We introduce a novel low-power imaging system capable of multispectral ultraviolet (UV) imaging with the ability to distinguish between different UV wavelengths. This advancement is achieved by enhancing a three-layer vertically stacked photodiode (3T APS) complementary metal-oxide-semiconductor (CMOS) image sensor with specially manufactured and tuned perovskite nanocrystals (PNCs), effectively extending the sensor’s multi-channel quantum efficiency across the UVB and UVA range. Our imaging system includes all necessary peripherals and circuits. Experimental results demonstrate that the PNC-enhanced sensor can precisely differentiate 11 distinct UV wavelengths between 300 nm and 400 nm, a level of detail unachievable with the unmodified sensor. This technology offers substantial potential for industrial and clinical applications, including the in-vitro detection of multiple biomarkers with subtly different UV fluorescence emission spectra.

We present the design, fabrication, and optical validation of a single-chip multispectral imaging sensor spanning 400 nm to 1050 nm. The sensor integrates vertically stacked photodiodes with pixelated spectral filters to capture 12 spectral bands—three in the visible spectrum and nine in the near-infrared (NIR) spectrum. This enables simultaneous visualization of both tumor-targeted and lymph node-mapping probes using the same NIR excitation source. The system’s performance was validated through optical experiments, demonstrating accurate spectral differentiation between indocyanine green (ICG) and the folate-targeted probe Cytalux. This multispectral imaging approach addresses the limitations of current imaging systems, which are restricted to single-probe visualization, offering a powerful tool for enhancing cancer surgery outcomes.

We present a single-chip imaging sensor capable of simultaneous ultraviolet (UV), visible (VIS), and near-infrared (NIR) imaging. The system integrates a checkerboard-patterned multispectral pixelated filter array with a three-layer stacked photodiode architecture, achieving six distinct spectral bands. Each photodiode layer is optimized for specific wavelengths, leveraging the wavelength-dependent absorption properties of silicon. With a power consumption of 250 mW and nearly 100% transmission in both UV and NIR spectra, the platform is optimized for intraoperative use without disrupting the surgical workflow. Clinical data from 33 patients with breast cancer demonstrated a positive predictive value of 100% for detecting primary tumors based on UV autofluorescence. These results highlight the platform’s potential for enhancing cancer detection in real-time surgical settings.

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%–92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

High-frequency aerosol observation from new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land especially over heterogeneous land surfaces. In this study, a Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 AHI sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in the East Asia from 2015 to 2020, with a correlation coefficient (R) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05 + 15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as MODIS AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement, and demonstrate the robustness of proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution.

Near-infrared fluorescence image-guided surgery relies on an interdisciplinary community to develop near-infrared fluorescent markers and near-infrared sensitive cameras capable of mapping relevant structures during surgical procedures. As biochemists pursue a new generation of near-infrared fluorophores aimed at surgical oncology and other applications, optoelectronic engineers developing near-infrared imagers have been slow to adopt architectural improvements that will enhance outcomes in existing operations and to document optoelectronic characteristics that are needed to predict endpoints for new procedures. Here we present a single-chip snapshot multispectral imaging system that integrates arrays of bandpass optical filters and six-transistor backside-illuminated pixels to provide RGB-NIR images with low read noise (3.47 e−) and high dynamic range (81 dB). This imaging system is being used in clinical studies for sentinel lymph node mapping during breast cancer surgery.

Air pollution is one of the most concerned problems both for researchers and the public. In this study, we collected long-term observation of mass concentrations of PM10, PM2.5, and other gaseous pollutants in Wuhan, China, including sulphur dioxide (SO2), nitrogen oxide (NOx), from 2011 to 2014. The time series analysis is utilized to analyze the long-term trends of particulate matter (PM) and gaseous pollutants. Results show that the concentrations of PM and SO2have the trends to decrease due to the efforts of emission reduction and energy optimization. However, with the increase number of motor vehicles, the upward momentum of NOxwill not be reduced. The joint efforts of the government and the public are still needed. And, the seasonal characteristic for most pollutants is obvious. At last, we demonstrate the linear relationship between PM10and PM2.5, and reveal that PM2.5serves as the primary pollutant in Wuhan region, which should be paid more attention.

