Remote Sensing

Research

20 pages, 5481 KiB

Open AccessArticle

Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

by Christopher O. Ilori, Nima Pahlevan and Anders Knudby

Remote Sens. 2019, 11(4), 469; https://doi.org/10.3390/rs11040469 - 25 Feb 2019

Cited by 94 | Viewed by 10909

Abstract

Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as [...] Read more.

Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (R_rs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr⁻¹ for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

18 pages, 6979 KiB

Open AccessArticle

Fast Atmospheric Correction Method for Hyperspectral Data

by Leonid V. Katkovsky, Anton O. Martinov, Volha A. Siliuk, Dimitry A. Ivanov and Alexander A. Kokhanovsky

Remote Sens. 2018, 10(11), 1698; https://doi.org/10.3390/rs10111698 - 28 Oct 2018

Cited by 26 | Viewed by 7830

Abstract

Atmospheric correction is a necessary step in processing data recorded by spaceborne sensors for cloudless atmosphere, primarily in the visible and near-IR spectral range. In this paper we present a fast and sufficiently accurate method of atmospheric correction based on the analytical solutions [...] Read more.

Atmospheric correction is a necessary step in processing data recorded by spaceborne sensors for cloudless atmosphere, primarily in the visible and near-IR spectral range. In this paper we present a fast and sufficiently accurate method of atmospheric correction based on the analytical solutions of radiative transfer equation (RTE). The proposed analytical equations can be used to calculate the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image, taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface reflectance, and also assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or within the whole frame), the spectral reflectance at the Earth’s surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation using non-linear least squares is based on the analytical solution of the direct transfer problem. This enables fast solution of the inverse problem in a very short calculation time. Testing of the method has been performed using the synthetic outgoing radiation spectra at the top of atmosphere, obtained from the LibRadTran code. In addition, we have used the spectra measured by the Hyperion. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate data obtained by our method, a comparative analysis with ground-based measurements of the Radiometric Calibration Network (RadCalNet) was carried out. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Figure 1

29 pages, 9629 KiB

Open AccessArticle

Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations

by Shun Bi, Yunmei Li, Qiao Wang, Heng Lyu, Ge Liu, Zhubin Zheng, Chenggong Du, Meng Mu, Jie Xu, Shaohua Lei and Song Miao

Remote Sens. 2018, 10(7), 1002; https://doi.org/10.3390/rs10071002 - 24 Jun 2018

Cited by 52 | Viewed by 6731

Abstract

Atmospheric correction is an essential prerequisite for obtaining accurate inland water color information. An inland water atmospheric correction algorithm, ACbTC (Atmospheric Correction based on Turbidity Classification), was proposed in this study by using OLCI (Ocean and Land Color Instrument) and SLSTR (Sea and [...] Read more.

Atmospheric correction is an essential prerequisite for obtaining accurate inland water color information. An inland water atmospheric correction algorithm, ACbTC (Atmospheric Correction based on Turbidity Classification), was proposed in this study by using OLCI (Ocean and Land Color Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) synergistic observations for the first time. This method includes two main steps: (1) water turbidity classification by the GRA index (GRAdient of the spectrum index); and (2) atmospheric correction by synergistic use of OLCI and SLSTR images. The algorithm was validated with 72 in situ sampling sites in Lake Erhai, Lake Hongze, and Lake Taihu, and compared with other atmospheric correction methods, i.e., C2RCC (Case 2 Regional Coast Colour processor), MUMM (Management Unit of the North Seas Mathematical Models), FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes), POLYMER (POLYnomial based algorithm applied to MERIS), and BPAC (Bright Pixel Atmospheric Correction). The results show that (1) the GRA index performed better than the proposed turbidity classification indices, i.e., the Diff (spectral difference index) and the Tind (turbid index), in inland lakes by using the reflectance peak at 1020 nm in clean water; (2) the synergistic use of OLCI and SLSTR performed feasibly for atmospheric correction, and the ACbTC algorithm achieved full-band average values of the mean absolute percentage error (MAPE) = 29.55%, mean relative percentage error (MRPE) = 13.98%, and the root mean square of error (RMSE) = 0.0039 sr⁻¹, which were more reliable than C2RCC, MUMM, FLAASH, POLYMER, and BPAC; and (3) the synergistic use of the 17th band (865 nm) on OLCI and the 5th band (1613 nm) on SLSTR are suitable for clean inland lakes, while both the 5th band (1613 nm) and 6th band (2250 nm) on SLSTR are advisable for the turbidity. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

20 pages, 10117 KiB

Open AccessCommunication

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

by Jong-Min Yeom, Jonghan Ko, Jisoo Hwang, Chang-Suk Lee, Chul-Uong Choi and Seungtaek Jeong

Remote Sens. 2018, 10(5), 697; https://doi.org/10.3390/rs10050697 - 2 May 2018

Cited by 16 | Viewed by 5327

Abstract

Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on [...] Read more.

Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec^® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II^® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R², values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

15 pages, 17357 KiB

Open AccessArticle

A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects

by Ronald Scheirer, Adam Dybbroe and Martin Raspaud

Remote Sens. 2018, 10(4), 560; https://doi.org/10.3390/rs10040560 - 5 Apr 2018

Cited by 1 | Viewed by 7075

Abstract

Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster [...] Read more.

Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Figure 1

18 pages, 4578 KiB

Open AccessArticle

Atmospheric Correction Inter-Comparison Exercise

by Georgia Doxani, Eric Vermote, Jean-Claude Roger, Ferran Gascon, Stefan Adriaensen, David Frantz, Olivier Hagolle, André Hollstein, Grit Kirches, Fuqin Li, Jérôme Louis, Antoine Mangin, Nima Pahlevan, Bringfried Pflug and Quinten Vanhellemont

Remote Sens. 2018, 10(2), 352; https://doi.org/10.3390/rs10020352 - 24 Feb 2018

Cited by 183 | Viewed by 16878

Abstract

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional [...] Read more.

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

2924 KiB

Open AccessArticle

Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA

by Bastien Rouquié, Olivier Hagolle, François-Marie Bréon, Olivier Boucher, Camille Desjardins and Samuel Rémy

Remote Sens. 2017, 9(12), 1230; https://doi.org/10.3390/rs9121230 - 28 Nov 2017

Cited by 25 | Viewed by 6745

Abstract

The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform [...] Read more.

The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

984 KiB

Open AccessArticle

The Aerosol Index and Land Cover Class Based Atmospheric Correction Aerosol Optical Depth Time Series 1982–2014 for the SMAC Algorithm

by Emmihenna Jääskeläinen, Terhikki Manninen, Johanna Tamminen and Marko Laine

Remote Sens. 2017, 9(11), 1095; https://doi.org/10.3390/rs9111095 - 27 Oct 2017

Cited by 9 | Viewed by 4419

Abstract

Atmospheric effects, especially aerosols, are a significant source of uncertainty for optical remote sensing of surface parameters, such as albedo. Also to achieve a homogeneous surface albedo time series, the atmospheric correction has to be homogeneous. However, a global homogeneous aerosol optical depth [...] Read more.

Atmospheric effects, especially aerosols, are a significant source of uncertainty for optical remote sensing of surface parameters, such as albedo. Also to achieve a homogeneous surface albedo time series, the atmospheric correction has to be homogeneous. However, a global homogeneous aerosol optical depth (AOD) time series covering several decades did not previously exist. Therefore, we have constructed an AOD time series 1982–2014 using aerosol index (AI) data from the satellite measurements of the Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI), together with the Solar zenith angle and land use classification data. It is used as input for the Simplified Method for Atmospheric Correction (SMAC) algorithm when processing the surface albedo time series CLARA-A2 SAL (the Surface ALbedo from the Satellite Application Facility on Climate Monitoring project cLoud, Albedo and RAdiation data record, the second release). The surface reflectance simulations using the SMAC algorithm for different sets of satellite-based AOD data show that the aerosol-effect correction using the constructed TOMS/OMI based AOD data is comparable to using other satellite-based AOD data available for a shorter time range. Moreover, using the constructed TOMS/OMI based AOD as input for the atmospheric correction typically produces surface reflectance [-20]values closer to those obtained using in situ AOD values than when using other satellite-based AOD data. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

5358 KiB

Open AccessArticle

Removal of Thin Cirrus Scattering Effects in Landsat 8 OLI Images Using the Cirrus Detecting Channel

by Bo-Cai Gao and Rong-Rong Li

Remote Sens. 2017, 9(8), 834; https://doi.org/10.3390/rs9080834 - 12 Aug 2017

Cited by 36 | Viewed by 9928

Abstract

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a [...] Read more.

Thin cirrus clouds frequently contaminate images acquired with either Landsat 7 ETM+ or the earlier generation of Landsat series satellite instruments. The situation has changed since the launch of the Landsat 8 Operational Land Imager (OLI) into space in 2013. OLI implemented a cirrus detecting channel (Band 9) centered within a strong atmospheric water vapor absorption band near 1.375 μm with a width of 30 nm. The specifications for this channel were the same as those specified for the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) in the early 1990s. The OLI Band 9 has been proven to be very effective in detecting and masking thin cirrus-contaminated pixels at the high spatial resolution of 30 m. However, this channel has not yet been routinely used for the correction of thin cirrus effects in other OLI band images. In this article, we describe an empirical technique for the removal of thin cirrus scattering effects in OLI visible near infrared (IR) and shortwave IR (SWIR) spectral regions. We present results from applications of the technique to three OLI data sets. We also discuss issues associated with parallax anomalies in OLI data. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

5516 KiB

Open AccessArticle

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

by Driss Bru, Bertrand Lubac, Cassandra Normandin, Arthur Robinet, Michel Leconte, Olivier Hagolle, Nadège Martiny and Cédric Jamet

Remote Sens. 2017, 9(8), 814; https://doi.org/10.3390/rs9080814 - 8 Aug 2017

Cited by 8 | Viewed by 5782

Abstract

Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) [...] Read more.

Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) suitable for the spectral characteristics of high spatial resolution sensors (HRS) as well as the high range of atmospheric and marine optical properties. The objective of the present study is to develop and demonstrate the potential of a ground-based AC approach adaptable to any HRS for regional monitoring and security of littoral systems. The in Situ-based Atmospheric CORrection (SACOR) algorithm is based on simulations provided by a Successive Order of Scattering code (SOS), which is constrained by a simple regional aerosol particle model (RAM). This RAM is defined from the mixture of a standard tropospheric and maritime aerosol type. The RAM is derived from the following two processes. The first process involved the analysis of a 6-year data set composed of aerosol optical and microphysical properties acquired through the ground-based PHOTONS/AERONET network located at Arcachon (France). The second process was related to aerosol climatology using the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Results show that aerosols have a bimodal particle size distribution regardless of the season and are mainly represented by a mixed coastal continental type. Furthermore, the results indicate that aerosols originate from both the Atlantic Ocean (53.6%) and Continental Europe (46.4%). Based on these results, absorbing biomass burning, urban-industrial and desert dust particles have not been considered although they represent on average 19% of the occurrences. This represents the main current limitation of the RAM. An assessment of the performances of SACOR is then performed by inter-comparing the water-leaving reflectance (

ρ_{w}

) retrievals with three different AC methods (ACOLITE, MACCS and 6SV using three different standard aerosol types) using match-ups (N = 8) composed of Landsat-8/Operational Land Imager (OLI) scenes and field radiometric measurements. Results indicate consistency with the SWIR-based ACOLITE method, which shows the best performance, except in the green channel where SACOR matches well with the in-situ data (relative error of 7%). In conclusion, the study demonstrates the high potential of the SACOR approach for the retrieval of

ρ_{w}

. In the future, the method could be improved by using an adaptive aerosol model, which may select the most relevant local aerosol model following the origin of the atmospheric air mass, and could be applied to the latest HRS (Sentinel-2/MSI, SPOT6-7, Pleiades 1A-1B). Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

32407 KiB

Open AccessArticle

Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

by Eija Parmes, Yrjö Rauste, Matthieu Molinier, Kaj Andersson and Lauri Seitsonen

Remote Sens. 2017, 9(8), 806; https://doi.org/10.3390/rs9080806 - 5 Aug 2017

Cited by 20 | Viewed by 10106

Abstract

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite) [...] Read more.

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite) satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud). This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

7613 KiB

Open AccessArticle

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

by Da-Bin Ji, Jian-Cheng Shi, Husi Letu, Tian-Xing Wang and Tian-Jie Zhao

Remote Sens. 2017, 9(6), 606; https://doi.org/10.3390/rs9060606 - 14 Jun 2017

Cited by 3 | Viewed by 4278

Abstract

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated [...] Read more.

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Figure 1

13249 KiB

Open AccessEditor’s ChoiceArticle

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

by Lu She, Linlu Mei, Yong Xue, Yahui Che and Jie Guang

Remote Sens. 2017, 9(3), 253; https://doi.org/10.3390/rs9030253 - 9 Mar 2017

Cited by 15 | Viewed by 10223

Abstract

The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, [...] Read more.

The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up-Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R² = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R² = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

1970 KiB

Open AccessArticle

A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping

by Simon Kraatz, Reza Khanbilvardi and Peter Romanov

Remote Sens. 2017, 9(3), 229; https://doi.org/10.3390/rs9030229 - 3 Mar 2017

Cited by 18 | Viewed by 5886

Abstract

The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform [...] Read more.

The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and owing to differences in detector performance, it is quite possible that identical algorithms applied to even nearly identical instruments may produce substantially different cloud masks. Besides detector performance, cloud identification can be biased due to local (e.g., land cover), viewing geometry, and transient conditions (snow and ice). Snow/cloud confusions and large view angles can result in substantial overestimates of clouds and ice. This impacts algorithms, such as CRIOS, since false cloud cover precludes the determination of whether an otherwise reasonably cloud free grid consists of water or ice. Especially for applications aiming to frequently classify or monitor a location it is important to evaluate cloud masking, including false cloud detections. We present an assessment of three cloud masks via the parameter of effective revisit time. A 100 km stretch of up to 1.6 km wide river was examined with daily data sampled at 500 m resolution, examined over 317 days during winter. Results show that there are substantial differences between each of the cloud mask products, especially while the river bears ice. A contrast-based cloud screening approach was found to provide improved and consistent cloud and ice identification within the reach (95%–99% correlations, and 3%–7% mean absolute differences) between the independently observing platforms. River ice was also detected accurately (proportion correct 95%–100%) and more frequently. Owing to cross-platform compositing, it is possible to obtain an effective revisit time of 2.8 days and further error reductions. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Graphical abstract

3619 KiB

Open AccessArticle

Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

by Hang Yang, Lifu Zhang, Cindy Ong, Andrew Rodger, Jia Liu, Xuejian Sun, Hongming Zhang, Xun Jian and Qingxi Tong

Remote Sens. 2017, 9(3), 217; https://doi.org/10.3390/rs9030217 - 28 Feb 2017

Cited by 8 | Viewed by 6185

Abstract

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical [...] Read more.

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Atmospheric Correction of Remote Sensing Data

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (15 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI