Retrieval Of Parameters Research Articles

FY-3G Satellite Instruments and Precipitation Products: First Report of China’s Fengyun Rainfall Mission In-Orbit

Precipitation is one of the most important parameters in Earth system but is hard to measure. China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polar-orbiting meteorological satellite program (FY-3). The first of total 2 rainfall missions scheduled, FY-3G, was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the tropical rainfall measuring mission in 1997 and global precipitation measurement core observatory in 2014. In this manuscript, we illustrate the platform of FY-3G and instruments mounted in great detail, with additional information about ground segments, designed sensor-based products, and retrieval of geophysical parameters. During the 4 months after launch, the specifications of the platform and instruments are under inspection as calibration and validation are carefully conducted. The first images captured by FY-3G are encouraging, and initial results show a strong capability for providing insights into all kinds of precipitation phenomena. The important work of data processing, such as data assimilation, data fusion between space-based and ground-based radar, and that between polar and geostationary satellites, as well as future applications in weather modification, has been prepared in advance. As a pioneer of China’s rainfall missions, FY-3G greatly improves our ability to provide global precipitation measurements, understand Earth’s water and energy cycle, and forecast extreme events for the benefit of society.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning.

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion. This drawback obstructed the accurate extraction of phenotypic parameters. Hence, this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques. The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network. The findings demonstrated that our network is stable and robust, as it can effectively complete diverse leaf point cloud morphologies, missing ratios, and multi-missing scenarios. A novel framework is presented for 3D plant reconstruction using a single-view RGB-D (Red, Green, Blue and Depth) image. This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions. Additionally, the extracted leaf area parameters, based on triangular mesh, were compared with the measured values. The outcomes revealed that prior to the point cloud completion, the R2 value of the flowering Chinese Cabbage's estimated leaf area (in comparison to the standard reference value) was 0.9162. The root mean square error (RMSE) was 15.88 cm2, and the average relative error was 22.11%. However, post-completion, the estimated value of leaf area witnessed a significant improvement, with an R2 of 0.9637, an RMSE of 6.79 cm2, and average relative error of 8.82%. The accuracy of estimating the phenotypic parameters has been enhanced significantly, enabling efficient retrieval of such parameters. This development offers a fresh perspective for non-destructive identification of plant phenotypes.

A novel theoretical method to determine the effective optical properties of high refractive index nanocomposites.

The continuous development of advanced optical devices towards high performance, miniaturization and integration has led to an increasing demand for high refractive index optical materials. Nanocomposites - made from high refractive index inorganic nanoparticles and good processability polymers - combine the advantages of both materials to create a synergistic effect. However, the diversity and complexity of the composites make laboratory preparation less efficient. Therefore, to prepare composites that meet the refractive index requirements, it is essential to predict the effective optical properties at different wavelengths. This study proposes a finite element parametric retrieval (FEPR) method to calculate the effective complex refractive index of nanocomposites (meff). The effects of the ratio of film thickness to particle diameter, particle arrangement, particle volume fraction (fv) and particle diameter (d) on meff are considered. The results demonstrate that changing the spatial arrangement, volume fraction and diameter of the particles can cause changes in the scattering effect of particles or the interaction between the electromagnetic waves and the particles, resulting in changes in the meff. Compared with effective medium theory (EMT), the FEPR method can be used to characterise the meff values in complex cases through finite element parametric modelling. The FEPR method is an efficient and accurate method for predicting the effective optical properties of nanocomposites, and can also be applied to the design and development of materials to discover the factors influencing the properties and variation patterns from large amounts of data, and to obtain predictive models that can guide the design of new materials.