Carbon dioxide is considered as the main factor leading to global climate change[1, 3]. Precise measurements, especially the different absorption lidar (DIAL), are needed for analyzing the carbon sources and sinks. The Ground-based DIAL usually emits on-line and offline lasers alternately[5, 8, 9], but aerosols fluctuations will affect the lidar signals. In order to offset the effects caused by aerosols fluctuations, a combined observation of 532 nm Mie lidar and 1572 nm DIAL was introduced and analyzed firstly. This paper analyzes the linear relation of the extinction coefficient between 532 nm lidar and 1572 nm lidar, and applies this connection to DIAL CO2retrieval by using an improved DIAL method, considering the importance of aerosols. The results obtained by 532 nm Mie lidar work as a reference, revealing an appropriate period of calculation and serving as calibration data. The result s of the combined observation show the feasibility of our experiment and method.

Although the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) has been widely used in aerosol research, the classification of aerosol and cloud still exist some problems. Tradition classification method used by NASA is probability distribution functions (PDFs), but in reality, when we want to realize this algorithm, we fund it is difficult to describe the multi-modal distribution of cloud backscatter coefficients. Further, because ice cloud and dust aerosol have some similar properties, so it is not easy to identify them. In this paper, we introduce a classification method which based on Support vector machine (SVM), and add another characteristic. Then according to the result of classification inverse the aerosol characteristic, the height of cloud top, at the same time, combine with the CloudSat calculate the other cloud character, these data will be helpful for further climate research.

Studying optical properties of atmospheric aerosol is important because aerosol affects people around the world significantly. These effects strongly depend on the physical and optical properties of aerosol particles. In this paper, we propose to use lidar, sun-photometer, and CALIPSO synchronously, then present combined retrieval to investigate the optical properties of aerosol. The observations were performed at Wuhan during the period of December 2007 to May 2008. The primary results show that the proposed method improved the precision of aerosol optical depth effectively. Furthermore, long-term atmospheric and aerosol data could be obtained by consecutive observations. Also these data will be useful for future understanding about their environmental and climate effects.

The stable aerosol retrieval algorithm needs a prior assumption of lidar ratio (the extinction-to-backscatter ratio), and the known aerosol type that is the prerequisite of this assumption, so how to identify the clouds and aerosol from lidar profile is fundamental to acquire atmospheric optical parameter. In this paper, we first employ the CloudSat to validate the CALISPO's classificatory results, which is released in different versions, after choosing more accurate classification, then start retrieving. Second, sun-photometer is used for verifying the CALIPSO's calibration coefficient and supplies the day time records which are relatively more accurate. Finally, aerosol characteristic in Hubei province is analyzed. All the data will supply more available information for further climate change research.

in this paper, a new method is proposed, which combine CALIPSO lidar data with AERONET data to acquire the unstable aerosol information in Taiwan. First, introduce a CALIPSO retrieval arithmetic to obtain the aerosol optical depth, and then compare the differences between CALIPSO and AERONET. By combining these two techniques we could not only have the precise site data from AERONET, but also own the change information of aerosol in southeast China from CALIPSO. Different from AERONET, we can also display the spatial properties of aerosol from CALIPSO lidar backscatter data, such as, the strength of aerosol in each layer and their change with time in the aerosphere.

In this paper, a hybrid retrieval method of aerosol optical depth based on the combination of active and passive optical remote sensing is proposed. Two methods to retrieve the atmospheric optical depth are introduced: the so-called dark pixel method is used for retrieving the aerosol optical depth from MODIS; the other one is used by CALIPSO lidar data. After analyzing the two methods, the combined MODIS and CALIPSO method is applied for the aerosol optical depth, the primary experimental results show that these data are in agreement with each other in time-space evolvement trend.

A new atmospheric correction method of earth observation images based on the combination of satellite data and lidar data is proposed in this paper. A mobile scanning Mie lidar was developed to detect the aerosols' spatial and temporal distribution for the purpose above. To obtain more accurate data, future development plan of a multi-wavelength, multi-channel Raman lidar is discussed. Earth observation images processed by the radiative transfer model and this new method are presented. Also issues to approach the final goal of this new atmospheric correction method are discussed.