The Experts below are selected from a list of 26517 Experts worldwide ranked by ideXlab platform
Shunlin Liang - One of the best experts on this subject based on the ideXlab platform.
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an improved Atmospheric Correction algorithm for hyperspectral remotely sensed imagery
IEEE Geoscience and Remote Sensing Letters, 2004Co-Authors: Shunlin Liang, Hongliang FangAbstract:There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through Atmospheric Correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous Atmospheric effects and recover surface reflectance spectra.
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Atmospheric Correction of landsat etm land surface imagery ii validation and applications
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen Chen, Jeffrey T Morisette, Chad J Shuey, C L Walthall, C S T DaughtryAbstract:For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on Atmospheric Correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new Atmospheric Correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much Atmospheric Correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that Atmospheric Correction does matter.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery part i methods
International Geoscience and Remote Sensing Symposium, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to Atmospheric Correction.
Mingzhen Chen - One of the best experts on this subject based on the ideXlab platform.
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Atmospheric Correction of landsat etm land surface imagery ii validation and applications
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen Chen, Jeffrey T Morisette, Chad J Shuey, C L Walthall, C S T DaughtryAbstract:For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on Atmospheric Correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new Atmospheric Correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much Atmospheric Correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that Atmospheric Correction does matter.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery part i methods
International Geoscience and Remote Sensing Symposium, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to Atmospheric Correction.
Hongliang Fang - One of the best experts on this subject based on the ideXlab platform.
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an improved Atmospheric Correction algorithm for hyperspectral remotely sensed imagery
IEEE Geoscience and Remote Sensing Letters, 2004Co-Authors: Shunlin Liang, Hongliang FangAbstract:There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through Atmospheric Correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous Atmospheric effects and recover surface reflectance spectra.
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Atmospheric Correction of landsat etm land surface imagery ii validation and applications
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen Chen, Jeffrey T Morisette, Chad J Shuey, C L Walthall, C S T DaughtryAbstract:For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on Atmospheric Correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new Atmospheric Correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much Atmospheric Correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that Atmospheric Correction does matter.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery i methods
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions.
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Atmospheric Correction of landsat etm land surface imagery part i methods
International Geoscience and Remote Sensing Symposium, 2001Co-Authors: Shunlin Liang, Hongliang Fang, Mingzhen ChenAbstract:To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, Atmospheric Correction is a necessary step. After reviewing historical development of Atmospheric Correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of Atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general Atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of Atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to Atmospheric Correction.
E Vermote - One of the best experts on this subject based on the ideXlab platform.
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Atmospheric Correction inter comparison exercise
Remote Sensing, 2018Co-Authors: Georgia Doxani, Jeanclaude Roger, Olivier Hagolle, E Vermote, Ferran Gascon, Stefan Adriaensen, David Frantz, Andre Hollstein, Grit Kirches, Jerome LouisAbstract: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.
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conterminous united states demonstration and characterization of modis based landsat etm Atmospheric Correction
Remote Sensing of Environment, 2014Co-Authors: D Roy, E Vermote, Yuchu Qin, V Kovalskyy, A Egorov, M C Hansen, Indrani Kommareddy, Lin YanAbstract:Abstract The potential of Landsat data processing to provide continental scale 30 m products has been demonstrated by the NASA Web-enabled Landsat Data (WELD) project. The integration of a recent MODIS based Landsat Atmospheric Correction algorithm into the WELD processing is described and demonstrated by application to 12 months of conterminous United States (CONUS) Landsat 7 ETM + data. A large volume assessment of the Atmospheric Correction is presented considering approximately 53 million 30 m pixel locations sampled systematically across the CONUS for December 2009 to November 2010. Monthly 30 m reflectance and derived normalized difference vegetation index (NDVI) data are assessed comparing the top of atmosphere (TOA) and the MODIS-based Atmospherically corrected surface reflectance values with respect to spectral, temporal, land cover, and a per-pixel Atmospheric Correction quality storage scheme. The mean CONUS absolute difference between surface and TOA NDVI expressed as a percentage of the surface NDVI was 28% and the surface NDVI was on average 0.1 greater than the TOA NDVI for “vegetated” surfaces. The mean difference between surface and TOA reflectance (surface minus TOA) increased monotonically with increasing surface reflectance. On average the change from a negative to a positive mean difference occurred when the surface reflectance was 0.36, 0.22, 0.17, 0.14, 0.07, and 0.02 for Landsat ETM + reflective bands 1, 2, 3, 4, 5, and 7 respectively. These values are of interest as they depict the average CONUS Landsat ETM + surface reflectance values where the atmosphere has on average no impact and provide the average boundary values for positive and negative Atmospheric contributions to ETM + TOA reflectance. The CONUS mean absolute differences between surface and TOA reflectance expressed as percentages of the surface reflectance were 45%, 22%, 12%, 6%, 5%, and 13% for Landsat ETM + bands 1, 2, 3, 4, 5 and 7 respectively.
