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Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chinese Atmospheric Environment Monitoring Satellite (DQ-1) is equipped with the wide swath imager (WSI), which includes three thermal infrared bands capable of providing global LST retrieval. This article introduces a nonlinear hybrid algorithm that combines the split-window (SW) algorithm and the temperature and emissivity separation (TES) algorithm, and the accuracies of the three algorithms, including hybrid, SW and TES algorithm are analyzed. The results demonstrated that the root mean square errors of LST for SW, TES, and hybrid algorithm are approximately 2.11, 1.78, and 1.64 K, with mean absolute errors (of 1.72, 1.40, and 1.21 K using in situ measurements from the SURFRAD sites. Cross-validation with moderate-resolution imaging spectroradiometer (MODIS) LST products showed that the hybrid algorithm outperforms the SW and TES algorithms in retrieving LST, achieving reductions in LST error of 0.43 and 0.16 K at the Qinghai Lake site, and 0.67 and 0.06 K at the Dunhuang site, respectively. In summary, this study demonstrates that the nonlinear hybrid algorithm can accurately estimate LST from DQ1/WSI data.

Summary: Background: To date, comprehensive data on the distribution of chronic kidney disease (CKD), the most prevalent comorbidity in diabetes, among Chinese adults with diabetes is lacking. Additionally, research gaps exist in understanding the association between CKD and cardiovascular health (CVH), an integrated indicator of lifestyle and metabolic control, within a nationwide sample of Chinese adults with diabetes. Methods: A nationally community-based cross-sectional survey was conducted in 2018–2020. 58,560 residents diagnosed with diabetes aged 18–74 years nationwide were invited to participate, and 52,000 participants with complete CKD data were included in this study. CKD was identified by the presence of albuminuria (urine albumin-to-creatinine ratio ≥30 mg/g) and/or decreased estimated glomerular filtration rate (eGFR,

Background: Hypertension is one of the most common chronic diseases, and dietary factors play an important role in hypertension. We examined the interaction of dietary sodium-to-potassium (Na/K) ratio and dinner energy ratio on hypertension. Methods: We conducted this study using data from the cross-sectional National Survey for Nutrition and Adult Chronic Disease in 2015 in Inner Mongolia, China. Dietary data were collected using 24-hour diet records with food weights across 3 consecutive days. Logistic regression was used to determine the interaction of dinner energy ratio and dietary Na/K ratio on hypertension. Results: A total of 1,861 participants were included in this study, and 914 individuals were hypertensive (49.1%). Dinner energy ratio and high dietary Na/K ratio were independently related to high prevalence of hypertension. A formal test showed that dinner energy ratio interacted significantly with dietary Na/K ratio on hypertension (P < 0.001), with an adjusted odds ratio (OR) of 1.119 (95% confidence interval [CI], 1.040–1.203). Participants whose dinner energy ratio greater than 39.1% and dietary Na/K ratio of 3.625–6.053 had the highest OR of hypertension prevalence, with an adjusted OR of 2.984 (95% CI, 1.758–5.066), compared with participants with dinner energy ratio of 30.2–39.1%, and dietary Na/K ratio less than 2.348. Conclusion: Our study highlighted the interactive effect of dinner energy ratio and dietary Na/K ratio on hypertension among adults in Inner Mongolia. We advocated a balanced diet (dinner energy ratio not small or large) and a low dietary Na/K ratio for reducing the prevalence of hypertension.

ObjectiveTo examine the prevalence and influencing factors of spirometry examination among residents aged ≥ 40 years in Inner Mongolia Autonomous Region (Inner Mongolia) for promoting prevention and control of chronic respiratory diseases. MethodsUsing multistage stratified cluster and probability proportionate to size sampling and a questionnaire adopted by Chinese Center for Disease Control and Prevention, we conducted a face-to-face survey among 2 400 permanent residents aged ≥ 40 years in four prefectures/banners with chronic obstructive pulmonary disease surveillance project in Inner Mongolia during May – December 2019. The estimated prevalence of spirometry examination in the participants was calculated based on complex sampling weight design; the impact factors of spirometry examination were explored with unconditional multivariate logistic regression analysis. ResultsOf the 2302 participants with eligible responses, 142 reported ever having pulmonary function test. The complicated weighted proportion of spirometry examination was 5.1% for the population surveyed. The results of logistic regression analysis showed that the residents with the education of junior high school and above, being enterprise clerks or professional and technical personnel in enterprises, being workers not classified or being unemployed, being aware of the names of respiratory diseases, ever suffering from respiratory symptoms, and with the history of respiratory illness were more likely to have spirometry examination; while the female residents were less likely to have the examination. ConclusionThe proportion of ever having spirometry examination was relatively low and mainly influenced by gender, education, occupation, the awareness of respiratory disease, respiratory symptoms, and the history of chronic respiratory disease among residents 40 years old and older in Inner Mongolia.

The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the “Cartwheel” configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China’s 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data.

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

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The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm.

Following the launch of the ZY1-02E satellite, the thermal infrared sensor aboard the satellite experienced alterations in the space environment, leading to varying degrees of attenuation in some components. The laboratory calibration accuracy could not satisfy the demands of quantitative production, and a certain degree of deviation was observed in on-orbit calibration. To accurately characterize the on-orbit radiation properties of thermal infrared remote sensing payloads, an absolute radiometric calibration campaign was carried out at the Ulansuhai Nur and Baotou calibration sites in Inner Mongolia in July 2022. This paper outlines the processes of onboard calibration and vicarious calibration for the ZY1-02E satellite, comparing the outcomes of onboard calibration with those of vicarious calibration. The onboard calibration method involved internal calibration, while the vicarious calibration method utilized an on-orbit absolute radiometric calibration technique based on various natural features that were not constrained by satellite–Earth spectrum matching requirements. Calibration coefficients were acquired, and the absolute radiometric calibration results of on-orbit vicarious and onboard calibration were compared, analyzed, and verified using the radiance computed from measured data and the reference sensor data. The accuracy of on-orbit absolute vicarious calibration was determined, and the causes for the decline in the radiation calibration accuracy on the orbiting satellite were examined. The findings revealed that the vicarious calibration results exhibited a lower percentage of radiance deviation compared with the onboard calibration results, meeting the quantitative requirements of remote sensing data. These results were significantly better than those obtained from onboard blackbody calibration, offering a data foundation for devising satellite calibration plans and enhancing calibration algorithms. In the future, the developmental trend of on-orbit radiometric calibration technology will encompass high-precision and slow-attenuation onboard calibration techniques, as well as high-frequency and simplified-step vicarious calibration methods.

Abstract Background Hypertension is a worldwide public health problem. We sought to examine the interactive associations among sodium intake, alcohol consumption and hypertension among older adult residents of Inner Mongolia in northern China. Methods This cross-sectional study used data from the National Survey for Nutrition and Adult Chronic Disease in Inner Mongolia. The prevalence of hypertension was age standardized by the direct method. Sodium intake and alcohol consumption were estimated using a weighing method and 24-h recalls on 3 consecutive days. Hypertension was either self-reported or field-measured. Participants were categorized into six subgroups according to combinations of sodium intake status and drinking level. Logistic regression was used to determine the interactive effect of sodium intake and drinking on hypertension. Results Of the 820 older adults who participated in this study, 523 (63.80%, age-standardized rate = 62.33%) had been diagnosed with hypertension. The mean sodium intake was 4.88 g. Sodium intake and drinking excessively were both independently related to higher risk of hypertension. A formal test for a multiplicative interaction between sodium intake and drinking revealed a significant interaction (p = 0.042), and the multivariable-adjusted odds ratio (95% CI) for the interaction was 1.1 (1.0–1.3). After adjusting for confounders, compared with moderate sodium intake and no drinking group, the risk of hypertension was highest among those with both excessive sodium intake and excessive alcohol consumption, with an odds ratio of 3.6 (95% CI: 1.7–7.9). Conclusions The study highlights the interactive effect of sodium intake and alcohol consumption on hypertension. Primary health care providers should pay special attention to older adults with hypertension—especially those with an unhealthy diet including both excessive sodium and excessive alcohol intake. These findings are applicable for older adults in Inner Mongolia and worldwide.

The Sentinel-2B offers high spatial resolution optical imagery from 13 bands in the visible, near infrared, and short-wave infrared range of the electromagnetic spectrum, providing enhanced continuity to monitoring global terrestrial surfaces and coastal waters. In this article, its radiometry calibration is evaluated using 37 clear-sky observations in 2018 from the four sites of radiometric calibration network for assuring data quality. However, since the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, it is difficult to determine which calibration sample is much more trustable. In view of this, by assuming that the calibration samples are independent of each other, a state-of-the-art reference value is derived by combining 37 calibration samples using a weighted average method, which has much lower uncertainty and approaches the “true” value. The reference value also could be used to compare each calibration result. The result shows that the reference value of the relative difference between the simulated and observed top-of-atmosphere reflectance is 4.29%, 4.95%, 4.54%, 5.34%, respectively, for bands 2, 3, 4, and 8, and the corresponding uncertainty is 1.09%, 1.10%, 1.10%, and 1.12%, respectively; the degree of equivalence for each sample is calculated by comparing each calibration result with the reference value. It is worth noting that the degrees of equivalence are lower than 5%, and the four samples on July 9, July 13, October 4, and October 11 perform worse than the other sample.

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs.