Context Parameters Retrieval Framework from Electronic Healthcare Record through Biomedical NLP for Clinical Support

Context Parameters Retrieval Framework from Electronic Healthcare Record through Biomedical NLP for Clinical Support

Context parameters retrieval framework from electronic healthcare record through biomedical NLP for clinical support

This paper presents architecture for extracting context features from electronic health records (EHR) to design a clinical support system using transfer learning for natural language processing (NLP). The system is trained to provide a supporting summary to the medical practioners on the basis of the ICD 9 codes and respective symptoms of the patient. The BioALBERT model has been trained over biomedical corpora and the proposed model uses improvised parameter sharing techniques and requires less physical memory. The theoretical analysis of the proposed system is supported by the experimental analysis. MIMIC-III database has been used to fine train the proposed models and to assess the efficiency and efficacy of the proposed work. This study introduced a context-aware approach for extracting useful context from EHR, which can be used to acquire a basic understanding of the treatment path.

Aircraft Wake Recognition and Strength Classification Based on Deep Learning

Aircraft wake is a pair of counter-rotating vortices generated behind the aircraft, which can greatly impact the safety of fast takeoff and landing of aircraft and limit the improvement of airport capacity. The current wake parameter retrieval methods cannot locate the wake vortex's position and estimate its strength level in real time. To deal with this issue, a novel algorithm based on YOLOv5s deep learning network is proposed. The new algorithm establishes single vortex locating concept to adapt the wake vortex's evolution at complicate background wind field conditions, and proposes strength-based classification standard which can represent the real-time hazard of wake vortex to shorten the takeoff and landing intervals. Meanwhile, EIOU loss function is introduced to improve the precision of YOLOv5s network. Compared with the state-of-the-art object detection approaches, such as Cascade R-CNN, FCOS, and YOLOv5l, the superiority of new method is demonstrated in terms of accuracy and robustness by using the field detection data from Hong Kong International Airport.

Cloud parameter retrieval for Environmental trace gases Monitoring Instrument (EMI) using the O2-O2 absorption band

This study proposes an improved cloud parameter retrieval algorithm for the existing cloud parameter algorithm of the Environmental trace gases Monitoring Instrument (EMI) using the collision complex of the oxygen (O2-O2) absorption feature. The main improvements are the evaluation of the impact of temperature on the O2-O2 slant column densities (SCDs), and the well-corrected EMI measured reflectance. The cloud parameters will help improve the accuracy of EMI trace gas retrievals. The O2-O2 SCDs were fitted based on EMI-observed radiances and solar irradiance, and the impact of temperature on the O2-O2 SCD was evaluated. A simulated reflectance look-up table (LUT), as well as the O2-O2 SCD LUT, were established through the SCIATRAN radiative transfer model. Reflectance and O2-O2 SCD LUTs are functions of effective cloud fraction (ECF) and cloud pressure (CP), which can be inverted to ECF and CP LUTs. On this basis, the EMI ECF and CP were obtained using the EMI observation geometric information, EMI measured reflectance, and O2-O2 SCD to interpolate the ECF and CP LUTs. To evaluate the results, we performed an inter-comparison between the EMI cloud parameters, TROPOspheric Monitoring Instrument (TROPOMI) cloud products, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CP, and ground-based lidar cloud altitude. Of them, the one-day global ECF and CP of EMI and TROPOMI exhibited good agreement; the correlation coefficients were 0.925 and 0.866, respectively. Additionally, EMI was closer to the CALIOP results when the cloud pressure was >600 hPa. The comparison with the ground-based lidar cloud altitude also yielded good agreement during the continuous observation period from September 21 to November 10, 2019. The results prove that the improved EMI cloud retrieval algorithm could provide effective cloud products for EMI trace gas inversion.

