Radiation and Optics in the Atmosphere

Platt, U.; Pfeilsticker, K.; Vollmer, Michael

doi:10.1007/978-0-387-30420-5_19

Cited by 12 publications

(3 citation statements)

References 156 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Atmospheric scattering contributes to over 90% of the total measured radiation [68]. Atmospheric correction programs are capable of converting the Digital Number (DN) of surface reflectance values in Sentinel-2 images to actual surface reflectance, mitigating a portion of the atmospheric effects [69,70]. The Sen2Cor algorithm necessitates the presence of enough dark pixels in the scene, allowing the assumption of constant Bottom of the Atmosphere (BOA) reflectance for different bands.…”

Section: Atmospheric Correctionmentioning

confidence: 99%

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Xie,

Chen,

Zhang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R2 accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies.

show abstract

Section: Atmospheric Correctionmentioning

confidence: 99%

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Xie,

Chen,

Zhang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The following values of parameters were used: A i : 0.0305, 0.0363, 0,1303, 0.211, 0.350; k i : 0.800, 0.765, 0.830, 0.802, 0.814; β i : 0.112, 0.134, 0.093, 0.111, 0.1035; and w i : 54, 54, 2.0, 1.1, 0.35, where all entries are given for windows I,…,V, respectively [8]. Further, the atmospheric scattering coefficient has form [11] b(λ) = σ RS (λ)N air = 4.02 × 10 −28 λ 4+x N air ( where σ RS (λ) is the Rayleigh scattering cross-section, x = 0.04 for λ > 0.05 µm, N air = 2.4 × 10 19 molec cm −3 , at temperature 20 • C and pressure 1 atm. In SI units b(λ) = 100σ RS (λ)N air m −1 .…”

mentioning

confidence: 99%

Integrating optical turbulence into Beer’s law

Ata¹,

Korotkova²

2022

Laser Phys. Lett.

View full text Add to dashboard Cite

A new quantity is introduced for characterization of the on-axis attenuation of a finite laser beam caused by a turbulent medium. It then can be incorporated into the Beer’s law accounting for the extended medium’s absorption and scattering, and hence provides a simple tool for comparison among the effects of turbulence, absorption and scattering on light. The application of the new attenuation coefficient to atmospheric and oceanic turbulence is illustrated.

show abstract

“…For the Rayleigh scattering cross section, we implement equation 7.37 from Goody and Yung (1989) and equation 19.5 from Platt et al (2007), see Eq. A20.…”

mentioning

confidence: 99%

NO<sub>2</sub> vertical profiles and column densities from MAX-DOAS measurements in Mexico City

Friedrich¹,

Cárdenas²,

Stremme³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract. We present a new numerical code, Mexican Maxdoas Fit (MMF), developed to retrieve profiles of different trace gases from the network of MAX-DOAS instruments operated in Mexico City. MMF uses differential slant column densities (dSCDs) retrieved with the QDOAS (Danckaert et al., 2013) software. The retrieval is comprised of two steps, an aerosol retrieval and the trace gas retrieval that uses the retrieved aerosol profile in the forward model for the trace gas. For forward model simulations, VLIDORT is used (e.g. Spurr et al., 2001; Spurr, 2006, 2013). Both steps use constrained least square fitting, but the aerosol retrieval uses Tikhonov regularization and the trace gas retrieval optimal estimation. Aerosol optical depth and scattering properties from the AERONET database, averaged ceilometer data, WRF-Chem model data as well as temperature and pressure sounding data are used for different steps in the retrieval chain. The MMF code was applied to retrieve NO2 profiles with two degrees of freedom (DOFs = 2) from spectra of the MAX-DOAS instrument located at the UNAM campus. We describe the full error analysis of the retrievals and include a sensitivity exercise to quantify the contribution of the uncertainties in the aerosol extinction profiles to the total error. A dataset comprised of measurements from January 2015 to July 2016 was processed and the results compared to independent surface measurements. We concentrate on the analysis of 4 single days and additionally present diurnal and annual variabilities from averaging the 1.5 years of data. Even though the total error is considerably large (depending on the exact counting 14–20 %) this work still provides new and relevant information about NO2 in the boundary layer of Mexico City.

show abstract

Radiation and Optics in the Atmosphere

Cited by 12 publications

References 156 publications

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Integrating optical turbulence into Beer’s law

NO<sub>2</sub> vertical profiles and column densities from MAX-DOAS measurements in Mexico City

Contact Info

Product

Resources

About

Radiation and Optics in the Atmosphere

Cited by 12 publications

References 156 publications

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

Integrating optical turbulence into Beer’s law

NO&lt;sub&gt;2&lt;/sub&gt; vertical profiles and column densities from MAX-DOAS measurements in Mexico City

Contact Info

Product

Resources

About

NO<sub>2</sub> vertical profiles and column densities from MAX-DOAS measurements in Mexico City