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validation of a new parametric model for Atmospheric Correction of thermal infrared data
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: E Ellicott, E Vermote, F Petitcolin, Simon J HookAbstract:Surface temperature is a key component for understanding energy fluxes between the Earth's surface and atmosphere. Accurate retrieval of surface temperature from satellite observations requires proper Correction of the thermal channels for Atmospheric emission and attenuation. Although the split-window method has offered relatively accurate measurements, this empirical approach requires in situ data and will only perform well if the in situ data are from the same surface type and similar climatology. Single channel Correction reduces uncertainty inherent to the split-window method, but requires an accurate radiative transfer model and description of the Atmospheric profile. Unfortunately, this method is impractical for operational Correction of satellite retrievals due to the size of data sets and computation time required by radiative transfer modeling. We present a thermal parametric model based upon the MODTRAN radiative transfer code and tuned to Moderate Resolution Imaging Spectrometer (MODIS) channels. Comparison with MODTRAN showed a good performance for the parametric model and computation speeds approximately three orders of magnitude faster. Sea surface temperature (SST) calculated using Atmospheric Correction parameters generated from our model showed consistent results (rmse = 0.49 K) and small bias (-0.45 K) with the MODIS SST product (MYD28). Validation of surface temperatures derived using our model with in situ land and water temperature measurements exhibited accuracy (mean bias < 0.35 K) and low error (rmse < 1 K) for MODIS bands 31 and 32. Finally, an investigation of profile sources and their effect on Atmospheric Correction offered insight into the application of the parametric model for operational Correction of MODIS thermal bands.
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Atmospheric Correction for the monitoring of land surfaces
Journal of Geophysical Research, 2008Co-Authors: E Vermote, Svetlana Y KotchenovaAbstract:[1] This paper briefly describes the land surface reflectance product (MOD09), the current Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Correction (AC) algorithm and its recent updates, and provides the evaluation of the algorithm performance and product quality. The accuracy of the AC algorithm has been significantly improved owing to the use of the accurate Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) radiative transfer code and a better retrieval of aerosol properties by a refined internal aerosol inversion algorithm. The Collection 5 MOD09 surface reflectance product computed by the improved AC algorithm was analyzed for the year of 2003 through the comparison with a reference data set created with the help of Aerosol Robotic Network (AERONET) measurements and the 6SV code simulations. In general, the MOD09 product demonstrated satisfactory quality in all used MODIS bands except for band 3 (470 nm), which is used for aerosol inversion. The impact of uncertainties in MOD09 upon the downstream product, such as vegetation indices and albedo, was also evaluated.
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operational Atmospheric Correction of landsat tm data
Remote Sensing of Environment, 1999Co-Authors: Hassan Ouaidrari, E VermoteAbstract:The recent algorithms developed for biophysical variables assessment require accurate surface reflectance measurements. This article describes algorithms used for Atmospheric Correction of Landsat Thematic Mapper (TM) data. Atmospheric Corrections include Rayleigh scattering, gaseous absorption, and aerosol scattering in three visible channels (480 nm, 560 nm, and 660 nm), and the near-infrared channel (830 nm). Atmospheric constituents such as water vapor and ozone are extracted from climatology data sets, while aerosol optical depths (AODs) are derived from the TM scene itself by adopting the dark target approach. The dark target pixels are identified, and their reflectances in the visible channels are estimated using TM Channel 7 (2.1 μm). Atmospheric transmittance and aerosol optical depth are derived for 16 grid points equally distributed over the scene, then interpolated to match the TM spatial resolution. This technique considerably reduces the computing time without decreasing the accuracy. These algorithms were tested using 11 TM scenes over a wide variety of sites, including forest, crop, and semiarid areas. The AOD in the blue, green, and red channels retrieved using the dark target technique was validated using sunphotometer measurements. The absolute error associated with AOD assessment was less than 0.15. A statistical analysis was also conducted to evaluate the Atmospheric Correction method. Based on data from the FIFE (First ISLSCP Field Experiment) experiment, the absolute error between ground measurements and TM reflectance was less than 0.015 in the visible channels, and less than 0.08 in the near-infrared channel.