Abstract Background To investigate the prevalence of pterygium and associated factors in Han and Mongolian adults at four survey sites in Inner Mongolia, China. Methods We conducted a population-based, cross-sectional study as part of the China National Health Survey (CNHS). By means of a stratified sampling method, we finally included 2651 participants of 30 years of age or older from a total of 3468 eligible residents. Factors associated with pterygium were analysed by a univariate analysis and logistic regression models. Results The study population included 1910 Han and 741 Mongolian adults. The mean age ± standard deviation of the study cohort was 48.93 ± 11.06 years. The overall prevalence of pterygium was 6.4% (n = 169); 1.4% (n = 38) of the cases were bilateral and 4.8% (n = 128) were unilateral. The most common grade of pterygium was Grade 2. Based on the results of the univariate analysis, eleven factors were included in a multivariate analysis. The results indicated that age (P

This article addressed the simultaneous retrieval of land surface temperature (LST) and emissivity (LST&E) from the time-series thermal infrared data. On the basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST&E from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by the method assumption, instrument noise, initial emissivity, atmospheric downwelling radiance error, etc. The proposed method from the simulated data is more immune to noise than the existing methods. Even with a noise equivalent delta temperature of 0.5 K, the root-mean-square error of LST is observed to be only 0.13 K, and that of the land surface emissivity (LSE) is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. To validate the proposed method, a field experiment from June to September 2017 was conducted for sand target in Baotou site, China. The results show that the samples have an accuracy of LST within 0.87 K and that the mean values of LSE are accurate to 0.01.

Optical satellite sensor generally suffers from drifts and biases relative to their prelaunch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transferring radiometric values between satellite sensors via a stable target. The data quality of visible and near-infrared (VNIR) sensor onboard Gaofen-4 (GF-4) satellite seriously depends on vicarious calibration, which is performed once every year, thus, a cross-calibration approach is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly have similar variation trend as a sinusoidal function during 2017 and 2018, varying from 0.00023 to 0.00079. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of referenced sensors, the uncertainty of the MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%-6.64%.

Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new cross-calibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.

Abstract Background Cigarette smoking is a known risk factor for cardiovascular disease (CVD), but the association between smoking and blood pressure is unclear. Thus, the current study examined the association between cigarette smoking and blood pressure in men. Methods Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP) were examined using digital blood pressure measuring device, and smoking status was determined with China National Health Survey. Results The ANCOVA showed that the adjusted DBP and MAP were lower in current smokers versus nonsmokers and the adjusted SBP was lower in current smokers versus former smokers (P

Abstract Background Han and Mongolian populations constitute approximately 96 % of the population of Inner Mongolia Autonomous Region, and the two ethnic groups have different genetic backgrounds and lifestyle. We aim to assess the prevalence, awareness, treatment, control, and related risk factors of hypertension among urban adults in Inner Mongolia, with the comparison of the differences between Mongolian and Han populations in this respect. Methods Three thousand two hundred fifty-one individuals aged 20–80 years (2326 Han and 925 Mongolian) were selected using a multistage cluster sampling method from Inner Mongolia in 2014. The adjusted prevalence, awareness, treatment and control of hypertension were evaluated by the Logistic regression. In addition, possible interactions were also tested. When interactions were found significant, strata-specific analysis were performed. Multivariate logistic regression was used for estimating independent associations between risk factors and hypertension. Results The prevalence of hypertension was 27.47 % for Han population, 31.46 % for Mongolian population. The adjusted prevalence, awareness, treatment and control of hypertension were 26.45, 65.43, 78.24 and 48.28 % in Han, and 31.30, 68.22, 85.57 and 50.55 % in Mongolian, respectively. There was no significant difference in the adjusted awareness, treatment and control of hypertension among Mongolian and Han adult residents (all P >0.05). Lower prevalence of hypertension was associated with younger age and healthy weight in both Mongolian and Han adults. Within Han adults, high education, moderate physical activity and non-alcohol drinkers were additionally associated with lower prevalence of hypertension, whereas within Mongolian adults, lower prevalence was associated with being female. Among residents with medium education level, nondrinkers had 0.60 times lower odds of having hypertension than current drinkers (OR = 0.60, 95 % CI: 0.44–0.82); among residents with high education level, nondrinkers has 0.65 times lower odds of having hypertension than current drinkers (OR = 0.65, 95 % CI: 0.43–0.97). Conclusions Mongolian population had a higher prevalence of hypertension than Han population. There were no significant difference between Mongolian and Han population in awareness, treatment and control of hypertension, which suggested that there was no difference between the two ethnicities in the distribution of health resources.

