Discriminating clear sky from clouds with MODIS
TL;DR: The MODIS cloud mask algorithm as discussed by the authors uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies, which is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options.
Abstract: The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of the MEDIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MEDIS cloud mask is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options. The MEDIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy-to-follow algorithm paths. The MEDIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear-sky conditions. This paper outlines the MEDIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MEDIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.
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TL;DR: In this article, the spectral optical thickness and effective radius of the aerosol over the ocean were validated by comparison with two years of Aerosol Robotic Network (AERONET) data.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both NASA’s Terra and Aqua satellites is making near-global daily observations of the earth in a wide spectral range (0.41–15 m). These measurements are used to derive spectral aerosol optical thickness and aerosol size parameters over both land and ocean. The aerosol products available over land include aerosol optical thickness at three visible wavelengths, a measure of the fraction of aerosol optical thickness attributed to the fine mode, and several derived parameters including reflected spectral solar flux at the top of the atmosphere. Over the ocean, the aerosol optical thickness is provided in seven wavelengths from 0.47 to 2.13 m. In addition, quantitative aerosol size information includes effective radius of the aerosol and quantitative fraction of optical thickness attributed to the fine mode. Spectral irradiance contributed by the aerosol, mass concentration, and number of cloud condensation nuclei round out the list of available aerosol products over the ocean. The spectral optical thickness and effective radius of the aerosol over the ocean are validated by comparison with two years of Aerosol Robotic Network (AERONET) data gleaned from 132 AERONET stations. Eight thousand MODIS aerosol retrievals collocated with AERONET measurements confirm that one standard deviation of MODIS optical thickness retrievals fall within the predicted uncertainty of 0.03 0.05 over ocean and 0.05 0.15 over land. Two hundred and seventy-one MODIS aerosol retrievals collocated with AERONET inversions at island and coastal sites suggest that one standard deviation of MODIS effective radius retrievals falls within reff 0.11 m. The accuracy of the MODIS retrievals suggests that the product can be used to help narrow the uncertainties associated with aerosol radiative forcing of global climate.
2,824 citations
Cites background or methods from "Discriminating clear sky from cloud..."
...The MOD/MYD35 cloud mask product also supplies the earth’s surface information that identifies whether a pixel is a “land” pixel or a “water” pixel....
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...These tests are IR cirrus test (byte 2, bit 4), 6.7- m test (byte 2, bit 8), and Delta IR test (byte 3, bit 3) (Ackerman et al. 1998)....
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...These reflectances along with the MODIS cloud mask product identified as MOD/MYD35 (Ackerman et al. 1998) and meteorological data from the National Centers for Environmental Prediction (NCEP) provide the input for the algorithms....
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...These tests are IR cirrus test (byte 2, bit 4), 6.7-m test (byte 2, bit 8), and Delta IR test (byte 3, bit 3) ( Ackerman et al. 1998 )....
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...Originally, the standard MODIS cloud mask (MOD/MYD35) provided all masking information....
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TL;DR: The various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir are described.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.
1,636 citations
Cites background from "Discriminating clear sky from cloud..."
...As described in [8], a minimum confidence is determined for each group as follows:...
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TL;DR: The Collection 6 (C6) algorithm as mentioned in this paper was proposed to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance.
Abstract: . The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target" (DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue" (DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DT-land (#1 and #2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to ≤ 84°) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time, we quantified how "upstream" changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.
1,628 citations
Cites background or methods from "Discriminating clear sky from cloud..."
...The so-called “Wisconsin” cloud mask (MxD35; Ackerman et al., 1998, 2010) has also been updated for C6 (http: //modis-atmos.gsfc.nasa.gov/products_C006update.html) There are many changes (including the land/sea mask issue just discussed); however, most do not impact the MxD04 product....
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...Therefore, three thermal infrared (IR) test results are selected from the upstream MODIS cloud mask file (MxD35_L2, Ackerman et al., 1998, 2010)....
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TL;DR: The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.
1,620 citations
Cites background from "Discriminating clear sky from cloud..."
...Though it works sometimes, most of the time it will inevitably include other dark surfaces that have similar spectral signatures (like topographic shadows or wetlands) and exclude cloud shadows that are not dark enough (Ackerman et al., 1998; Hutchison et al., 2009)....
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...These sensors are usually equipped with more than one thermal band, or with water vapor/CO2 absorption bands, both of which are useful for thin semitransparent cloud detection (Ackerman et al., 1998; Derrien et al., 1993; Saunders & Kriebel, 1998)....
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TL;DR: An improved replacement detection algorithm is presented that offers increased sensitivity to smaller, cooler fires as well as a significantly lower false alarm rate.
1,553 citations
Cites background from "Discriminating clear sky from cloud..."
..., 2002; Seielstad, Riddering, Brown, Queen, & Hao, 2002), this problem has been experienced with other cloud masking methods, including the MODIS cloud mask product (Ackerman et al., 1998)....
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...As noted previously (Justice, Giglio et al., 2002; Seielstad, Riddering, Brown, Queen, & Hao, 2002), this problem has been experienced with other cloud masking methods, including the MODIS cloud mask product (Ackerman et al., 1998)....
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References
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TL;DR: The SNOMAP algorithm as discussed by the authors was developed to map global snow cover using Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning at launch in 1998.
1,069 citations
"Discriminating clear sky from cloud..." refers methods in this paper
...An abbreviated snow index [ Hall et al. 1995] has been incorporated into the cloud mask so that the cloud mask can update the snow/ice cover from the last 24 hours to accommodate synoptic changes....
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...An abbreviated snow index [Hall et al. 1995] has been incorporated into the cloud mask so that the cloud mask can update the snow/ice cover from the last 24 hours to accommodate synoptic changes....
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TL;DR: The authors describe the status ofMODIS-N and its companion instrument MODIS-T (tilt), a tiltable cross-track scanning spectrometer with 32 uniformly spaced channels between 0.410 and 0.875 mu m, used for determining the total precipitable water vapor and atmospheric stability.
Abstract: The authors describe the status of MODIS-N and its companion instrument MODIS-T (tilt), a tiltable cross-track scanning spectrometer with 32 uniformly spaced channels between 0.410 and 0.875 mu m. They review the various methods being developed for the remote sensing of atmospheric properties using MODIS, placing primary emphasis on the principal atmospheric applications of determining the optical, microphysical, and physical properties of clouds and aerosol particles from spectral reflection and thermal emission measurements. In addition to cloud and aerosol properties, MODIS-N will be used for determining the total precipitable water vapor and atmospheric stability. The physical principles behind the determination of each of these atmospheric products are described, together with an example of their application to aircraft and/or satellite measurements. >
995 citations
TL;DR: In this article, the authors proposed a scheme to identify cloud-free and cloud-filled pixels (i.e. fields of view) from satellite radiance data, which consists of five daytime or five night-time tests applied to each individual pixel to determine whether that pixel is cloud free, partly cloudy or cloud filled.
Abstract: To obtain accurate estimates of surface and cloud parameters from satellite radiance data a scheme has to be devised which identifies cloud-free and cloud-filled pixels (i.e. fields of view). Such a scheme has been developed for application to high resolution (1·1 km pixel) images recorded over Western Europe and the North Atlantic by the AVHRR on the TIROS-N/NOAA polar orbiters. The scheme consists of five daytime or five night-time tests applied to each individual pixel to determine whether that pixel is cloud-free, partly cloudy or cloud-filled. The pixel is only identified as cloud-free or cloud-filled if it passes all the tests to identify that condition; otherwise it is assumed to be partly cloudy. Surface parameters (e.g. skin temperature, reflectance, vegetation index, snow cover) can then be inferred from the cloud-free radiances, and cloud parameters (e.g. cloud top temperature, optical depth and liquid water content) from the cloud-filled radiances. Only fractional cloud cover is deriv...
842 citations
"Discriminating clear sky from cloud..." refers methods in this paper
...Very thin cirrus clouds would best be detected by the 1.38 µm and BT11- BT12 APOLLO tests, two tests which have difficulty detecting low level clouds....
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...Gesell, G., 1989: An algorithm for snow and ice detection using AVHRR data: An extension to the APOLLO software package....
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...The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme Over cLoud Land and Ocean (APOLLO) [Saunders and Kriebel 1988; Kriebel and Saunders 1988; Gesell 1989] uses the two visible and three infrared bands of the AVHRR....
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...The IR thin cirrus test applies brightness temperature differences to detect the presence of thin cirrus through the split window analysis of APOLLO 17 and the tri-spectral approach of Strabala et al [1994]....
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TL;DR: A new vegetation index is proposed which has been designed specifically to reduce the relative effects of these undesirable atmospheric perturbations, while maintaining the information about the vegetation cover.
Abstract: Knowledge about the state, spatial distribution and temporal evolution of the vegetation cover is of great scientific and economic value. Satellite platforms provide a most convenient tool to observe the biosphere globally and repetitively, but the quantitative interpretation of the observations may be difficult. Reflectance measurements in the visible and near-infrared regions have been analyzed with simple but powerful indices designed to enhance the contrast between the vegetation and other surface types, however, these indices are rather sensitive to atmospheric effects. The ‘correction’ of satellite data for atmospheric effects is possible but requires large data sets on the composition of the atmosphere. Instead, we propose a new vegetation index which has been designed specifically to reduce the relative effects of these undesirable atmospheric perturbations, while maintaining the information about the vegetation cover.
617 citations
TL;DR: In this article, the cloud detection part of the International Satellite Cloud Climatology Project (ISCCP) analysis is described, and the detection algorithm is supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes.
Abstract: This paper, the first of three, describes the cloud detection part of the International Satellite Cloud Climatology Project (ISCCP) analysis. Key features of the cloud detection alogrithm are (1) use of space and time radiance variation tests over several different space and time domains to account for the global variety of cloudy and clear characteristics, (2) estimation of clear radiance values for every time and place, and, (3) use of radiance thresholds that vary with the type of surface and climate regime. Design of the detection algorithm was supported by global, multiyear surveys of the statistical behavior of satellite-measured infrared and visible radiances to determine those characteristics that differentiate cloudy and clear scenes and how these characteristics vary among climate regimes. A summary of these statistical results is presented to illustrate how the cloud detection method works in a variety of circumstances. The sensitivity of the results to changing test parameter values is determined to provide a first estimate of the uncertainty of ISCCP cloud amounts. These test results (which exclude polar regions) suggest detection uncertainties of about 10% with possible negative biases of 5% (especially at night).
403 citations