Evaluation of Sentinel-6 Altimetry Data over Ocean

The Sentinel-6 Michael Freilich (S6-MF) satellite was launched on 21st November 2020. Poseidon-4, the main payload onboard S6-MF, is the first synthetic aperture radar (SAR) altimeter operating in an interleaved open burst mode. In this study, the sea surface height (SSH), significant wave height (SWH) and wind speed observations from the Poseidon-4 Level 2 altimetry products from November 2021 to October 2022 are assessed. The assessment contains synthetic aperture radar mode (SARM) as well as low-resolution mode (LRM) data. The SSH assessment is conducted using range noise, sea level anomaly (SLA) spectral analysis and crossover analysis, whereas the SWH and wind speed assessments are performed against NDBC buoy data and other satellite altimetry missions. The performance of the Sentinel-6 altimetry data is compared to those of Sentinel-3A/B and Jason-3 altimetry data. The 20 Hz range noise is 3.07 cm for SARM and 6.40 cm for LRM when SWH is 2 m. The standard deviation (STD) of SSH differences at crossovers is 3.76 cm for SARM and 4.27 cm for LRM. Compared against the NDBC measurements, the Sentinel-6 SWH measurements have a root-mean-square error (RMSE) of 0.361 m for SARM and an RMSE of 0.225 m for LRM. The Sentinel-6 wind speed measurements show an RMSE of 1.216 m/s for SARM and an RMSE of 1.323 m/s for LRM. We also present the impacts of ocean waves on parameter retrievals from Sentinel-6 SARM data. The Sentinel-6 SARM data are sensitive to wave period and direction as well as vertical velocity. It should be paid attention to in the future.

Determining optical material parameters with motion in structured illumination.

A set of power measurements as a function of controlled nanopositioner movement of a planar film arrangement in a standing wave field is presented as a means to obtain the thicknesses and the dielectric constants to a precision dictated by noise in an exciting laser beam and the positioning and detector process, all of which can be refined with averaging. From a mutual information perspective, knowing the set of positions at which measurements are performed adds information. While applicable to any arrangement of planar films, the implementation considered involves thin transmissive membranes, as are employed in applications such as optomechanics. We show that measured power data as a function of object position provides sensitivity to the film refractive index and far-subwavelength thickness. Use of a cost function allows iterative retrieval of the film parameters, and a multi-resolution framework is described as a computationally efficient procedure. The approach is complementary to ellipsometry and could play an important role in routine film characterization studies for fields involving solid state material processing, as is common in the semiconductor device field.

Finite element and multivariate random forests modelling for stress shield attenuation in customized hip implants

Primary total hip replacement surgery has an undisputable reputation as a widely successful orthopaedic operation, but it is beset by a phenomenon known as stress shielding. The cause of stress shielding is multifaceted. However, its reduction is reported to be hinged on the optimal design of prosthetic implants. Yet, to date, the design of a hip implant profile that behaves biomechanically similar to the natural physiological load-bearing zones of the femur remains an open problem. Along this vein, this paper instantiates an inquiry into the development of a framework that couples the capability of the finite element analysis (FEA) with that of machine learning methods toward the discovery of optimal design parameters for a customized hip implant. First, premised on the properties of a commercial normal-stem hip implant, a baseline computer-aided design (CAD) parametric model was created. From the baseline CAD model, a database of 120 hip implant profiles is established from the perturbation of the lateral edge, lateral angle, and the ratio of the radial cross-sectional areas of the implant. Next, the validation of the developed finite element procedure was conducted on a healthy intact femur and detailed numerical simulations were undertaken to assess the stress shielding (SS) attributes of all hip implants in the established database. The ensuing stress and strain data from the FEA is then deployed to ward a data-driven inverse model based on the random forests machine learning algorithm. Results-wise, the validation of the static analysis on the intact femur yielded von Mises stresses that matched those reported in published studies. Moreover, other results from the FEA revealed that a rectangular cross-sectioned hip implant resulted in the highest SS in the four zones of the proximal femoral compared to the trapezoidal cross-sectioned implant. Further, the inverse RF model exhibited excellent predictive capability and was subsequently employed towards the retrieval of the optimal geometric parameters that will manifest minimal stress shielding effect.