Menghua Wang - One of the best experts on this subject based on the ideXlab platform.
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sensor capability and Atmospheric Correction in ocean colour remote sensing
Remote Sensing, 2015Co-Authors: Simon Emberton, Lars Chittka, Andrea Cavallaro, Menghua WangAbstract:Accurate Correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for Atmospheric Correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in Atmospheric Correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.
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Atmospheric Correction using near infrared bands for satellite ocean color data processing in the turbid western pacific region
Optics Express, 2012Co-Authors: Menghua Wang, Lide JiangAbstract:A regional near-infrared (NIR) ocean normalized water-leaving radiance (nLw(λ)) model is proposed for Atmospheric Correction for ocean color data processing in the western Pacific region, including the Bohai Sea, Yellow Sea, and East China Sea. Our motivation for this work is to derive ocean color products in the highly turbid western Pacific region using the Geostationary Ocean Color Imager (GOCI) onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS). GOCI has eight spectral bands from 412 to 865 nm but does not have shortwave infrared (SWIR) bands that are needed for satellite ocean color remote sensing in the turbid ocean region. Based on a regional empirical relationship between the NIR nLw(λ) and diffuse attenuation coefficient at 490 nm (Kd(490)), which is derived from the long-term measurements with the Moderate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, an iterative scheme with the NIR-based Atmospheric Correction algorithm has been developed. Results from MODIS-Aqua measurements show that ocean color products in the region derived from the new proposed NIR-corrected Atmospheric Correction algorithm match well with those from the SWIR Atmospheric Correction algorithm. Thus, the proposed new Atmospheric Correction method provides an alternative for ocean color data processing for GOCI (and other ocean color satellite sensors without SWIR bands) in the turbid ocean regions of the Bohai Sea, Yellow Sea, and East China Sea, although the SWIR-based Atmospheric Correction approach is still much preferred. The proposed Atmospheric Correction methodology can also be applied to other turbid coastal regions.
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Evaluation of MODIS SWIR and NIR-SWIR Atmospheric Correction algorithms using SeaBASS data
Remote Sensing of Environment, 2009Co-Authors: Menghua Wang, Seung Hyun Son, Wei ShiAbstract:Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of Atmospheric Correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the Atmospheric Correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.
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The NIR-SWIR combined Atmospheric Correction approach for MODIS ocean color data processing
Optics Express, 2007Co-Authors: Menghua Wang, Wei ShiAbstract:A method of ocean color data processing using the combined near-infrared (NIR) and shortwave infrared (SWIR) bands for Atmospheric Correction for the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua is proposed. MODIS-Aqua has been producing the high quality ocean color products in the open oceans, but there are still some significant errors in the derived products in the coastal regions. With the proposed NIR-SWIR combined algorithm, MODIS ocean color data can be processed using the standard (NIR) Atmospheric Correction algorithm for the open oceans, whereas for the turbid waters in the coastal region the SWIR Atmospheric Correction algorithm can be executed. The turbid water index developed by Shi and Wang (2007) (Remote Sens. Environ. 110, 149-161 (2007)) is computed prior to the Atmospheric Correction for the identification of the productive and/or turbid waters where the SWIR algorithm can be operated. For non-turbid ocean waters (discriminated using the turbid water index criterion), the MODIS data are still processed using the standard (NIR) algorithm. The NIR-SWIR combined algorithm has been tested and evaluated. Two examples from MODIS-Aqua measurements along the U.S. and China east coast regions show improved ocean color products with the new approach. In particular, there are no obvious data discontinuities between using the NIR and SWIR methods. Therefore, with the NIR-SWIR combined approach for the MODIS ocean color data processing, good quality ocean color products can be derived both in clear (open) oceans as well as for turbid coastal waters.