Reflectance Retrieval Research Articles

Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)

Abstract. In satellite remote sensing applications, enhancing the precision of level 2 (L2) algorithms relies heavily on the accurate estimation of the surface reflectance across the ultraviolet (UV) to visible (VIS) spectrum. However, the mutual dependence between the L2 algorithms and the surface reflectance retrieval poses challenges, necessitating an alternative approach. To address this issue, many satellite algorithms generate Lambertian-equivalent reflectivity (LER) products as a priori surface reflectance data; however, this often results in an underestimation of these data. This study is the first to assess the applicability of background surface reflectance (BSR), derived using a semi-empirical bidirectional reflectance distribution function (BRDF) model, in an operational environmental satellite algorithm. This study pioneered the application of the BRDF model to hyperspectral satellite data at 440 nm, aiming to provide more realistic preliminary surface reflectance data. In this study, the Geostationary Environment Monitoring Spectrometer (GEMS) data were used, and a comparative analysis of the GEMS BSR and GEMS LER retrieved in this study revealed an improvement in the relative root mean squared error (rRMSE) accuracy of 3 %. Additionally, a time series analysis across diverse land types indicated a greater stability exhibited by the BSR than by the LER. For further validation, the BSR was compared with other LER databases using ground-truth data, yielding superior simulation performance. These findings present a promising avenue for enhancing the accuracy of surface reflectance retrieval from hyperspectral satellite data, thereby advancing the practical applications of satellite remote sensing algorithms.

Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications.

An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters

Shallow water benthic habitats have been significantly degraded and seriously threatened by intensifying climate changes and anthropogenic stressors. Benthic reflectance (Rbλ) of optically shallow waters (OSWs) is a key parameter for remote sensing of benthic habitats’ composition and health status. The remote sensing reflectance (Rrsλ) just above the water surface contains the coupled spectral information of the water column and seabed in OSWs, which makes our effort challenging to separate the signals between seabed and water column properties. Due to too many unknowns of the water column and seabed optical properties, the current approaches are inaccurate or inadequate to retrieve benthic reflectance spectra over a large area from remotely sensed data. In this study, we proposed an enhanced large-scale benthic reflectance (LSBR) retrieval model from satellite data in OSWs with only input of Rrsλ. A new spectral library of benthic reflectance was built upon multi-source reflectance datasets obtained from several open-source field measurements. Then, the specific relationship between Rb443nm and Rb490nm was developed. By combining a semi-analytical benthic reflectance (SABR) model based on radiative transfer simulations and this relationship, water-column chlorophyll concentration (chl) was determined. Moreover, large-scale water depth was estimated by Rrsλ through satellite-derived bathymetry (SDB) technologies. Finally, benthic reflectance was retrieved with the inputs of the satellite-derived Rrsλ, chl, and water depth. In this way, the LSBR model fulfills the Rbλ retrieval with only input of satellite-derived Rrsλ. The new model was successfully applied to Sentinel-2/MSI data in the seagrass region of Xincun Bay and the coral region of Huaguang Reef and further verified by using field measurements and results of the along-track benthic reflectance (ATBR) retrieval model, which combined active lidar and passive high-resolution satellite images. Additionally, to gain insights into the long-term evolution of benthic communities, a typical application of the LSBR model for detecting benthic changes was conducted using eight years of Sentinel-2/MSI data in Heron Reef, depicting a similar consistency between the probable deterioration and restoration region and the sea surface temperature (SST) anomalies. The results demonstrated the robustness and accuracy of the LSBR model in retrieving benthic reflectance at large scales. The LSBR model should be helpful for further investigating benthic changes which can enhance the ability to monitor and assess the health status of submerged ecosystems in OSWs.

An applied framework to unlocking multi-angular UAV reflectance data: a case study for classification of plant parameters in maize (Zea mays)

AbstractOptical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectance dataset. We propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval. A standard (nadir) and a multi-angular, 10-band multispectral dataset was collected for maize using a UAV on two different days. Reflectance data was grouped by VZA and VAA (on average 2594 spectra/plot/day for the multi-angular data and 890 spectra/plot/day for nadir flights only, 13 spectra/plot/day for a standard orthomosaic), serving as predictor variables for leaf chlorophyll content (LCC), leaf area index (LAI), green leaf area index (GLAI), and nitrogen balanced index (NBI) classification. Results consistently showed higher accuracy using grouped VZA/VAA reflectance compared to the standard orthomosaic data. Pooling all reflectance values across viewing directions did not yield satisfactory results. Performing multiple flights to obtain a multi-angular dataset did not improve performance over a multi-angular dataset obtained from a single nadir flight, highlighting its sufficiency. Our openly shared code (https://github.com/ReneHeim/proj_on_uav) facilitates access to reflectance data from pre-defined VZA/VAA groups, benefiting cross-disciplinary and agriculture scientists in harnessing the potential of multi-angular datasets. Graphical abstract

Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates.

Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm

AbstractSatellite remote sensing enables the study of atmospheric aerosols at large spatial scales, with geostationary platforms making this possible at sub‐daily frequencies. High‐temporal‐resolution aerosol observations can be made from geostationary data by using robust numerical inversion methods such as the widely‐used optimal estimation (OE) theory. This is the case of the instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit (iAERUS‐GEO) algorithm, which successfully retrieves aerosol optical depth (AOD) maps from the Meteosat Second Generation weather satellite based on a simple implementation of the OE approach combined with the Levenberg–Marquardt method. However, the exact gain in inversion performances that can be obtained from the multiple and more advanced possibilities offered by OE is not well documented in the current literature. Against this background, this article presents the quantitative assessment of OE for the future improvement of the iAERUS‐GEO algorithm. To this end, we use a series of comprehensive experiments based on AOD maps retrieved by iAERUS‐GEO using different OE implementations, and ground‐based observations used as reference data. First, we assess the varying importance in the inversion process of satellite observations and a priori information according to the content of satellite aerosol information. Second, we quantify the gain of AOD estimation in log space versus linear space in terms of accuracy, AOD distribution and number of successful retrievals. Finally, we evaluate the accuracy improvement of simultaneous AOD and surface reflectance retrieval as a function of the regions covered by the Meteosat Earth's disk.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, Rrs(λ) (sr-1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rrs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rrs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla < 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla > 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kd(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in Rrs is further verified through forward-calculating chla from MODIS-retrieved and in situ Rrs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in Rrs, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics

Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (n = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires.

Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for neighboring pixels, resulting in surface-related biases in the retrieved surface and atmospheric parameters. In this work, we present a mathematical framework to more accurately retrieve surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. This framework retrieves the full spatial–spectral reflectance data cube in a computationally efficient manner and is to our knowledge the first approach capable of accurately finding this global solution for large imaging spectroscopy datasets. We implement this mathematical framework in an example proposed algorithm which we call spatially constrained optimal estimation (SCOE). We show that SCOE improves the overall surface reflectance retrieval error, reduces surface-related biases in the retrieved surface reflectance and atmospheric parameters, and is more robust to noise than a traditional pixel-by-pixel approach and an operational empirical line approach in simulated and experimental results. This reduction in surface reflectance error will impact science data products across application areas, making imaging spectroscopy algorithms more robust for global acquisitions.

