scholarly journals Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

2018 ◽  
Vol 11 (6) ◽  
pp. 3689-3715 ◽  
Author(s):  
Daniel J. Miller ◽  
Zhibo Zhang ◽  
Steven Platnick ◽  
Andrew S. Ackerman ◽  
Frank Werner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Droplet Size ◽  
High Spatial Resolution ◽  
Atmospheric Correction ◽  
Cloud Microphysics ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Test Bed ◽  
Satellite Retrieval

Abstract. Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

scholarly journals Comparisons of bispectral and polarimetric cloud microphysical retrievals using LES-Satellite retrieval simulator

2017 ◽  
Author(s):  
Daniel J. Miller ◽  
Zhibo Zhang ◽  
Steven Platnick ◽  
Andrew S. Ackerman ◽  
Frank Werner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Droplet Size ◽  
High Spatial Resolution ◽  
Cloud Model ◽  
Atmospheric Correction ◽  
Droplet Size Distribution ◽  
Systematic Difference ◽  
Test Bed ◽  
Satellite Retrieval

Abstract. Many passive remote sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological datasets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g. MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g. POLDER, Polarization and Directionality of Earth’s Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration, atmospheric correction, etc. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote sensing retrieval simulator. Created by coupling a large eddy simulation (LES) cloud model with radiative transfer models, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison), but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are indeed highly correlated with one another. The small differences at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval, but generally maintains a constant mean value.

scholarly journals An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

2020 ◽  
Vol 12 (17) ◽  
pp. 2752
Author(s):  
Christopher O. Ilori ◽  
Anders Knudby
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Complete Solution ◽  
Atmospheric Correction ◽  
Florida Keys ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Landsat 8 ◽  
Depth Range

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

scholarly journals High resolution and Monte Carlo additions to the SASKTRAN radiative transfer model

2015 ◽  
Vol 8 (3) ◽  
pp. 3357-3397 ◽  
Author(s):  
D. J. Zawada ◽  
S. R. Dueck ◽  
L. A. Rieger ◽  
A. E. Bourassa ◽  
N. D. Lloyd ◽  
...  
Keyword(s):  
Monte Carlo ◽  
High Resolution ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Reference Model ◽  
Monte Carlo Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Systematic Bias

Abstract. The OSIRIS instrument on board the Odin spacecraft has been measuring limb scattered radiance since 2001. The vertical radiance profiles measured as the instrument nods are inverted, with the aid of the SASKTRAN radiative transfer model, to obtain vertical profiles of trace atmospheric constituents. Here we describe two newly developed modes of the SASKTRAN radiative transfer model: a high spatial resolution mode, and a Monte Carlo mode. The high spatial resolution mode is a successive orders model capable of modelling the multiply scattered radiance when the atmosphere is not spherically symmetric; the Monte Carlo mode is intended for use as a highly accurate reference model. It is shown that the two models agree in a wide variety of solar conditions to within 0.2%. As an example case for both models, Odin-OSIRIS scans were simulated with the Monte Carlo model and retrieved using the high resolution model. A systematic bias of up to 4% in retrieved ozone number density between scans where the instrument is scanning up or scanning down was identified. It was found that calculating the multiply scattered diffuse field at five discrete solar zenith angles is sufficient to eliminate the bias for typical Odin-OSIRIS geometries.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

Comparison of regional chemistry-modelled NO2 tropospheric columns and profiles with TROPOMI observations and 4-azimuth MAX-DOAS measurements

2020 ◽  
Author(s):  
Vinod Kumar ◽  
Julia Remmers ◽  
Benedikt Steil ◽  
Astrid Kerkweg ◽  
Jos Lelieveld ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Climate Modeling ◽  
Temporal Correlation ◽  
Domain Model ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Regional Models ◽  
Vertical Column

