Comparative Study on Atmospheric Correction Radiative Transfer Model of Common Remote Sensing Images

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.

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Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0017 ◽

2014 ◽

Vol 18 (2) ◽

pp. 35-45 ◽

Cited By ~ 2

Author(s):

Michał T. Chiliński ◽

Marek Ostrowski

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Vegetation Index ◽

Vegetation Mapping ◽

Radiative Transfer Model ◽

Unmanned Aerial Systems ◽

Transfer Model ◽

Digital Cameras ◽

Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

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Effect of OH emission on the temperature and wind measurements derived from limb-viewing observations of the 1.27 µm O<sub>2</sub> dayglow

Atmospheric Measurement Techniques ◽

10.5194/amt-13-1817-2020 ◽

2020 ◽

Vol 13 (4) ◽

pp. 1817-1824

Author(s):

Kuijun Wu ◽

Weiwei He ◽

Yutao Feng ◽

Yuanhui Xiong ◽

Faquan Li

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Spectral Region ◽

Emission Lines ◽

Radiative Transfer Model ◽

Transfer Model ◽

Altitude Range ◽

Band Emission ◽

Near Space ◽

Wind Measurements

Abstract. The O2(a1Δg) emission near 1.27 µm is well-suited for remote sensing of global wind and temperature in near-space by limb-viewing observations to its bright signal and extended altitude coverage. However, vibrational–rotational emission lines of the OH dayglow produced by the hydrogen–ozone reaction (H+O3→OH•+O2) overlap the infrared atmospheric band emission (a1Δg→X3Σg) of O2. The main goal of this paper is to discuss the effect of OH emission on the wind and temperature measurements derived from the 1.27 µm O2 dayglow limb-viewing observations. The O2 dayglow and OH dayglow spectrum over the spectral region and altitude range of interest is calculated by using the line-by-line radiative transfer model and the most recent photochemical model. The method of four-point sampling of the interferogram and sample results of measurement simulations are provided for both O2 dayglow and OH dayglow. It is apparent from the simulations that the presence of OH dayglow as an interfering species decreases the wind and temperature accuracy at all altitudes, but this effect can be reduced considerably by improving OH dayglow knowledge.

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

Remote Sensing ◽

10.3390/rs12172752 ◽

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.

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Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model

Remote Sensing ◽

10.3390/rs11060671 ◽

2019 ◽

Vol 11 (6) ◽

pp. 671 ◽

Cited By ~ 19

Author(s):

Roshanak Darvishzadeh ◽

Tiejun Wang ◽

Andrew Skidmore ◽

Anton Vrieling ◽

Brian O’Connor ◽

...

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Leaf Area Index ◽

Plant Traits ◽

Remote Sensing Data ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Field Campaigns ◽

Sentinel 2

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.

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A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

Machine Learning and Knowledge Extraction ◽

10.3390/make1030052 ◽

2019 ◽

Vol 1 (3) ◽

pp. 904-927 ◽

Cited By ~ 1

Author(s):

Usman A. Zahidi ◽

Ayan Chatterjee ◽

Peter W. T. Yuen

Keyword(s):

Radiative Transfer ◽

Hyperspectral Image ◽

Mathematical Formulation ◽

False Negative ◽

Atmospheric Correction ◽

Ground Truth ◽

Radiative Transfer Model ◽

Surface Model ◽

Transfer Model ◽

Regression Learning

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.

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Radiometric Microwave Indices for Remote Sensing of Land Surfaces

Remote Sensing ◽

10.3390/rs10121859 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1859 ◽

Cited By ~ 4

Author(s):

Simonetta Paloscia ◽

Paolo Pampaloni ◽

Emanuele Santi

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Snow Water Equivalent ◽

Hydrological Cycle ◽

Microwave Remote Sensing ◽

Dense Medium ◽

Single Frequency ◽

Radiative Transfer Model ◽

Transfer Model ◽

Land Surfaces

This work presents an overview of the potential of microwave indices obtained from multi-frequency/polarization radiometry in detecting the characteristics of land surfaces, in particular soil covered by vegetation or snow and agricultural bare soils. Experimental results obtained with ground-based radiometers on different types of natural surfaces by the Microwave Remote Sensing Group of IFAC-CNR starting from ‘80s, are summarized and interpreted by means of theoretical models. It has been pointed out that, with respect to single frequency/polarization observations, microwave indices revealed a higher sensitivity to some significant parameters, which characterize the hydrological cycle, namely: soil moisture, vegetation biomass and snow depth or snow water equivalent. Electromagnetic models have then been used for simulating brightness temperature and microwave indices from land surfaces. As per vegetation covered soils, the well-known tau-omega (τ-ω) model based on the radiative transfer theory has been used, whereas terrestrial snow cover has been simulated using a multi-layer dense-medium radiative transfer model (DMRT). On the basis of these results, operational inversion algorithms for the retrieval of those hydrological quantities have been successfully implemented using multi-channel data from the microwave radiometric sensors operating from satellite.

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A fast and accurate radiative transfer model for aerosol remote sensing

Journal of Quantitative Spectroscopy and Radiative Transfer ◽

10.1016/j.jqsrt.2020.107270 ◽

2020 ◽

Vol 256 ◽

pp. 107270

Author(s):

Linlu Mei ◽

Vladimir Rozanov ◽

John P. Burrows

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Radiative Transfer Model ◽

Transfer Model ◽

Aerosol Remote Sensing

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Modelling of underwater light fields in turbid and eutrophic waters: application and validation with experimental data

Ocean Science ◽

10.5194/os-11-33-2015 ◽

2015 ◽

Vol 11 (1) ◽

pp. 33-52 ◽

Cited By ~ 10

Author(s):

B. Sundarabalan ◽

P. Shanmugam

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Water Column ◽

Radiative Transfer Model ◽

Light Fields ◽

Transfer Model ◽

Inhomogeneous Layer ◽

Layer By Layer ◽

Direct Part ◽

Underwater Light

Abstract. A reliable radiative transfer (RT) model is an essential and indispensable tool for understanding the radiative transfer processes in homogenous and layered waters, analyzing measurements made by radiance sensors and developing remote-sensing algorithms to derive meaningful physical quantities and biogeochemical variables in turbid and productive coastal waters. Existing radiative transfer models have been designed to be applicable to either homogenous waters or inhomogeneous waters. To overcome such constraints associated with these models, this study presents a radiative transfer model that treats a homogenous layer as a diffuse part and an inhomogeneous layer as a direct part in the water column and combines these two parts appropriately in order to generate more reliable underwater light-field data such as upwelling radiance (Lu), downwelling irradiance (Ed) and upwelling irradiance (Eu). The diffuse model assumes the inherent optical properties (IOPs) to be vertically continuous and the light fields to exponentially decrease with depth, whereas the direct part considers the water column to be vertically inhomogeneous (layer-by-layer phenomena) with the vertically varying phase function. The surface and bottom boundary conditions, source function due to chlorophyll and solar incident geometry are also included in the present RT model. The performance of this model is assessed in a variety of waters (clear, turbid and eutrophic) using the measured radiometric data. The present model shows an advantage in terms of producing accurate Lu, Ed and Eu profiles (in spatial domain) in different waters determined by both homogenous and inhomogeneous conditions. The feasibility of predicting these underwater light fields based on the remotely estimated IOP data is also examined using the present RT model. For this application, vertical profiles of the water constituents and IOPs are estimated by empirical models based on our in situ data. The present RT model generates Lu, Ed and Eu spectra closely consistent with the measured data. These results lead to a conclusion that the present RT model is a viable alternative to existing RT models and has an important implication for remote sensing of optically complex waters.

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