The Radiometric Stability and Scaling of Collection 6 Terra- and Aqua-MODIS VIS, NIR, and SWIR Spectral Bands

Doelling, David R.; Wu, Aisheng; Xiong, Xiaoxiong; Scarino, Benjamin; Bhatt, Rajendra; Haney, Conor O.; Morstad, D.; Gopalan, Arun

doi:10.1109/tgrs.2015.2400928

Cited by 68 publications

(66 citation statements)

References 33 publications

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“…It corresponds to an increase in the NIR reflectance of 8%, which is higher than the calibration drift (less than 2%) identified for MODIS collection 6 [48]. The increasing SPM trend is consistent with the runoff increase observed in this area [7,36].…”

Section: Discussionsupporting

confidence: 71%

“…The overall trend is equal to 27%, which corresponds to an increase of SPM from 845 mg/L to 1072 mg/L (p-value: 1.2 × 10 −7 ). It is caused by an increase in the NIR reflectance, which is higher than the calibration drift identified for MODIS collection 6 [48]. At the monthly timescale, three months show significant trends: April, May, and July.…”

Section: Trends Over 2000-2016mentioning

confidence: 82%

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Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali

Robert

Kergoat

Soumaguel³

et al. 2017

Remote Sensing

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Abstract:The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental factors triggering SPM variability are yet to be understood. This study addresses these issues using remote sensing optical data. Turbidity and SPM of the Agoufou Lake in Sahelian Mali were measured from October 2014 to present, providing a large range of 'values (SPM ranging from 106 to 4178 mg/L). These data are compared to satellite reflectance from Landsat (ETM+, OLI) and MODIS (MOD09GQ, MYD09GQ). For each of these sensors, a spectral band in the near infrared region is found to be well suited to retrieve turbidity and SPM, up to very high values (R 2 = 0.70) seldom addressed by remote sensing studies. The satellite estimates are then employed to assess the SPM dynamics in the main lakes and ponds of the Gourma region and its links to environmental and anthropogenic factors. The main SPM seasonal peak is observed in the rainy season (June to September) in relation to precipitation and sediment transport. A second important peak occurs during the dry season, highlighting the importance of resuspension mechanisms in maintaining high values of SPM. Three different periods are observed: first, a relatively low winds period in the early dry season, when SPM decreases rapidly due to deposition; then, a period of wind-driven resuspension in January-March; and lastly, an SPM deposition period in April-May, when the monsoon replaces the winter trade wind. Overall, a significant increase of 27% in SPM values is observed between 2000 and 2016 in the Agoufou Lake. The significant spatio-temporal variability in SPM revealed by this study highlights the importance of high resolution optical sensors for continuous monitoring of water quality in these poorly instrumented regions.

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Section: Discussionsupporting

confidence: 71%

Section: Trends Over 2000-2016mentioning

confidence: 82%

Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali

Robert

Kergoat

Soumaguel³

et al. 2017

Remote Sensing

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“…The MODIS bands in gray in Table 1 are the selected reflective solar bands in this study To accurately track the performance of the on-orbit calibration, invariant earth targets such as the deserts [7,8], Dome-Concordia (Dome-C, located in Antarctica) [9,10], and clear-sky oceans [11,12] are used to monitor the stability of the MODIS L1B product. The use of deep convective clouds (DCCs) is another similar method and particularly useful for the SWIR bands 5-7 and 26 in assessing their long-term calibration stability [13,14]. DCCs are clouds undergoing intense convection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Deep Convective Cloud Technique in Evaluating the Long-Term Radiometric Stability of MODIS Reflective Solar Bands

Xiong

et al. 2017

Remote Sensing

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MODIS reflective solar bands are calibrated on-orbit using a solar diffuser and near-monthly lunar observations. To monitor the performance and effectiveness of the on-orbit calibrations, pseudo-invariant targets such as deep convective clouds (DCCs), Libya-4, and Dome-C are used to track the long-term stability of MODIS Level 1B product. However, the current MODIS operational DCC technique (DCCT) simply uses the criteria set for the 0.65-µm band. We optimize several critical DCCT parameters including the 11-µm IR-band Brightness Temperature (BT11) threshold for DCC identification, DCC core size and uniformity to help locate DCCs at convection centers, data collection time interval, and probability distribution function (PDF) bin increment for each channel. The mode reflectances corresponding to the PDF peaks are utilized as the DCC reflectances. Results show that the BT11 threshold and time interval are most critical for the Short Wave Infrared (SWIR) bands. The Bidirectional Reflectance Distribution Function model is most effective in reducing the DCC anisotropy for the visible channels. The uniformity filters and PDF bin size have minimal impacts on the visible channels and a larger impact on the SWIR bands. The newly optimized DCCT will be used for future evaluation of MODIS on-orbit calibration by MODIS Characterization Support Team.

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“…SAR (Synthetic Aperture Radar) [20] image has less capability in measuring ground deformation information because of Atmospheric Phase Delay Effects (APDE …”

Section: Atmospheric Correctionsmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

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The Radiometric Stability and Scaling of Collection 6 Terra- and Aqua-MODIS VIS, NIR, and SWIR Spectral Bands

Cited by 68 publications

References 33 publications

Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali

Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali

Optimization of a Deep Convective Cloud Technique in Evaluating the Long-Term Radiometric Stability of MODIS Reflective Solar Bands

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

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