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Abstract High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Abstract The prediction of wheel/rail rolling contact fatigue (RCF) crack initiation during railway operations is an important task. Since RCF crack evolution is influenced by many factors, its prediction process is complex. This paper reviews the existing approaches to predict RCF crack initiation. The crack initiation region is predicted by the shakedown map. By combining the shakedown map with various initiation criteria and the critical plane method, the crack initiation life is calculated. The classification, methodologies, theories and applications of these approaches are included in this paper. The advantages and limitations of these methods are analyzed to provide recommendation for RCF crack initiation prediction. This review highlights that wheel/rail dynamic characteristic, complex working conditions, surface defects and wear all affect the RCF crack initiation. The optimal selection of criteria is essential in the crack initiation prediction. Based on the research gap regarding the challenging process of crack initiation prediction detailed in this review, a proposed prediction process of RCF crack initiation is proposed to achieve a more accurate result.

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.

Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models, such as rough edges and insufficient overall accuracy, cloud and cloud shadow segmentation based on Swin-UNet was studied in the wide field of view (WFV) images of GaoFen-1 (GF-1). The Swin Transformer blocks help the model capture long-distance features and obtain deeper feature information in the network. This study selects a public GF1_WHU cloud and cloud shadow detection dataset for preprocessing and data optimization and conducts comparative experiments in different models. The results show that the algorithm performs well on vegetation, water, buildings, barren and other types. The average accuracy of cloud detection is 98.01%, the recall is 96.84% and the F1-score is 95.48%. The corresponding results of cloud shadow detection are 84.64%, 83.12% and 97.55%. In general, compared to U-Net, PSPNet and DeepLabV3+, this model performs better in cloud and cloud shadow detection, with clearer detection boundaries and a higher accuracy in complex surface conditions. This proves that Swin-UNet has great feature extraction capability in moderate and high-resolution remote sensing images.

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with “Belt and Road” countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity.

The concentration of particulate matter (PM2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM2.5 datasets, and the application of low-light satellite data to analyze nighttime PM2.5 concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM2.5 in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R2 = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM2.5 distribution over the area. Interannual and seasonal trends in nighttime PM2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM2.5 at night steadily decreased. Finally, the estimation of nighttime PM2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM2.5, enabled by nightly PM2.5, will serve as an invaluable resource for future research.

Rapid urbanization and the continued expansion of buildings have resulted in a consistent rise in the energy consumption of buildings. At the same time, the monitoring of building energy consumption has to achieve the goals of an “Emission peak” and “Carbon neutrality”. Numerous energy consumption monitoring systems have been established in several types of public buildings. However, there is a need to ensure that the data are continuously acquired and of superior quality. Scholars have noted that the in-depth research connected to the continuous monitoring of energy consumption in public buildings (CMECPB) is currently sparse. As a result, additional precise quantitative studies targeting the behavior of various stakeholders are also lacking. Hence, there is a need to explore the definition of value and the dynamic benefits of relevant subjects in continuous energy consumption monitoring based on evolutionary game theory and to propose incentive policies. This paper constructs an evolutionary game model for CMECPB between an energy service company (ESCO) and its owner to study the dynamic evolution path of a game system and the evolutionarily stable strategy under market-based mechanisms. Furthermore, by introducing government actions, the incentive policies and subsidy strategy for different subjects of interest are probed in detail by developing a principal-agent model to explore the incentive strength. The following conclusions can be reached: (1) it is inefficient and risky to rely only on the owner and the ESCO in achieving the optimal Pareto equilibrium; (2) the optimal incentives are “fixed incentives” in the case of information symmetry and a “fixed incentive + variable incentive” in the case of information asymmetry; (3) the choice of optimal incentive strategy is also influenced by the cost effort coefficient, risk aversion, external uncertainty, and integrated value transformation coefficient; (4) the incentive intensity and subsidy should be determined by comprehensive analysis with multiple indicators based on the conventional value of a project and the external value of a particular project. An in-depth understanding of each component of the CMECPB pathway yields insights into overcoming the challenges of building energy saving. Furthermore, the results may be useful in developing targeted, effective incentive policies for different disciplines and promoting the continued progress of monitoring building energy consumption and building energy efficiency.

To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.

Rolling contact fatigue (RCF) damages often occur, sometimes even leading to shelling on locomotive wheel treads. In this work, the RCF damage behaviors of two locomotive wheel materials with different molybdenum (Mo) contents were studied, and the influence of depth of wheel material was explored as well. The result indicates that with the increase in the Mo content from 0.01 wt.% (wheel 1, i.e., a standard wheel) to 0.04 wt.% (wheel 2, i.e., an improved wheel), the proeutectoid ferrite content and the interlamellar spacing of pearlite decreased, the depth and length of the RCF cracks increased and the average RCF live of locomotive wheel steel improved by 34.06%. With the increase in the depth of material, the proeutectoid ferrite content and the interlamellar spacing of pearlite increased, the depth of RCF cracks increased, the length of RCF cracks of wheel 1 increased and then decreased whereas that of wheel 2 decreased, the RCF live showed a decrease trend for wheel 1, while the RCF life increased and then decreased for wheel 2. The processes of shelling can be divided into three patterns: cracks propagating back to the surface, crack connection and fragments of surface materials.

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

The wear and fatigue behaviors of two newly developed types of high-speed railway wheel materials (named D1 and D2) were studied using the WR-1 wheel/rail rolling−sliding wear simulation device at high temperature (50 °C), room temperature (20 °C), and low temperature (−30 °C). The results showed that wear loss, surface hardening, and fatigue damage of the wheel and rail materials at high temperature (50 °C) and low temperature (−30 °C) were greater than at room temperature, showing the highest values at low temperature. With high Si and V content refining the pearlite lamellar spacing, D2 presented better resistance to wear and fatigue than D1. Generally, D2 wheel material appears more suitable for high-speed railway wheels.

For four types of railway rails, corresponding rolling tests have been executed by using the JD-1 wheel/rail simulation device based on the Hertz simulation method, which can ensure the reciprocal contact condition between simulation rail and wheel under experimental conditions approximate to practical contact conditions. The results indicate that, for the rail material which has a higher hardness, the wear volume was less and the plastic deformation layer was thinner after the rolling test, but the crack propagation was more significant and the fatigue damage was severer, showing that its wear resistance was better. The analysis shows that the fatigue resistance of U71Mn rail was better, and considering fatigue is the main failure type for high-speed rail, so it is more suited to the high-speed railway.

WS2-C is produced from a hydrothermal reaction, in which WS2 nano-sheets are coated with carbon, using glucose as the carbon source. In order to investigate the tribological properties of WS2-C as a lubricant additive, WS2-C was modified by surfactant Span80, and friction tests were carried out on an MRS-10A four-ball friction and wear tester. The results show that Span80 can promote the dispersibility of WS2-C effectively in base oil. Adding an appropriate concentration of WS2-C can improve the anti-wear and anti-friction performance of the base oil. The friction coefficient reached its lowest point upon adding 0.1 wt % WS2-C, reduced by 16.7% compared to the base oil. Meanwhile, the wear scar diameter reached its minimum with 0.15 wt % WS2, decreasing by 26.45%. Moreover, at this concentration, the depth and width of the groove and the surface roughness on the wear scar achieved their minimum. It is concluded that WS2-C dispersed in oil could enter friction pairs to avoid their direct contact, thereby effectively reducing friction and wear. At the same time, WS2-C reacts with the friction matrix material to form a protective film, composed of C, Fe2O3, FeSO4, WO3, and WS2, repairing the worn surface.

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.

A three-dimensional finite element model of rail grinding was established to explore the effects of grinding passes and grinding direction on the material removal behaviour of grinding rails during the grinding process. The results indicate that as the number of grinding passes increases, a decrease in the grinding force reduces both the amount of removed rail material and the surface roughness. There is a decrease in the grinding ratio caused by the increase in the wear on the grinding wheel and the decreased removal of the rail material. When the grinding direction changes, the wear of the grinding wheel decreases, which is contrary to the increasing trend of the amount of removed rail material, the grinding ratio, the surface roughness and the grinding force.

Four kinds of rail steels were tested to investigate the wear behaviors of wheel–rail materials under three kinds of axle loads. Results indicate that the increase in axle load not only significantly enlarges the wear loss but also enlarges the depth and the length of the fatigue cracks. However, with the decreases in the hardness ratios, some ripples are exhibited on the surface, and the wear surfaces become much rougher; the subsurface analyses deliver the presence of extremely rough surface, and the deformation depths are irregular. The relationship between the total wear loss of the wheel/rail system and the hardness ratio indicates that the hardness ratio of wheel/rail steels has slight impact on the total wear loss at a low axle load; however, the decrease in the hardness ratio enlarges the total wear loss significantly at a high axle load. In summary, a quenched rail should be chosen for the heavy-haul railway, and the hardness of the wheel steel should be raised to a degree close to the rail. However, a hot rolled rail is much suitable for the high-speed railway, and the wheel hardness should be smaller than the rail.

As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program.

The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%–5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day.