the measurement of the polarized volume scattering characteristics in a large angle range and achieves the measurement of the 3×3 scattering Mueller matrix of particles in water in the range of 10°-170°.

In this study, pure water was used for baseline measurements, and the scattering characteristics of particles were determined by removing the contribution of the pure water signal to the total signal. According to the characteristics of standard particles in the Mie scattering theory, a standard particle (diameter of 0.2 μm) with relatively gentle Mie scattering results was used for amplitude calibration and calibration coefficient determination, and another one (diameter of 2 μm) with salient angle characteristics was used for angle calibration. Polarization was performed with a Stokes meter. The scattered light received by the detector suggests that the scattering optical paths detected at all angles are not the same, and the attenuation transmission distances in water are also different, which necessitates the normalization of the scattering optical path and the correction of the attenuation optical path.

In this study, a measurement system based on a periscope-like optical path structure and the rotating polarization detector was designed to measure the polarized volume scattering function of particles in water, and the measurement of the 3×3 polarized volume scattering function in the range of 10°-170° was thereby achieved. Moreover, a new type of exit prism with simpler processing was designed, and the measurement accuracy of the new prism was verified. To obtain accurate measurement results, this paper proposes strict methods for data processing and calibration procedures of the instrument, including baseline measurement, angle and amplitude calibration, polarization calibration, and data correction. An accurate theoretically calculated value of the polarized volume scattering function was obtained by applying the Mie scattering theory and compared with the measurement results of the experimental instrument. The measurement results are in good agreement with the theoretically calculated value. The accuracy of the measurement results of the experimental prototype is thus ensured. The system was further tested in the Qiandao Lake, and a 3×3 scattering Muller matrix of particles in water was obtained in the range of 10°-170° in this lake.