&lt;p&gt;Regional chemistry-transport models typically simulate the physical and chemical state of the atmosphere at a high spatial resolution, e.g. of less than 7 km. At this relatively high spatial resolution, air quality and relevant processes within cities can be assessed to facilitate strategic mitigation planning. Comparison of regional models with satellite and ground-based observations helps validate the models and evaluate emission inventories, as well as satellite retrieval algorithms. For example, an underestimation of atmospheric trace gases (like often found for NO&lt;sub&gt;2&lt;/sub&gt;) by satellite observations can be improved by providing high-resolution input fields from regional models.&lt;/p&gt;&lt;p&gt;MECO(n), a global-to-regional chemistry climate modeling system, in which the finer resolved domains receive their initial and boundary conditions on-line from the next coarser model instance, was set-up with Germany as focus. 1-way nested MECO(3)&amp;#160; simulations were performed for May 2018 with spatial resolution up to ~2.2 km &amp;#215; 2.2 km in the finest domain. Model simulations accounting separately for both TNO MACC III and EDGAR 4.3.2 anthropogenic emissions are evaluated against TROPOMI observations. A diurnal factor was applied to road transport emissions to account for their temporal variation. For the comparison with TROPOMI data, we applied a novel method of online sampling of model fields along the satellite overpass by also accounting for the difference in local solar time across the swath width, which can be up to 90 minutes. Modified airmass factors in the TROPOMI data product, using the model calculated NO&lt;sub&gt;2&lt;/sub&gt; a priori profiles and taking into account averaging kernels, resulted in an improved agreement of the spatial pattern of NO&lt;sub&gt;2&lt;/sub&gt; vertical column density (VCD) between model and satellite.&lt;/p&gt;&lt;p&gt;NO&lt;sub&gt;2&lt;/sub&gt; VCDs over Mainz, calculated using model output at the finest model resolution, were compared against MAX-DOAS observations for the simulation period. Vertical profiles of NO&lt;sub&gt;2&lt;/sub&gt; were also retrieved in 4 azimuth directions around Mainz by profile inversion of MAX-DOAS measurements. The temporal (e.g. day-to-day and diurnal) variation of the 3-D NO&lt;sub&gt;2&lt;/sub&gt; field derived from the model was evaluated against the MAX-DOAS observations. For the cloud-free days, the model is able to reproduce the temporal development with satisfactory temporal correlation (slope=0.7, r=0.5) of the NO&lt;sub&gt;2&lt;/sub&gt; VCDs. For a direct comparison of measured slant column densities of NO&lt;sub&gt;2&lt;/sub&gt;, height-resolved 2-D box airmass factors were calculated using McArtim (Monte Carlo Atmospheric radiative transfer model) and applied to the modelled trace gas profiles along individual elevation angles of the measurements. This comparison procedure accounts for the complex dependency of the MAX-DOAS column densities on the 3D (vertical and horizontal) trace gas distribution in the measurement direction.&lt;/p&gt;

Down-scaling MODIS operational vegetation products with machine learning and fused gap-free high resolution reflectance data in Google Earth Engine

2020 ◽  
Author(s):  
Álvaro Moreno Martínez ◽  
Emma Izquierdo Verdiguier ◽  
Gustau Camps Valls ◽  
Marco Maneta ◽  
Jordi Muñoz Marí ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Learning Approaches ◽  
Area Index ◽  
Uncertainty Estimates ◽  
Google Earth Engine

&lt;p&gt;Among Essential Climate Variables (ECVs) for global climate observation, the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are the most widely used to study land vegetated surfaces. The NASA&amp;#8217;s Moderate&amp;#160; Resolution Imaging Spectro-radiometer (MODIS) is a key instrument aboard the Terra and Aqua platforms and allows to estimate both biophysical variables at coarse resolution (500 m) and global scales. The MODIS operational algorithm to retrieve LAI and FAPAR (MOD15/MYD15/MCD15) uses a physically-based radiative transfer model (RTM) to compute their estimates with corrected surface spectral information content. This algorithm has been heavily validated and compared with field measurements and other sensors but, so far, no equivalent products at high spatial resolution and continental or global scales are routinely produced.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, we introduce and validate a methodology to create a set of high spatial resolution LAI/FAPAR products by learning the MODIS RTM using advanced machine learning approaches and gap filled Landsat surface reflectances. The latter are smoothed and gap-filled by the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM). HISTARTFM has a great potential to improve the original Landsat reflectances by reducing their noise and recovering missing data due to cloud contamination. In addition, HISTARFM runs very fast in cloud computing platforms such as Google Earth Engine (GEE) and provides uncertainty estimates which can be propagated through the models. These estimates allow to compute numerical uncertainties beyond the typical and qualitative control information layers provided in operational products such as the MODIS LAI/FAPAR. The introduced high spatial resolution biophysical products here could be of interest to the users to achieve the needed levels of spatial detail to adequately monitor croplands and heterogeneously vegetated landscapes.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Three-dimensional radiative transfer effects on airborne, satellite and ground-based trace gas remote sensing

2020 ◽  
Author(s):  
Marc Schwaerzel ◽  
Claudia Emde ◽  
Dominik Brunner ◽  
Randulph Morales ◽  
Thomas Wagner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
High Spatial Resolution ◽  
Three Dimensional ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Vertical Column ◽  
Different Types ◽  
3D Effects