The pre-launch on-ground characterization of Mars Multispectral Imager for Subsurface Studies (Ma_MISS) spectrometer for ExoMars rover mission: Spectral calibration.

The Ma_MISS spectrometer is integrated within the drilling system of the Rosalind Franklin ExoMars rover. This paper reports the on-ground calibration campaign performed on the spectrometer. Here, we focus on the spectral calibration of the instrument. The experimental setup used to carry out calibration is described, and the methods used for data processing and key parameters retrieval are explained. In particular, the spectral parameters such as (i) pixel central wavelengths, (ii) spectral response function, (iii) spectral resolution, (iv) sampling, and (v) range are determined. In a follow-up paper, the linearity and radiometric calibrations are described, while in De Sanctis etal. [Planet. Sci. J. 3, 142 (2022)], the validation of spectral measurements performed on synthetic and natural rock targets is presented.

A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data

Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retrieve NSAT, and deep learning was used to optimize the calculations. Multisource data (physical model simulations, remote sensing data, and assimilation products) were used to establish the training and test databases. The NSAT retrieval accuracy was enhanced using the land surface temperature (LST) and land surface emissivity (LSE) as prior knowledge. The highest mean absolute error (MAE) and root-mean-square error (RMSE) of the retrieved NSAT data were 0.78 K and 0.89 K, respectively. In a cross-validation against the China Meteorological Forcing Dataset (CMFD), the MAE and RMSE were 1.00 K and 1.29 K, respectively. The actual inversion MAE and RMSE for the optimal band combination were 1.21 K and 1.33 K, respectively. The proposed method effectively overcomes the limitations of traditional methods as the inversion accuracy is enhanced by adding the information of atmospheric water vapor and more bands, and the applicability (portability) of the algorithm is enhanced using LST and LSE as prior knowledge. This model can become a general inversion paradigm for geophysical parameter retrieval, which is of milestone significance because of its accuracy and the ability to allow deep learning for physical interpretation.

Remote Sensing Retrieval of Total Nitrogen in the Pearl River Delta Based on Landsat8

The Pearl River Delta in South China is subject to severe eutrophication, which is significantly exacerbated by the total nitrogen (TN). Remote sensing technology with large-scale synchronous observations in the Pearl River Delta can effectively monitor organic pollution. Statistical methods based on remote sensing images have been widely used in water quality parameter retrieval for inland rivers, reservoirs, and lakes, but have seldom been applied in the Pearl River Delta. TN is also a non-optically active substance, so it is difficult to retrieve TN through analysis methods. This study retrieves the concentration of total nitrogen (TN) based on Landsat8 images of the Pearl River Delta using a statistical method. The stepwise regression function is built by analyzing the TN concentration and the single-band, two-band, and three-band spectral information groups measured by an ASD FieldSpec3 spectrometer. The retrieval results show that the proposed method performs well with a small mean absolute error (MAE) (0.36 mg/L for TN) and high agreement (R2 = 0.61 for TN) between the in situ data and the retrieval concentration. The results demonstrate that the concentration of TN in the east of the Pearl River Delta was higher than in the west. Dachan Bay and Shenzhen Bay had the highest TN concentrations, which were around 3.02 mg/L and 3.67 mg/L. The 750–850 nm band could be an important reference for further exploring the spectral characteristics and retrieval of TN. The retrieval method in this study is easy to implement and convenient for local TN distribution capture, which can provide a timely reference for daily water quality supervision and management in the Pearl River Delta.

Testing Sentinel-2 spectral configurations for estimating relevant crop biophysical and biochemical parameters for precision agriculture using tree-based and kernel-based algorithms

Sentinel-2 spectral configurations, S2-10m and S2-20m, were evaluated for retrieving essential crop biophysical and biochemical parameters and their effect on the performance of three machine learning regression algorithms (MLRAs) in two African semi-arid sites. The results were benchmarked against all spectral bands (S2-All). The results show that the S2-20m was more robust in retrieving Leaf Area Index (LAI) (RMSE cv : 0.58 m2 m−2, 0.47 m2 m−2), while the S2-10m provided optimal retrievals Leaf Chlorophyll a + b (LC ab ) (RMSE cv : 6.89 µg cm−2, 7.02 µg cm−2) for the two sites, respectively. In contrast, S2-20m performed better in retrieving Canopy Chlorophyll Content (CCC) in Bothaville to an RMSE cv of 35.65 µg cm−2, while S2-10m yielded relatively lower uncertainties (RMSE cv of 26.84 µg cm−2) in Harrismith. Moreover, various MLRAs were sensitive to the various spectral configurations, and performance varied by site. GPR and XGBoost were more robust, and thus have the most potential for crop biophysical and biochemical parameter retrieval in both sites. Based on the benchmark results, the two configurations can be used independently. The results obtained here are relevant for the rapid development of essential crop biophysical and biochemical parameters for precision agriculture using Sentinel-2’s 10 m or 20 m bands, without the need for resampling.

A review of recent developments in low-frequency ultra-wideband microwave radiometry for studies of the cryosphere

Over the past decade, a series of airborne experiments in the Arctic and Antarctica explored microwave emission from sea ice and ice sheets at frequencies from 0.5 to 2 GHz. The experiments were motivated by the fact that lower frequencies penetrate deeper into a frozen surface, thus offering the possibility to measure physical temperatures at great depths in ice sheets and, subsequently, other unique geophysical observables including sea ice salinity. These experiments were made feasible by recent engineering advances in electronics, antenna design, and noise removal algorithms when operating outside of protected bands in the electromagnetic spectrum. These technical advances permit a new type of radiometer that not only operates at low frequency, but also obtains continuous spectral information over the band from 0.5 to 2 GHz. Spectral measurements facilitate an understanding of the physical processes controlling emission and also support the interpretation of results from single frequency instruments. This paper reviews the development of low-frequency, wide band radiometry and its application to cryosphere science over the past 10 years. The paper summarizes the engineering design of an airborne instrument and the associated algorithms to mitigate radio frequency interference. Theoretical models of emission built around the morphologic and electrical properties of cryospheric components are also described that identify the dominant physical processes contributing to emission spectra. New inversion techniques for geophysical parameter retrieval are summarized for both Arctic and Antarctic scenarios. Examples that illustrate how the measurements are used to inform on glaciological problems are presented. The paper concludes with a description of new instrument concepts that are foreseen to extend the technology into operation from space.

Improved Estimation of O-B Bias and Standard Deviation by an RFI Restoration Method for AMSR-2 C-Band Observations over North America

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra

We develop a new method for analytical inversion of binned exoplanet transit spectra and for retrieval of planet parameters. The method has a geometrical interpretation and treats each observed spectrum as a single vector in the multidimensional spectral space of observed bin values. We decompose the observed into two orthogonal components: a wavelength-independent component corresponding to the spectral mean across all observed bins, and a transverse component that is wavelength dependent and contains the relevant information about the atmospheric chemistry. The method allows us to extract, without any prior assumptions or additional information, the relative mass (or volume) mixing ratios of the absorbers in the atmosphere, the scale height to stellar radius ratio, H/R S , and the atmospheric temperature. The method is illustrated and validated with several examples of increasing complexity.

Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes

• Time-efficient co-registration of UAV and ground LiDAR data is proposed for plantation forests. • Coarse alignment of forest point clouds is divided into ground alignment and canopy image matching. • Canopy images are matched by two pairs of canopy corner points and the canopy overlap rate. • The runtime of coarse alignment is less than 1 s for millions of forest points. Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate retrieval of forest parameters. However, tree occlusion poses challenges for those registration methods used artificial markers, and some automated registration methods have low time-efficiency due to the process of object (e.g., tree, crown) segmentation. In this study, we propose an automated and time-efficient method to register ULS and ground LiDAR (including terrestrial and backpack laser scanning) forest point clouds. Registration involves coarse alignment and fine registration, where the coarse alignment is divided into vertical and horizontal alignment. The vertical alignment is implemented by rotating grounds to the horizontal plane, and the horizontal alignment is achieved by canopy image matching. During image matching, vegetation points are projected onto the horizontal plane to obtain two binary images, and then, canopy shape feature, which is described by a two-point congruent set and canopy overlap, is used to match the binary images. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration in six plantation forest plots with sizes of 0.03 ha to 0.25 ha. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the coarse alignment errors are less than 0.20 m in the horizontal direction, the final registration accuracy is less than 0.15 m, and the average runtime is less than 1 s. Our study demonstrates the effectiveness of the proposed strategy and has able to perform accurate and quick registration of ULS and ground LiDAR data from plantation forests with different attributes.

Accurate quantification of lattice temperature dynamics from ultrafast electron diffraction of single-crystal films using dynamical scattering simulations.

In ultrafast electron diffraction (UED) experiments, accurate retrieval of time-resolved structural parameters, such as atomic coordinates and thermal displacement parameters, requires an accurate scattering model. Unfortunately, kinematical models are often inaccurate even for relativistic electron probes, especially for dense, oriented single crystals where strong channeling and multiple scattering effects are present. This article introduces and demonstrates dynamical scattering models tailored for quantitative analysis of UED experiments performed on single-crystal films. As a case study, we examine ultrafast laser heating of single-crystal gold films. Comparison of kinematical and dynamical models reveals the strong effects of dynamical scattering within nm-scale films and their dependence on sample topography and probe kinetic energy. Applying to UED experiments on an 11 nm thick film using 750 keV electron probe pulses, the dynamical models provide a tenfold improvement over a comparable kinematical model in matching the measured UED patterns. Also, the retrieved lattice temperature rise is in very good agreement with predictions based on previously measured optical constants of gold, whereas fitting the Debye-Waller factor retrieves values that are more than three times lower. Altogether, these results show the importance of a dynamical scattering theory for quantitative analysis of UED and demonstrate models that can be practically applied to single-crystal materials and heterostructures.

Pseudo-Passive Time-of-Flight Imaging: Simultaneous Illumination, Communication, and 3D Sensing

The 3D time-of-flight (ToF) cameras have recently received a lot of attention due to their wide range of applications. Despite remarkable advancements in ToF imaging, state-of-the-art ToF cameras are still afflicted by the power hungriness of their illumination sources. To tackle this problem, we exploited existing lighting infrastructure, which ensures the ubiquitous presence of modulated light sources in indoor spaces that serve as opportunity illuminators. We explored the bistatic geometry for passive imaging using the pulse-based ToF approach. Our work is inspired by the recently introduced visible light communication (VLC) or light-fidelity (Li-Fi) infrastructure. VLC allows the infrastructure to provide indoor simultaneous illumination, communication, and sensing (SICS). To this end, we designed a bistatic geometry for the purpose of attaining passive 3D imaging. Such capabilities are achieved by exploiting the pulse shape of the autocorrelation function of real optical signals generated by VLC/Li-Fi modules (e.g., OpenVLC and LiFiMAX). We demonstrated passive imaging by means of matched filtering. In this work, we also studied different sampling strategies in the time-shift domain, including uniform, random, and sparse rulers, which is another step forward toward preserving high depth accuracy with a minimal number of measurements. The proposed methodology achieved successful depth reconstruction with negligible root-mean-square error (RMSE) for the low signal-to-noise ratio (SNR) of the measurements. Parametric models, such as Gaussian and sum of sines, are used to characterize the cross correlation functions and allow for robust parametric depth retrieval from a few measurements. Moreover, we attained a 20-mm worst case error for a target at 25 cm. The experiment proved that the bistatic passive depth reconstruction is feasible.