Lidar Parameters Research Articles

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide the precise height and detailed structure of trees, and can estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. First, the tree crowns are segmented using the profile-rotating algorithm, and tree positions are registered based on dominant tree heights. Second, considering the correlation between crown shape and tree species, we use DBN classifier to identify species using features of crowns, which are extracted to 1D array. Third, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass, and stock volume can be extracted from trees, enabling accurate large-scale forest surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east–west crown width, north–south crown width, diameter at breast height, aboveground biomass, and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based survey is practical and can be widely applied in forest resource monitoring.

Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.

Analysis and Correction of Water Forward-Scattering-Induced Bathymetric Bias for Spaceborne Photon-Counting Lidar

The new spaceborne photon-counting lidar, i.e., ICESat-2, has shown great advantages in obtaining nearshore bathymetry at a global scale. The forward-scattering effect in the water column is one of the main error sources in airborne lidar bathymetry (ALB). However, the magnitude of the bathymetric bias for spaceborne lidars and how can we effectively correct this bias have not been evaluated and are very worthy of investigation. In this study, the forward-scattering effect on spaceborne photon-counting lidar bathymetry is quantitatively modeled and analyzed based on the semi-analytic Monte Carlo simulation method. Meanwhile, an empirical formula for correcting forward-scattering-induced bathymetric bias specific to ICESat-2 is derived. When the water depth exceeds 20 m, this bias cannot be neglected for ICESat-2 even in clear open ocean waters. In two study areas with local in situ measurements (St. Thomas and Hawaii), the bathymetric bias of ICESat-2 in deep waters (>20 m) is corrected from exceeding 50 cm to less than 13 cm using the proposed empirical formula. This study is valuable to evaluate and correct the forward-scattering-induced bias for the existing ICESat-2 and is also fundamental to optimizing the hardware parameters of a possible future photon-counting bathymetric lidar.

MEASUREMENT OF EXTERNAL GEOMETRIC FEATURE PARAMETERS OF GIS EQUIPMENT BASED ON TERRESTRIAL 3D LIDAR

MEASUREMENT OF EXTERNAL GEOMETRIC FEATURE PARAMETERS OF GIS EQUIPMENT BASED ON TERRESTRIAL 3D LIDAR

Intrinsic Calibration of Multi-Beam LiDARs for Agricultural Robots

With the advantages of high measurement accuracy and wide detection range, LiDARs have been widely used in information perception research to develop agricultural robots. However, the internal configuration of the laser transmitter layout changes with increasing sensor working duration, which makes it difficult to obtain accurate measurement with calibration files based on factory settings. To solve this problem, we investigate the intrinsic calibration of multi-beam laser sensors. Specifically, we calibrate the five intrinsic parameters of LiDAR with a nonlinear optimization strategy based on static planar models, which include measured distance, rotation angle, pitch angle, horizontal distance, and vertical distance. Firstly, we establish a mathematical model based on the physical structure of LiDAR. Secondly, we calibrate the internal parameters according to the mathematical model and evaluate the measurement accuracy after calibration. Here, we illustrate the parameter calibration with three steps: planar model estimation, objective function construction, and nonlinear optimization. We also introduce the ranging accuracy evaluation metrics, including the standard deviation of the distance from the laser scanning points to the planar models and the 3σ criterion. Finally, the experimental results show that the ranging error of calibrated sensors can be maintained within 3 cm, which verifies the effectiveness of the laser intrinsic calibration.

Calibration by Air in Polarization Sensing

Scattered light polarization serves as an indicator and a characteristic of various processes in the atmosphere. The polarization measurements of all scattering matrix elements provide an adequate description of the optical and morphological parameters and orientation of particles in clouds. In this article, we consider the problem of the calibration of matrix polarization lidar (MPL) parameters. Calibration by air is an effective alternative to the technique for correcting optical element parameters and among the calibration parameters of the MPL optical path are the relative transmission coefficient of a two-channel receiver, the angular positions of the transmission axes of the optical elements of the transmitter and receiver units, including the polarizers and wave plates, and the retardance of wave plates. For the first time, the method of calibration by air was partially implemented in the MPL to study Asian dust in the atmosphere. We considered the calibration problem more generally and this was due to the need to calibrate different MPL modifications from stationary to mobile ones. The calibration equations have been derived in terms of instrumental vectors, and the method of their solution by the generalized least squares method has been proposed. The method has been verified on a numerical MPL model and validated using MPL measurements in Daejeon, Republic of Korea.

Examining the Angular Effects of UAV-LS on Vegetation Metrics Using a Framework for Mediating Effects

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).

Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery

The Full Waveform LiDAR system has been developed and used commercially all over the world. It acts to record the complete time of a laser pulse and has a high-resolution sampling interval compared to the traditional multiple-echo LiDAR, which only provides signals within a single target range. This study area mainly collects data from Riegl LMS-Q680i Full Waveform LiDAR and WorldView-2 satellite imagery, which focuses on buildings, vegetation, grassland, asphalt roads and other ground types as the surface objects. The amplitude and pulse width are selected as waveform basic parameters. The parameter of topography is slope, and the height classification parameters of the test ground are 0–0.5 m, 0.5–2.5 m, and 2.5 m. To eliminate noise, the neighborhood average is applied on the LiDAR parameter values and analyzed as the classification accuracy comparison. This survey uses Decision Tree as the classification method. Comparing the data between neighborhood average and non-neighborhood average, the data classification accuracy improves by 7%, and Kappa improves by 5.92%. NDVI image data are utilized to distinguish the artificial from natural ground. The results show that the neighborhood average with previous data can improve the classification accuracy by 5%, and Kappa improves by 4.25%. By adding NIR-2 of WorldView-2 satellite imagery to the neighborhood average analysis, the overall classification accuracy is improved by 2%, and the Kappa value by 1.21%. This article shows that utilizing the analysis of neighborhood average and image parameters can effectively improve the classification accuracy of land covers.

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of techniques to remotely measure black carbon aerosol particles with high range and time resolution. This article presents a direct and contact-free remote technique to estimate the black carbon aerosol number and mass concentration at a few meters from the emission source. This is done using the Colibri instrument based on a novel technique, referred to here as Picosecond Short-Range Elastic Backscatter Lidar (PSR-EBL). To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh–Debye–Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression of lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, we demonstrate the ability of the technique to quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.

Application of LiDAR System on Firefighting and Rescue

Abstract— This paper first introduces about LiDAR/ToF (light detection and ranging based on time of flight) sensor system. Then, the chance of applying LiDAR system on firefighting and rescue is also discussed. In addition, via several experiments of using LiDAR system on various levels of low visibility environment condition (such as strong back-ground light, snow, smoke, foggy, dust cloud), we will conclude the configurations and parameters of LiDAR for suitable choices and decisions when considering applying of LiDAR on firefighting and rescue. Keywords-- Firefighting and Rescue; LiDAR; ToF; Low Visibility Environments; SLAM

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

Comparison of Three Methodologies for Removal of Random-Noise-Induced Biases From Second-Order Statistical Parameters of Lidar and Radar Measurements.

Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability.

A Novel Fast Multiple-Scattering Approximate Model for Oceanographic Lidar

An effective lidar simulator is vital for its system design and processing algorithms. However, laser transmission is a complex process due to the effects of sea surface and various interactions in seawater such as absorption, scattering, and so on. It is sophisticated and difficult for multiple scattering to accurately simulate. In this study, a multiple-scattering lidar model based on multiple-forward-scattering-single-backscattering approximation for oceanic lidar was proposed. Compared with previous analytic models, this model can work without assuming a homogeneous water and fixed scattering phase function. Besides, it takes consideration of lidar system and environmental parameters including receiver field of view, different scattering phase functions, particulate sizes, stratified water, and rough sea surface. One should note that because the scattering phase function is difficult to determine accurately, the simulation accuracy may be reduced in a complex oceanic environment. The Cox–Munk model used in our method simulates capillarity waves but ignores gravity waves, and the pulse stretching is not included. The wide-angle scattering occurs in the dense subsurface phytoplankton, which sometimes makes it hard to use this model. In this study, we firstly derived this method based on an analytical solution by convolving Gaussians of the forward-scattering contribution of layer dr and the energy density at R in the small-angle-scattering approximation. Then, the effects of multiple scattering and water optical properties were analyzed using the model. Meanwhile, the validation with Monte Carlo model was implemented. Their coefficient of determination is beyond 0.9, the RMSE is within 0.02, the MAD is within 0.02, and the MAPD is within 8%, which indicates that our model is efficient for oceanographic lidar simulation. Finally, we studied the effects of FOV, SPF, rough sea surface, stratified water, and particle size. These results can provide reference for the design of the oceanic lidar system and contribute to the processing of lidar echo signals.

