Laser Pulse Bidirectional Reflectance from CALIPSO Mission

In this Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) study, we present a simple way of determining laser pulse bidirectional reflectance over snow/ice surface using the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) 532 nanometer polarization channels' measurements. The saturated laser pulse returns from snow and ice surfaces are recovered based on surface tail information. The method overview and initial assessment of the method performance will be presented. The retrieved snow surface bidirectional reflectance is compared with reflectance from both CALIOP cloud cover regions and Moderate Resolution Imaging Spectroradiometer (Earth Observing System (EOS)) (MODIS) Bi-directional Reflectance Distribution Function (BRDF) / Albedo model parameters. The comparisons show that the snow surface bidirectional reflectance over Antarctica for saturation region are generally reliable with a mean value of about 0.90 plus or minus 0.10, while the mean surface reflectance from cloud cover region is about 0.84 plus or minus 0.13 and the calculated MODIS reflectance at 555 nanometers from the BRDF / Albedo model with near nadir illumination and viewing angles is about 0.96 plus or minus 0.04. The comparisons here demonstrate that the snow surface reflectance underneath the cloud with cloud optical depth of about 1 is significantly lower than that for a clear sky condition

Laser Pulse Bidirectional Reflectance from CALIPSO Mission

This paper presents an innovative retrieval method that translate the CALIOP land surface laser pulse returns into the surface bidirectional reflectance. To better analyze the surface returns, the CALIOP receiver impulse response and the downlinked samples distribution at 30 m resolution are discussed. The saturated laser pulse returns from snow and ice surfaces are recovered based on surface tail information. The retrieved snow surface bidirectional reflectance is compared with reflectance from both CALIOP cloud cover regions and MODIS BRDF/Albedo model parameters. Besides the surface bidirectional reflectance, the column top-of-atmosphere bidirectional reflectance is calculated from the CALIOP lidar background data. It is compared with bidirectional reflectance from WFC radiance measurements. The retrieved CALIOP surface bidirectional reflectance and column top-of-atmosphere bidirectional reflectance results provide unique information to complement existing MODIS standard data products and would have valuable applications for modellers

A New Approach for Checking and Complementing CALIPSO Lidar Calibration

We have been studying the backscatter ratio of the two CALIPSO wavelengths for 3 different targets. We are showing the ratio of integrate attenuated backscatter coefficient for cirrus clouds, ocean surface and liquid. Water clouds for one month of nightime data (left:July,right:December), Only opaque cirrus classified as randomly oriented ice[1] are used. For ocean and water clouds, only the clearest shots, determined by a threshold on integrated attenuated backscatter are used. Two things can be immediately observed: 1. A similar trend (black dotted line) is visible using all targets, the color ratio shows a tendency to be higher north and lower south for those two months. 2. The water clouds average value is around 15% lower than ocean surface and cirrus clouds. This is due to the different multiple scattering at 532 nm and 1064 nm [2] which strongly impact the water cloud retrieval. Conclusion: Different targets can be used to improve CALIPSO 1064 nm calibration accuracy. All of them show the signature of an instrumental calibration shift. Multiple scattering introduce a bias in liquid water cloud signal but it still compares very well with all other methods and should not be overlooked. The effect of multiple scattering in liquid and ice clouds will be the subject of future research. If there really is a sampling issue. Combining all methods to increase the sampling, mapping the calibration coefficient or trying to reach an orbit per orbit calibration seems an appropriate way

CALIOP Level 3 Ice Cloud Product

No abstract availabl

Forest Canopy Height Estimation from Calipso Lidar Measurement

The canopy height is an important parameter in aboveground biomass estimation. Lidar remote sensing from airborne or satellite platforms, has a unique capability for forestry applications. This study introduces an innovative concept to estimate canopy height using CALIOP two wavelengths lidar measurements. One main advantage is that the concept proposed here is dependent on the penetration depths at two wavelengths without making assumption about the last peak of waveform as the ground location, and it does not require the ancillary Digital Elevation Model (DEM) data in order to obtain the slope information of terrain. Canopy penetration depths at two wavelengths indicate moderately strong relationships for estimating the canopy height. Results show that the CALIOP-derived canopy heights were highly correlated with the ICESat/GLAS-derived values with a mean RMSE of 3.4 m and correlation coefficient (R) of 0.89. Our findings present a relationship between the penetration difference and canopy height, which can be used as another metrics for canopy height estimation, except the full waveforms

Forest Canopy Height Estimation from Calipso Lidar Measurement

The canopy height is an important parameter in aboveground biomass estimation. Lidar remote sensing from airborne or satellite platforms, has a unique capability for forestry applications. This study introduces an innovative concept to estimate canopy height using CALIOP two wavelengths lidar measurements. One main advantage is that the concept proposed here is dependent on the penetration depths at two wavelengths without making assumption about the last peak of waveform as the ground location, and it does not require the ancillary Digital Elevation Model (DEM) data in order to obtain the slope information of terrain. Canopy penetration depths at two wavelengths indicate moderately strong relationships for estimating the canopy height. Results show that the CALIOP-derived canopy heights were highly correlated with the ICESat/GLAS-derived values with a mean RMSE of 3.4 m and correlation coefficient (R) of 0.89. Our findings present a relationship between the penetration difference and canopy height, which can be used as another metrics for canopy height estimation, except the full waveforms

Lidar equation for ocean surface and subsurface

International audienceThe lidar equation for ocean at optical wavelengths including subsurface signals is revisited using the recent work of the radiative transfer and ocean color community for passive measurements. The previous form of the specular and subsurface echo term are corrected from their heritage, which originated from passive remote sensing of whitecaps, and is improved for more accurate use in future lidar research. A corrected expression for specular and subsurface lidar return is presented. The previous formalism does not correctly address angular dependency of specular lidar return and overestimates the subsurface term by a factor ranging from 89% to 194% for a nadir pointing lidar. Suggestions for future improvements to the lidar equation are also presented

Observations of Arctic snow and sea ice cover from CALIOP lidar measurements

International audienceThis paper describes the development and validation of a method to accurately identify snow/ice cover, surface melting, land surface and open water in polar regions using polar-orbiting Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements from the Cloud and Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) mission. The technique is based on the relationship between integrated attenuated backscatter color ratio and integrated depolarization ratio, and is proven to efficiently separate snow/ice cover and surface melting from open water and land surfaces. The method has been applied to 10 years (2006–2016) of CALIOP data to study the seasonal and inter-annual variability of Arctic sea ice cover and its declining trend. Results show that the area fraction of snow cover over land at latitudes > 60°N varied between 0.9 during winter and 0.1 in summer. The CALIOP observations of Arctic sea ice cover exhibit a strong seasonal cycle and significant inter-annual variability, which are consistent with the passive microwave-based sea ice results. The > 10 years of CALIOP continuous observations of the snow/ice cover will benefit the communities modeling snow/ice melting and climate change

Mother-of-Pearl cloud particle size and composition from aircraft-based photography of coloration and lidar measurements

Mother-of-Pearl clouds (MPCs) with their magnificent displays of spectral colors must have fascinated humans long before scientists began their study in the late 19th century. Human eye obersations of the iridescent could were described in greas detail and related to the weathe situation. Around the turm of the 20th contury, multistatic photometric observations allowed Strömer to estimate the MPC height which relealed their stratospheric origins

Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms