scholarly journals MODIS Collection 6 MAIAC Algorithm

2018 ◽  
Author(s):  
Alexei Lyapustin ◽  
Yujie Wang ◽  
Sergey Korkin ◽  
Dong Huang
Keyword(s):  
Missing Values ◽  
Atmospheric Correction ◽  
Global Processing ◽  
Cloud Detection ◽  
Bidirectional Reflectance ◽  
Time Series Approach ◽  
Fine Mode ◽  
Spectral Regression ◽  
Cloud Mask ◽  
Aerosol Type

Abstract. This paper describes the latest version of algorithm MAIAC used for processing of the MODIS Collection 6 data record. Since initial publication in 2011–2012, MAIAC has changed considerably to adapt global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include 1) transition from 25 km to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance; 2) continuous improvements of cloud detection; 3) introduction of “Smoke” and “Dust” tests to discriminate absorbing fine and coarse mode aerosols; 4) adding over-water processing; 5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an inter-disciplinary suite of atmospheric and land products, including: cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 μm, aerosol type (background/smoke/dust), and fine mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-Thick Li-Sparse (RTLS) BRDF model and instantaneous albedo; for snow-covered surfaces, we provide sub-pixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except BRF which is also provided at 500 m resolution, on Sinusoidal grid adopted by the MODIS land team. All products are provided on per-observation basis in daily files except BRDF/albedo which is reported every 8 days. Because MAIAC uses a time series approach, the BRDF/albedo are naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS land bands 1–7 and ocean band 8, BRF is reported for both land and ocean bands 1–12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

scholarly journals MODIS Collection 6 MAIAC algorithm

2018 ◽  
Vol 11 (10) ◽  
pp. 5741-5765 ◽  
Author(s):  
Alexei Lyapustin ◽  
Yujie Wang ◽  
Sergey Korkin ◽  
Dong Huang
Keyword(s):  
Missing Values ◽  
Atmospheric Correction ◽  
Global Processing ◽  
Cloud Detection ◽  
Bidirectional Reflectance ◽  
Time Series Approach ◽  
Fine Mode ◽  
Spectral Regression ◽  
Cloud Mask ◽  
Bidirectional Reflectance Distribution

Abstract. This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record. Since initial publication in 2011–2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported for both land and ocean bands 1–12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

scholarly journals SENTINEL-2 SURFACE REFLECTANCE PRODUCTS GENERATED BY CNES AND DLR: METHODS, VALIDATION AND APPLICATIONS

2021 ◽  
Vol V-1-2021 ◽  
pp. 9-15
Author(s):  
O. Hagolle ◽  
J. Colin ◽  
S. Coustance ◽  
P. Kettig ◽  
P. D’Angelo ◽  
...  
Keyword(s):  
Western Europe ◽  
Atmospheric Correction ◽  
Weighted Average ◽  
Data Sets ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
In Situ Data ◽  
Multi Temporal ◽  
Cloud Mask ◽  
Sentinel 2

Abstract. To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

scholarly journals BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

2016 ◽  
Vol III-7 ◽  
pp. 3-8 ◽  
Author(s):  
M. Matsuoka ◽  
M. Takagi ◽  
S. Akatsuka ◽  
R. Honda ◽  
A. Nonomura ◽  
...  
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Atmospheric Correction ◽  
Time Of Day ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

scholarly journals Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product

2020 ◽  
Vol 12 (1) ◽  
pp. 611-628 ◽  
Author(s):  
Michel M. Verstraete ◽  
Linda A. Hunt ◽  
Hugo De Lemos ◽  
Larry Di Girolamo
Keyword(s):  
Missing Values ◽  
Near Infrared ◽  
Data Repository ◽  
Observation Data ◽  
Science Data ◽  
Data Services ◽  
Data Product ◽  
Spectral Bands ◽  
Earth Observation Data ◽  
Cloud Mask

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal  

2021 ◽  
Author(s):  
Boli Yang ◽  
Yan Feng ◽  
Ruyin Cao
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Missing Values ◽  
Similarity Index ◽  
Visual Assessment ◽  
New Method ◽  
Landsat Data ◽  
Cloud Detection ◽  
Cloud Removal ◽  
Ultimate Objective

<p>Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     </p><p><strong> </strong></p><p><strong>Keywords: </strong>Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN</p>

Comparison of NOAA's Operational AVHRR-Derived Cloud Amount to Other Satellite-Derived Cloud Climatologies

2004 ◽  
Vol 17 (24) ◽  
pp. 4805-4822 ◽  
Author(s):  
Sarah M. Thomas ◽  
Andrew K. Heidinger ◽  
Michael J. Pavolonis
Keyword(s):  
High Resolution ◽  
Phase 1 ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Noaa Avhrr ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual ◽  
Cloud Mask

Abstract A comparison is made between a new operational NOAA Advanced Very High Resolution Radiometer (AVHRR) global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the Clouds from AVHRR phase 1 (CLAVR-1) cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high-latitude performance and the inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1-derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVR-x cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than the International Satellite Cloud Climatology Project (ISCCP) and the University of Wisconsin High Resolution Infrared Radiation Sounder (UW HIRS). Comparisons to the Moderate Resolution Imaging Spectroradiometer (MODIS) for 1–8 April 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July–January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel-level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR-x cloud detection results have been operational since late 2003 and are available in real time from NOAA.

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

2017 ◽  
Vol 9 (12) ◽  
pp. 1230 ◽  
Author(s):  
Bastien Rouquié ◽  
Olivier Hagolle ◽  
François-Marie Bréon ◽  
Olivier Boucher ◽  
Camille Desjardins ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Monitoring Service ◽  
Aerosol Type

scholarly journals Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record

2015 ◽  
Vol 8 (10) ◽  
pp. 4561-4571 ◽  
Author(s):  
A. Lattanzio ◽  
F. Fell ◽  
R. Bennartz ◽  
I. F. Trigo ◽  
J. Schulz
Keyword(s):  
Seasonal Variation ◽  
Surface Albedo ◽  
Global Climate ◽  
Radiation Budget ◽  
Limited Information ◽  
Cloud Detection ◽  
Climate Data ◽  
Data Record ◽  
Cloud Mask ◽  
Climate Data Record

Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (1982–2006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release.

Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5

2008 ◽  
Vol 25 (7) ◽  
pp. 1057-1072 ◽  
Author(s):  
Richard A. Frey ◽  
Steven A. Ackerman ◽  
Yinghui Liu ◽  
Kathleen I. Strabala ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Sea Surface ◽  
Sea Surface Temperatures ◽  
Cloud Detection ◽  
Stream Data ◽  
Water Vapor Absorption ◽  
Brightness Temperatures ◽  
Surface Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract Significant improvements have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask (MOD35 and MYD35) for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2-μm water vapor absorption band have been added as well as updates to the 3.9–12- and 11–12-μm cloud tests. More non-MODIS ancillary input data have been added. Land and sea surface temperature maps provide crucial information for mid- and low-level cloud detection and lessen dependence on ocean brightness temperature variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud versus clear-sky signals, where visible and near-infrared (NIR) reflectances are high, but infrared brightness temperatures are relatively warm. Day and night Arctic cloud frequency results are compared to those created by the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-Extended (APP-X) algorithm. Day versus night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions are shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions. Continuing validation activities are described in Part II of this paper.

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