Investigation of Aerosol Inhomogeneities Parameters in Planetary Boundary Layer in UV-A Spectral Region

For today necessity of atmosphere wind remote sensing for wide pool of applications require development of new measurement methods and improvement of already existing.Currently doppler methods for wind speed measuring have largest sensing range. Correlation-based methods are provide lower sensing range for wind speed measurement. However, doppler-based lidar are expensive in opposite to simply designed correlation lidars that additionally allows to measure wind profile along sensing direction.Development of wind correlation lidars required knowledge of atmosphere aerosol inhomogeneities parameters.The most experimental research till now in this area are related to visible spectral range.One of the perspective eye-safety spectral range for wind lidar is UV spectral region. There not so much experimental research works of aerosol inhomogeneities parameters in UV.This paper is related to field experiments of aerosol inhomogeneities parameters in planetary boundary layer on 0.355 um operating wavelength.Block diagram and parameters of lidar with 0.355 operating wavelength is shown. Process of experiments is described. Typical atmosphere backscattering echo-signal for single laser pulse is demonstrated.Procedures if lidar signals processing are described, example of two-dimensional field of relative fluctuations of volume backscattering coefficient for different atmosphere conditions is shown.Results of measurement data processing shown that in planetary boundary layer average contrast of aerosol inhomogeneities in most cases has values in range 0,4% – 10 % (in case of snow up to 25%), and size in range 1,5 – 20 m. Dependency of observation number of aerosol inhomogeneities by range z from lidar to backscattering volume is descending with 1/z manner that the same for dependency for signal-noise ratio SNR(z).

Research on Automatic Joint Calibration Method of Multi 3D-LIDARs and Inertial Measurement Unit

<div class="section abstract"><div class="htmlview paragraph">In the field of automatic driving, the combination of 3D LIDAR and inertial measurement unit (IMU) is a common sensor configuration scheme in laser point-cloud localization, high-precision map making and point-cloud target detection. So it is critical to calibrate LIDAR and IMU accurately. At present, due to the large volume and high cost of 3D LIDAR with high-line-number(Such as 64 lines or 128 lines), the configuration scheme of using multiple low-line-number 3D LIDARs appears in the automatic driving vehicle sensing system. However, the common calibration methods are not suitable for multi 3D LIDARs and IMU parameters calibration on autonomous vehicle, which have the disadvantages of cumbersome implementation and low accuracy. In this paper, a joint calibration test platform composed of dual LIDARs and IMU is assembled, and a method of precise automatic calibration based on GPS/RTK data is proposed. Firstly, the initial parameters of the main 3D LIDAR and IMU are obtained by hand-eye calibration method, and then the motion distortion of the point cloud are removed by using the pose information. After global and local optimization of nearest neighbor error, the conversion parameters from the main LIDAR to IMU are obtained. Then, the remaining LIDARs are calibrated with the main LIDAR by combining coarse registration and fine registration, and finally realize the automatic calibration of external parameters of the entire system. The experimental results show that the proposed method has high calibration accuracy for the system composed of multiple 3D LIDARs and IMU, and the calibration effect is stable.</div></div>

Imaging S-lidars enhancement by optimizing range-domain characteristics

The paper is devoted to a relatively new, promising class of lidar systems called S-lidars (the S symbol comes from Scheimpflug), which is gradually becoming a recognizable, popular laser remote sensing tool. We analyzed their potential range-domain capabilities from different angles. A generalized structure of S-lidars combining the methods and approaches used has been developed that emphasizes specific features of their design and operation and distinguishes them from classic lidars as predecessors. Based on the introduced Q-factor of range resolution quality, we proposed to describe the boundaries and variability of the operation range as the depth of field of sharply focused images, where the required spatial selectivity is still provided. It has been shown how to optimize the range-domain characteristics of S-lidars according to the criterion of maximum operation range length and how to justify and algorithmize the proper selection of instrument parameters. We have proposed a range-domain efficiency criterion of S-lidars based on comparison of any real lidar with an optimized one in terms of achieved operation range taking into account its possible narrowing from far and near border. Requirements for lidar parameters were formulated to provide a range resolution of 1 m at the far border, remote at 1 km. An algorithm for solving the synthesis problem as a sequence of selecting instrumental parameters to ensure the required characteristics has been developed. The results of the analysis and synthesis of S-lidar systems under discussion can be adapted to a wide range of specific applications, which confirms the prospects of this class of imaging-based laser diagnostic devices.

A maximum bathymetric depth model to simulate satellite photon-counting lidar performance

With the development of photon-counting sensors, spaceborne photon-counting lidars have shown many advantages in mapping underwater topography. Although a space based lidar is normally a profiling system, the depth penetration and the vertical accuracy achieved with a bathymetric lidar is superior to imagery (that only provides relative depths). Therefore, many satellite derived bathymetry products use both active/passive spaceborne data to achieve spatial coverage as well as absolute depth measurements. Compared with shipborne and airborne bathymetric systems, satellite derived bathymetry data can have a global coverage, especially covering some remote areas where airborne/shipborne systems are hard or very expensive to reach. In this paper, a theoretical model is proposed to quantitatively analyze the maximum bathymetric depth of a satellite photon-counting lidar, which considers the system parameters, environmental effects, and the constraint of the SNR (signal to noise ratio). System parameters of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) as well as the MODIS (Moderate-resolution Imaging Spectroradiometer) and NCEP (National Centers for Environmental Prediction) datasets are used to provide systematic and environmental inputs to the model, and the ICESat-2 actual bathymetric data are used to verify the maximum depth estimated by the proposed model. In six different sites with different water qualities, solar angles and wind speeds, the theoretical maximum bathymetric depths agree well with the actually achieved ICESat-2 lidar depths, in terms of MAE (mean absolute error) of 0.50 m and the RMSE (root mean square error) of 0.60 m. The errors in all study areas are within 14% of the corresponding maximum depths. Additionally, the non-linear response of a photon-counting lidar, independent parameter impacts on the theoretical model, and the errors in the experimental process were quantitatively analyzed. In the future, this theoretical model can be used to evaluate the maximum bathymetric depth for a bathymetry investigation when applying system parameters and environmental parameters. In addition, it can be used to optimize the hardware parameters of a photon-counting lidar in its early design process.

星载海洋激光雷达叶绿素剖面探测能力估算

In order to evaluate and analyze performance of spaceborne oceanographic lidar for global ocean optical properties detection, a simulation system for spaceborne oceanographic lidar was developed based on lidar equation and the results of Monte Carlo simulation model. The lidar simulation system consisted of three modules, forward simulation, data inversion and error analysis, which could simulate the whole process of laser emission, transmission and detection. According to the given lidar parameters, the detection signals of 443 nm, 486.1 nm and 532 nm in four typical areas, Mediterranean Sea, Indian Ocean, Southern Ocean and Pacific Ocean, were simulated. The results show that the detection depths of 443 nm and 486 nm are approximately the same and deeper than that of 532 nm. For the given lidar parameters, the detection depths of 486.1 nm wavelength in the Pacific Ocean and the Southern Ocean are 120 m and 70 m, respectively, and the detection depth in the Mediterranean Sea and the Indian Ocean is about 100 m. The detection depths of chlorophyll-a concentration in the above sea areas are about 80 m, 50 m and 70 m, respectively.

Investigation of the effect of noise parameters of a 3D lidar on the error in estimating relief signatures of distant objects from 2D field intensity distributions of reflected radiation

We consider the influence of noise parameters of a 3D lidar and external background radiation on the error in estimating the relief signatures of distant objects from 2D field intensity distributions of the reflected radiation. A physical and mathematical model is developed to analyse the effect of noise. The dependence of the error in estimating the relief signatures at various signal-to-noise ratios is investigated taking into account the parameters of real multi-element photodetectors (MEPDs). The obtained results indicate the possibility of evaluating relief signatures of distant objects with high accuracy based on standard MEPDs simultaneously for a large number of points on the object’s surface with a signal-to-noise ratio of more than 4.

Optimum wavelength of spaceborne oceanic lidar in penetration depth

The evaluation of the wavelength parameters of spaceborne oceanic lidar is of great importance to ensure that the lidar system can provide more information of ocean and can be realized in engineering. In this paper, the optimum wavelengths of spaceborne oceanic lidar for purposes of ocean detection at coastal and global scales are analyzed mainly in terms of the penetration depth. The global distribution of ocean penetration depth and the corresponding optimum wavelength bands are calculated by using the oceanic optical properties data from Moderate Resolution Imaging Spectroradiometer (MODIS). Ocean with optimum penetration wavelength of 488 nm covers 61.82% of the global ocean and the optimum wavelength of 443 nm with ocean area share of 14.81% is a good complement for 488 nm. The penetration depths of 96.26% of the global ocean are deeper than 0.8 times the euphotic zone depths by using wavelengths of 488 nm and 443 nm, simultaneously. More importantly, taking advantage of the characteristic of solar Fe Fraunhoferline (438.355 nm) and H-β Fraunhoferline (486.134 nm), 70% of the background light can be suppressed by a filter with bandwidth of 0.1 nm and the penetration depth can be increased by approximately 5.0%. In conclusion, we propose that 486.134 nm is the optimum wavelength of single-wavelength spaceborne oceanic lidar and the combination of 486.134 nm and 438.355 nm is a good choice for dual-wavelength lidar for purpose of global ocean detection.