Prelaunch algorithm and data format for the Level 1 calibration products for the EOS-AM1 Moderate Resolution Imaging Spectroradiometer (MODIS)

Guenther, B.; Godden, G.; Xiong, Xiaoxiong; Knight, Edward J.; Qiu, Shi-Yue; Montgomery, Harry E.; Hopkins, M.; Khayat, M.; Hao, Zhi-Dong

doi:10.1109/36.701021

Cited by 84 publications

(44 citation statements)

References 4 publications

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“…The 250-m bands are centered at 0.65 and 0.86 m with the 500-m bands at 0.47, 0.56, 1.24, 1.63, and 2.13 m. Each band's spectral response is determined by an interference filter overlying a detector array imaging a 10-km along-track scene for each scan (i.e., 40, 20, and 10 element arrays for the 250, 500, and 1000-m bands, respectively). MODIS has several onboard instruments for in-orbit radiometric and spectral characterization [5], [6]. Of particular importance to the cloud optical and microphysical algorithms, which are fundamentally based on solar reflectance values and not radiance, is a solar diffuser panel for reflectance calibration up through the 2.1-m MODIS band and an accompanying diffuser stability monitor for assessing the stability of the diffuser up to 1 m.…”

mentioning

confidence: 99%

The MODIS cloud products: algorithms and examples from terra

Platnick

King

Ackerman

et al. 2003

IEEE Trans. Geosci. Remote Sensing

1,672

1,498

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Abstract-TheIn 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.

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mentioning

confidence: 99%

The MODIS cloud products: algorithms and examples from terra

Platnick

King

Ackerman

et al. 2003

IEEE Trans. Geosci. Remote Sensing

1,672

1,498

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“…The Gaussian distributed random noises were simulated. From the specified description of the MODIS infrared channels (Guenther et al 1998), the standard deviations were found to be 0.05, 0.01 and 0.005 K for channels 2.1, 4.0 and 11.0 µm, respectively.…”

Section: Data Simulationmentioning

confidence: 99%

A method to retrieve subpixel fire temperature and fire area using MODIS data

Kong

2012

International Journal of Remote Sensing

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“…To unify to the angle's data, the first band (red band) data of MODIS are extracted from MOD02 and MYD02 products (Level-1B Calibrated Geolocation Data Set), in which the resolution is aggregated to 1 km. In MOD03, MYD03, MOD02 and MYD02 products, MOD is for TERRA, and MYD is for AQUA [30].…”

Section: Retrieval Of Hod Based On Satellite Remote Sensing In Clear mentioning

confidence: 99%

A Quantitatively Operational Judging Method for the Process of Large Regional Heavy Haze Event Based on Satellite Remote Sensing and Numerical Simulations

Wang

et al. 2017

Atmosphere

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Abstract:In recent years, large-area heavy haze pollution cases occur frequently in eastern China, especially evident in Beijing-Tianjin-Hebei and the surrounding regions. In order to operationally monitor the process of larger regional heavy haze events, a type of quantitative method based on satellite remote sensing and numerical simulations was first established and applied in multiple heavy haze processes in the research area. First, this study proposed the operational haze aerosol optical depth (HOD) method by combining Terra, Aqua satellite and WRF-NAQPM numerical simulation in haze days. Second, based on the coupled HOD data, we proposed the quantitative method for obtaining the process and severity degree for larger regional heavy haze events. Finally, this study used the method applying it to several typical heavy pollution events which occurred in Beijing-Tianjin-Hebei and its three surrounding provinces in the winter season from 1 November 2015 to 4 January 2016. The validation for HOD retrieval results showed that the couple HOD from this study have good accuracy, the linear correlation coefficient between retrieval HOD and the AERONET Beijing station data reached over 0.8, and the linear correlation coefficient between the retrieval HOD and the regional ground monitoring station PM 2.5 data reached over 0.7. The applied results showed that the method in this study is feasible to reflect the whole process of heavy haze events. Analysis of the typical heavy haze pollution events showed that the set of quantitative haze judging method in this study was consistent with the meteorological conditions in haze days also verifying that the method for haze inversion and the process analysis is reliable.

show abstract

Prelaunch algorithm and data format for the Level 1 calibration products for the EOS-AM1 Moderate Resolution Imaging Spectroradiometer (MODIS)

Cited by 84 publications

References 4 publications

The MODIS cloud products: algorithms and examples from terra

The MODIS cloud products: algorithms and examples from terra

A method to retrieve subpixel fire temperature and fire area using MODIS data

A Quantitatively Operational Judging Method for the Process of Large Regional Heavy Haze Event Based on Satellite Remote Sensing and Numerical Simulations

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