Atmospheric correction of MODIS data in the visible to middle infrared: first results

Vermote, Éric; Saleous, Nazmi; Justice, Christopher O.

doi:10.1016/s0034-4257(02)00089-5

Cited by 654 publications

(385 citation statements)

References 10 publications

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“…To generate the data used in this paper, MODIS thirty-two day composites were used as inputs and included data from the MODIS land bands (blue (459-479 nm), green (545-565 nm), red (620-670 nm), near-infrared (841-876 nm), and mid-infrared (1230-1250, 1628-1652, 2105-2155 nm)) (Vermote et al 2002), as well as data from the MODIS land surface temperature product (Wan et al 2002). Composite imagery represent the best land observation over the compositing period, in this case 32 days.…”

Section: Iiic Constructing the Satellite Datasetmentioning

confidence: 99%

The Political Economy of Deforestation in the Tropics*

Burgess¹,

Hansen²,

Olken³

et al. 2012

The Quarterly Journal of Economics

321

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Tropical deforestation accounts for almost one-…fth of greenhouse gas emissions and threatens the world's most diverse ecosystems. Much of this deforestation is driven by illegal logging. We use novel satellite data that tracks annual deforestation across eight years of Indonesian institutional change to examine how local o¢ cials'incentives a¤ect deforestation. Increases in the number of political jurisdictions lead to increased deforestation and lower timber prices, consistent with Cournot competition between jurisdictions. Illegal logging and local oil and gas rents are short run substitutes, but this e¤ect disappears over time with political turnover. The results illustrate how local o¢ cials'incentives a¤ect deforestation, and show how standard economic theories can explain illegal behavior.

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Section: Iiic Constructing the Satellite Datasetmentioning

confidence: 99%

The Political Economy of Deforestation in the Tropics*

Burgess¹,

Hansen²,

Olken³

et al. 2012

The Quarterly Journal of Economics

321

View full text Add to dashboard Cite

show abstract

“…This is a well known software life cycle phenomenon, but is complex to diagnose and rectify across the interdependent MODIS Land products. For example, bugs and QA issues found in the cloud mask product (Platnick et al 2003) are passed through the surface reflectance products (Vermote et al 2002) which in turn propagate into the vegetation index (Huete et al 2002), LAI/FPAR (Myneni et al 2002) and BRDF/Albedo (Schaaf et al 2002) products. The number of code updates was highest in the first 12 months following launch, when for example, the MODIS Terra Land surface reflectance code was updated 14 times.…”

Section: Modis Land Quality Assessment Rolesmentioning

confidence: 99%

“…Users can pan and zoom and interactively identify data granules by mouse clicks, allowing for synoptic visual inspection and identification of problematic data files. Figure 5.2 illustrates a Collection 4 Land surface reflectance (Vermote et al 2002) global browse image.…”

Section: Global Browsementioning

confidence: 99%

MODIS Land Data Products: Generation, Quality Assurance and Validation

Masuoka

Roy²,

Wolfe³

et al. 2010

Remote Sensing and Digital Image Processing

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“…Scheme 2 (MODIS land surface reflectance product (MOD09) as input): The MOD09 product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648, 858, 470, 555, 1240, 1640, and 2130Vermote et al, 2002). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption.…”

Section: Modis Datamentioning

confidence: 99%