Abstract. Air mass factors (AMF) are used in passive trace gas remote sensing for converting slant column densities (SCD) to vertical column densities (VCD). AMFs are traditionally computed with 1D radiative transfer models assuming horizontally homogeneous conditions. However, when observations are made with high spatial resolution in a heterogeneous atmosphere or above a heterogeneous surface, 3D effects may not be negligible. To study the importance of 3D effects on AMFs for different types of trace gas remote sensing, we implemented 1D-layer and 3D-box AMFs into the Monte Carlo radiative transfer model (RTM) MYSTIC. The 3D-box AMF implementation is fully consistent with 1D-layer AMFs under horizontally homogeneous conditions and agrees very well (

scholarly journals The remote sensing of radiative forcing by light-absorbing particles (LAPs) in seasonal snow over northeastern China

2019 ◽  
Vol 19 (15) ◽  
pp. 9949-9968 ◽  
Author(s):  
Wei Pu ◽  
Jiecan Cui ◽  
Tenglong Shi ◽  
Xuelei Zhang ◽  
Cenlin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Atmospheric Correction ◽  
Northeastern China ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spatial Variance ◽  
Snow Albedo ◽  
Sky Conditions

Abstract. Light-absorbing particles (LAPs) deposited on snow can decrease snow albedo and affect climate through snow-albedo radiative forcing. In this study, we use MODIS observations combined with a snow-albedo model (SNICAR – Snow, Ice, and Aerosol Radiative) and a radiative transfer model (SBDART – Santa Barbara DISORT Atmospheric Radiative Transfer) to retrieve the instantaneous spectrally integrated radiative forcing at the surface by LAPs in snow (RFMODISLAPs) under clear-sky conditions at the time of MODIS Aqua overpass across northeastern China (NEC) in January–February from 2003 to 2017. RFMODISLAPs presents distinct spatial variability, with the minimum (22.3 W m−2) in western NEC and the maximum (64.6 W m−2) near industrial areas in central NEC. The regional mean RFMODISLAPs is ∼45.1±6.8 W m−2 in NEC. The positive (negative) uncertainties of retrieved RFMODISLAPs due to atmospheric correction range from 14 % to 57 % (−14 % to −47 %), and the uncertainty value basically decreases with the increased RFMODISLAPs. We attribute the variations of radiative forcing based on remote sensing and find that the spatial variance of RFMODISLAPs in NEC is 74.6 % due to LAPs and 21.2 % and 4.2 % due to snow grain size and solar zenith angle. Furthermore, based on multiple linear regression, the BC dry and wet deposition and snowfall could explain 84 % of the spatial variance of LAP contents, which confirms the reasonability of the spatial patterns of retrieved RFMODISLAPs in NEC. We validate RFMODISLAPs using in situ radiative forcing estimates. We find that the biases in RFMODISLAPs are negatively correlated with LAP concentrations and range from ∼5 % to ∼350 % in NEC.

scholarly journals High-resolution and Monte Carlo additions to the SASKTRAN radiative transfer model

2015 ◽  
Vol 8 (6) ◽  
pp. 2609-2623 ◽  
Author(s):  
D. J. Zawada ◽  
S. R. Dueck ◽  
L. A. Rieger ◽  
A. E. Bourassa ◽  
N. D. Lloyd ◽  
...  
Keyword(s):  
Monte Carlo ◽  
High Resolution ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Imaging System ◽  
Monte Carlo Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Systematic Bias

Abstract. The Optical Spectrograph and InfraRed Imaging System (OSIRIS) instrument on board the Odin spacecraft has been measuring limb-scattered radiance since 2001. The vertical radiance profiles measured as the instrument nods are inverted, with the aid of the SASKTRAN radiative transfer model, to obtain vertical profiles of trace atmospheric constituents. Here we describe two newly developed modes of the SASKTRAN radiative transfer model: a high-spatial-resolution mode and a Monte Carlo mode. The high-spatial-resolution mode is a successive-orders model capable of modelling the multiply scattered radiance when the atmosphere is not spherically symmetric; the Monte Carlo mode is intended for use as a highly accurate reference model. It is shown that the two models agree in a wide variety of solar conditions to within 0.2 %. As an example case for both models, Odin–OSIRIS scans were simulated with the Monte Carlo model and retrieved using the high-resolution model. A systematic bias of up to 4 % in retrieved ozone number density between scans where the instrument is scanning up or scanning down was identified. The bias is largest when the sun is near the horizon and the solar scattering angle is far from 90°. It was found that calculating the multiply scattered diffuse field at five discrete solar zenith angles is sufficient to eliminate the bias for typical Odin–OSIRIS geometries.

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