The utilization of ghost reflections retrieved by seismic interferometry for layer‐specific characterization of the shallow subsurface

AbstractSeismic interferometry (SI) retrieves the Green function between two receiver locations using their recordings from a boundary of sources. When using sources and receivers only at the surface, the virtual‐source gathers retrieved by SI contain pseudo‐physical reflections as well as ghost (non‐physical) reflections. These ghost reflections are the results of the cross‐correlation or auto‐correlation (AC) of primary reflections from two different depth levels, and they contain information about the seismic properties of specific layers in the subsurface. We investigated the application of ghost reflections for layer‐specific characterization of the shallow subsurface using SI by AC. First, we showed the technique's potential using synthetic data for a subsurface model with a lateral change in velocity, a gradient in depth for velocity, a thickness change and a velocity change of the target layer. Then, we applied the technique to shallow subsurface field data. We also focused on improving the retrieval of ghost reflections by removing the free‐surface multiples and muting undesired events in active‐source gathers before applying SI. Our results demonstrate that the ghost reflections can be used advantageously to characterize the layer that causes them to appear in the results of SI. Consequently, they can also provide valuable information for imaging and monitoring shallow subsurface structures.

An application of internal multiple prediction and primary reflection retrieval to salt structures

Irregular salt bodies complicate the seismic wavefield propagation and cause multipathing, leading to intricate internal multiples that cannot be easily suppressed during data processing. To investigate the performance of internal multiple removal in salt bodies, we compare two types of Marchenko-based internal multiple removal strategies. One focuses on internal multiple prediction (IMP) based on Marchenko redatuming and seismic interferometry. With the retrieved upgoing and downgoing Green’s functions, internal multiples can be reconstructed using convolutional interferometry. The second method emphasizes primary reflection retrieval (PRR) by capturing primary reflections without adaptive subtraction. It is derived from a modified method based on the Gel’fand-Levitan-Marchenko equation. Testing with the Pluto salt model, we find that for the IMP the critical point is obtaining the purely scattering components of the upgoing and downgoing Green’s functions from the complicated wavefield. Our complex salt model indicates that the predicted internal multiples’ adaptive subtraction also introduces an extra challenge for attenuation quality. We develop a layer-by-layer adaptive subtraction scheme for the IMP to obtain primary reflections. The constant-time truncation operator in the time domain makes the PRR independent of additional model information, which can exclude internal multiples from the primary reflections. However, selecting an appropriate smoothing window for truncation requires further investigation. We evaluate the weaknesses of these methods and enhance each method individually. Our numerical results indicate that these improved schemes can correctly handle internal multiples in salt structure scenarios. In the addition, we study the robustness of the IMP when an erroneous background velocity model is used to estimate the direct arrival and the effects of sampling on the virtual receiver/source lines in IMP.

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery.

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon () estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.

Beyond Correlations: Deep Learning for Seismic Interferometry.

Passive seismic interferometry is a vastly generalized blind deconvolution question, where different paths through the Earth correspond to different channels called Green's functions; the sources are completely incoherent and not shared by the channels, and the question is to estimate paths (channels) that are not present in the dataset. SI, turning noise to signal, has numerous applications, from monitoring industrial activities to crustal structure investigation. No standard method of signal processing will solve SI. Instead, domain scientists resort to a simple cross-correlation operation, a.k.a. correlogram, which can retrieve the Green's function directly, but only under restrictive assumptions of ergodicity (energy equipartitioning) of the random process generating the seismic source. However, in practice, correlograms are not equal to the empirical Green's function, because these assumptions are generally far from being satisfied in realistic situations. In the framework of supervised learning, we propose to train deep neural networks (NNs) to overcome two limitations of correlation-based SI: the temporal limitation of passive recordings and the spatial limitation of the random source distribution. Deep NNs are trained to implicitly find the relationship between the empirical Green's functions and the correlograms and then used to extract the correct Green's functions from ambient noise. The input of the network is correlograms (a virtual shot gather), and the desired output is the empirical Green's function (the active shot gather). The NN can often retrieve Green's functions from 5-min passive recordings with acceptable accuracy in our synthetic example. Although an exact estimation of the source locations may not be necessary, a prior knowledge of the source directionality (through a preliminary beamforming step) is helpful when training the NN to mitigate the challenges associated with inhomogeneous source distributions (directional wave fields). In this work, all the numerical examples are based on the retrieval of P-wave reflections in the exploration scale and are conducted on synthetic data. We use a modified ResNet in our numerical experiments.

Evaluation of surface reflectance retrieval over diverse surface types using SREM algorithm in varied aerosol conditions for coarse to medium resolution data from multiple spaceborne sensors

ABSTRACT Surface reflectance (SR) is one of the most important components for deriving different biophysical parameters from remotely sensed data. For accurate retrieval of SR, it is essential to accurately estimate the degree of atmospheric alteration to the target signals reaching at-sensor. Simplified and robust SR estimation method (SREM) is one such approximate estimation method that could demonstrate the retrieval of SR from top-of-atmosphere (ToA) signals, without using any precalculated LUTs or aerosol information. The present study attempts to assess the accuracy of the SREM algorithm over varied Indian land surfaces using data from Landsat 8, Sentinel 2, AWiFS and LISS III. The results indicate that while SREM performs quite well for Sentinel 2 over most land surface types, the performance is good for Landsat 8 as well except over water and in coastal aerosol band. SREM retrievals also show high correlation for AWiFS and LISS III but the estimated values are much lower than level 2 SR due to smaller ToA reflectance values. Furthermore, while aerosol load does impact the performance adversely, it is least impactful for Sentinel 2 and LISS III datasets and most over Landsat 8, particularly over water. Additionally, while AWiFS and LISS III SR retrievals are underestimated at all spectral bands, retrieval is slightly underestimated from green to SWIR band in case of Sentinel 2 and SWIR band in case of Landsat 8. Overall, the findings demonstrate that while SREM will need to be modified to adapt to LISS III and AWiFS retrievals, it can, nevertheless, be readily used as a simplified method for quick and approximate estimation of SR over the Indian region using data from Landsat 8 and Sentinel 2.

Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation

This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 sensor, the study performed a cross-calibration using a ground truth measurement collected with the Analytical Spectral Device (ASD) as the reference point. Additionally, a spectral band adjustment factor (SBAF) was applied across the calibrated Arable surface reflectance to compare it against the orbital sensor. An automated library aided in calculating SBAF for the days with unavailable hyperspectral data. The study found that the Arable Mark 2 sensor can provide accurate surface reflectance data that can be used for orbital sensor validation. The Arable sensor was successfully calibrated against the ASD FieldSpec with an average difference of less than 1/10 reflectance unit (reflectance unit = 0.01) for the blue, green, yellow, and red bands. The red-edge and NIR-1 bands showed an average difference of less than 1/2 reflectance units, while the NIR-2 band had an average difference of less than 1/10 reflectance unit of calibration accuracy. The calibrated Arable surface reflectance data was then compared against orbital sensor surface reflectance data, and the results showed good agreement between the two datasets. The study concludes that the low-cost and automated nature of the Arable Mark 2 sensor makes it a promising tool for surface reflectance retrieval and orbital sensor validation.

Distribution Retrieval of Both Depth and Reflectivity in 3-D Objects via Using Modulated Single Pixel Imaging

3-D objects are illustrated in terms of depth and reflectivity, and these parameters are usually detected by different devices. A modulated single pixel imaging (MSI) method is introduced for simultaneous detections of both depth and reflectivity in 3-D targets. Two modulations for spatial resolution and noise immunity are realized by operations of a digital-micro-mirror device (DMD) and emitted laser amplitude. Laser intensities are modulated at a high frequency up to 100 MHz and demodulated for their amplitude and phase shift. Modulated laser is reflected by the imaged object and collected by a single photodetector. The imaging model is termed as a minimization problem and solved by a proposed modified log-barrier method at optimal iteration steps. The proposed method yields faster convergence and fewer steps of about a half of that used in the typical line-searching method. Two mirrors were positioned in parallel to fabricate a 3-D phantom. The distributions of both depth and surface reflectivity of these mirrors are well reconstructed. Both simulated and experiment data verified the efficacy of proposed MSI on 3-D objects.

Assessing the Cloud Adjacency Effect on Retrieval of the Ground Surface Reflectance from MODIS Satellite Data for the Baikal Region

The cloud adjacency effect on surface reflectance retrievals for the region of the Russian Federation with coordinates 51–54∘ N, 103–109∘ E including the southern part of Lake Baikal for the period of 1–23 July 2021 is assessed in this paper. The method is based on the computer program for statistical simulation of radiative transfer in the atmosphere with the stochastic cloud field including a deterministic gap of a given radius. The results of this program are then used in the interpolation formula. Masks of cloudless pixels, for which the cloud adjacency effect (CAE) changes the ground surface reflectance by more than 0.005, are constructed. The analysis of the resulting CAE radii shows that the average radius is 13.7 km for MODIS band 8, 11.2 km for band 3, 8.4 km for band 4, 7.2 km for band 1, and 7 km for band 2. For the considered MODIS images and bands, the pixels with strong CAE make up from 2.8 to 100% of the total number of cloudless pixels. The correlation coefficients between the initial data and the CAE radius suggest that the cloud optical depth, cloud cover index, and ground surface reflectance exert the major influence on the considered images. A simplified approximation equation for the CAE radius as a function of the cloud optical depth, cloud cover index, and surface reflectance is derived. The analysis of the approximation shows that for the considered images, the CAE radius decreases nearly linearly with wavelength for low reflective surfaces. However, for high reflective surfaces, its wavelength dependence is nonlinear.

Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

Numerical Simulation of a Giant-Hail-Bearing Mediterranean Supercell in the Adriatic Sea

On 10 July 2019, a giant hail-bearing supercell hit the Adriatic coast of central Italy. Hailstones with a maximum diameter of 14 cm were reported in the city of Pescara between 10:00 and 11:00 UTC. In this work, the main synoptic and mesoscale features, responsible for the triggering and the development of the supercell, are analyzed using the WRF model. The intrusion of Bora wind over the northern and central Adriatic was relevant for two reasons: on the one side, the arrival of low-level cold air produced an uplift of the pre-existing warm air and favored the triggering of convection; on the other side, the strong vertical wind shear, also due to the presence of intense upper-level southwesterlies, created conditions favorable to the formation of the supercell. The predictability of the event is also discussed, comparing simulations starting at different initial times and forced with GFS and IFS forecasts. The model results show that the runs initialized at earlier times reproduced more accurately the track and the time evolution of the supercell. The HAILCAST module of WRF was also used to simulate hailstorm characteristics, such as the average hailstone diameter. WRF-HAILCAST simulations proved to be in fair agreement with the radar reflectivity retrievals and with local reports.

Unified Topographic and Atmospheric Correction for Remote Imaging Spectroscopy

Models linking surface characteristics within incident solar radiation are inexorably dependent on the topography of the given region. To date, however, most operational surface reflectance retrievals treat this dependence by assuming a flat terrain, leading to significant deviations in the estimated reflectance. Here, we demonstrate that incorporating dynamic topography directly into the joint surface and atmospheric model during retrievals has several advantages. First, it allows for a more complete physical accounting of downwelling illumination, providing more accurate estimates of the absolute magnitude of reflectance. Second, it facilitates a superior resolution of the atmospheric state, most notably due to the confounding influence of atmospheric aerosols and unresolved topographic effects. Our methodology utilizes a practical, high-fidelity, model-driven approach to separate out diffuse and direct irradiation and account for topographic effects during the joint inversion of atmosphere and surface properties. We achieve this by enhancing the atmosphere/surface inversion to account for the radiative transfer effects of surface slope. We further demonstrate how uncertainties in topographic features can be quantified and leveraged within our formulation for a more realistic posterior uncertainty estimates. Our results demonstrate that the inclusion of topographic effects into the retrieval model reduces errors in the reflectance of an only moderately rugged terrain by more than 15%, and that a post hoc accounting of topography cannot achieve these same results.