The Day–Night Band (DNB) imaging sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) adds nighttime monitoring capability to the Suomi National Polar-Orbiting Partnership and National Oceanic and Atmospheric Administration 20 weather satellite launched in 2011 and 2017, respectively. Nighttime visible imagery has already found diverse applications, but image quality is often unsatisfactory. In this study, variations in observed top-of-atmosphere (TOA) reflectance were examined in terms of nighttime bidirectional effects. The Antarctica Dome C ground site was selected due to high uniformity. First, variation of reflectance was characterized in terms of viewing zenith angle, lunar zenith angle, and relative lunar azimuth angle, using DNB data from 2012 to 2020 and Miller–Turner 2009 simulations. Variations in reflectance were observed to be strongly anisotropic, suggesting the presence of bidirectional effects. Then, based on this finding, three popular bidirectional reflectance distribution function (BRDF) models were evaluated for effectiveness in correcting for these effects on the nighttime images. The observed radiance of VIIRS DNB was compared with the simulated radiance respectively based on the three BRDF models under the same geometry. Compared with the RossThick-LiSparseReciprocal (RossLi) BRDF model and Hudson model, the Warren model has a higher correlation coefficient (0.9899–0.9945) and a lower root-mean-square-error (0.0383–0.0487). Moreover, the RossLi BRDF model and Hudson model may have similar effects in the description of the nighttime TOA over Dome C. These findings are potentially useful to evaluate the radiometric calibration stability and consistency of nighttime satellite sensors.

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

Field calibration is a feasible way to evaluate space-borne optical sensor observations via natural or artificial sites on Earth’s surface with the aid of synchronous surface and atmospheric characteristic data. Since field calibration is affected by the coupled effects of surface and atmospheric characteristics, the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, making it difficult to determine the consistency of these multiple calibration results. In view of this, by assuming that the radiometric performance is invariant during field calibration and the calibration samples are independent of each other, the surface–atmosphere invariant Key Comparison Reference Value (KCRV) is essentially derived from various calibration results. As the number of calibration samples increases, the uncertainty in the KCRV should decrease, and the KCRV should approach the “true” value. This paper addresses a novel method for estimating a weighted average value from multiple calibration results that can be used to compare each calibration result, and this value is accepted as the KCRV. Furthermore, this method is preliminarily applied to the field calibration of the Multispectral Instrument (MSI) onboard the Sentinel-2B satellite via the desert target at the Baotou site, China. After employing a chi-squared test to verify that 12 calibration samples are independent from each other, the KCRV of the 12 calibration samples at the Baotou site is derived, which exhibits much lower uncertainty than a single sample. The results show that the KCRVs of the relative differences between the simulated and observed at-sensor reflectance are 3.75%, 5.11%, 6.09%, and 5.03% for the four bands of Sentinel-2B/MSI, respectively, and the corresponding uncertainties are 1.84%, 1.87%, 1.90%, and 1.93%. It is noted that the KCRV uncertainty obtained with only 12 calibration samples is reduced significantly, and in the future, more samples in other instrumented sites will be used to validate this method thoroughly.

Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from Airborne Hyperspectral Scanner (AHS). The estimation is conducted after eliminating reflected direct solar radiance with the aid of water vapor content (WVC), the view zenith angle (VZA), and the solar zenith angle (SZA). The results demonstrate that the LST can be well estimated with a root mean square error (RMSE) less than 1.0 K. Furthermore, an error analysis for the proposed method is also performed in terms of the uncertainty of LSE and WVC, as well as the Noise Equivalent Difference Temperature (NEΔT). The results show that the LST errors caused by a LSE uncertainty of 0.01, a NEΔT of 0.33 K, and a WVC uncertainty of 10% are 0.4~2.8 K, 0.6 K, and 0.2 K, respectively. Finally, the proposed method is applied to the AHS data of 4 July 2008. The results show that the differences between the estimated and the ground measured LST for water, bare soil and vegetation areas are approximately 0.7 K, 0.9 K and 2.3K, respectively.

Urban land surface temperature (LST) is critical for understanding urban thermal environments and expansion. Compared with natural surfaces, urban areas have complex geometric structures causing multiple scattering and adjacency effects. This study combines the XGBoost algorithm with an improved temperature and emissivity separation (TES) algorithm to retrieve urban LST and land surface emissivity (LSE) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data. Three aspects are included: firstly, sing the XGBoost algorithm to estimate urban canopy bright temperature (BT) from 5 ASTER TIR top of atmosphere (TOA) BTs; secondly, employing an improved TES algorithm based on sky view factor (SVF) to separate LST and LSE; finally, the accuracy of the proposed algorithm is evaluated from simulation data. Simulation results show that the root mean squared errors (RMSEs) of the urban canopy BTs are about 0.2 K and 1.2 K using the XGBoost algorithm and split window (SW) algorithm, respectively. The urban LST and LSE RMSEs using the proposed algorithm are approximately 0.36 K and 0.022, respectively. Applied to Beijing and Wuhan, China, the algorithm yields slightly lower urban LST compared to ASTER level-2 products, with LST/LSE RMSEs of 0.72 K and 0.017, respectively.

The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model.