A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+

Moran, M. Susan; Bryant, R.; Thome, Kurtis J.; Ni, Wanmei; Nouvellon, Yann; González-Dugo, María P.; Qi, Jiaguo; Clarke, Thomas R.

doi:10.1016/s0034-4257(01)00250-4

Cited by 139 publications

(80 citation statements)

References 23 publications

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“…Therefore, the empirical line method (Moran et al 2001 and references within the article) was used to derive an atmospheric correction of the ALI image. The empirical line method requires knowledge about reflectance of at least one target within an image (Moran et al 2001). This target reflectance may be measured during image acquisition or estimated from historical data.…”

Section: Methodsmentioning

confidence: 99%

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Kutser

2004

Limnology & Oceanography

333

199

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The extent of cyanobacterial blooms has been mapped using different satellite sensors from weather satellites to synthetic aperture radars. Quantitative detection of chlorophyll in cyanobacterial blooms by remote sensing, however, has been less successful. The first civilian hyperspectral sensor in space, Hyperion, acquired an image of cyanobacterial bloom in the western part of the Gulf of Finland on 14 July 2002. A chlorophyll concentration map was produced from this image using a spectral library that was created by running a bio-optical model with variable concentrations of chlorophyll. The results show that chlorophyll concentrations in the bloom area were much higher than reported by conventional water-monitoring programs, ships-of-opportunity, and satellite measurements. The reason why both in situ and satellite methods underestimate the amount of phytoplankton during cyanobacterial blooms is vertical and horizontal distribution of cyanobacteria, because cyanobacteria can regulate their buoyancy and are not uniformly distributed within the top mixed layer of water column in calm weather conditions. Aggregations of cyanobacteria form dense subsurface blooms and surface scums during extensive blooms. This study demonstrates that it is difficult to collect representative water samples from research vessels using standard methods because ships and water samplers destroy the natural distribution of cyanobacteria in the sampling process. Flowthrough systems take water samples from the depths at which the concentration of cyanobacteria is not correlated with the amount of phytoplankton that remote sensing instruments detect. The chlorophyll estimation accuracy in cyanobacterial blooms by many satellites is limited because of spatial resolution, as significant changes in chlorophyll concentration occur even at a smaller spatial scale than 30 m.

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Section: Methodsmentioning

confidence: 99%

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Kutser

2004

Limnology & Oceanography

333

199

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“…Methods and/or correction coefficients have been published for many radiometric correction techniques, including conversion to top-ofatmosphere reflectance (Chander and Markham 2003;Chander et al 2007), dark-object subtraction (Chavez 1988;Teillet and Fedosejevs 1995), measuring or estimating atmospheric aerosols to derive surface reflectance (Liang et al 2001;Thome 2001), applying radiative transfer functions (Moran et al 1992), empirical line calibration (Moran et al 2001), and haze removal (Richter 1996). More detailed reviews and summaries of radiometric image processing entailing terminology, sensor radiometric calibration, surface reflectance retrieval, image normalisation, and topographic corrections are available from Richards and Jia (1999), Liang et al (2001Liang et al ( , 2002, Peddle et al (2003), Schaepman-Strub et al (2006), and Vanonckelen et al (2013).…”

Section: What Sensors Are Available?mentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

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“…Landsat 7 ETM+ data (30 m spatial resolution) were collected for the study area on 25 July 2001. All image data were converted to at-satellite exo-atmospheric reflectance, a temporally comparable surface reflectance factor (Moran et al, 2001), following procedures outlined by NASA (2002) and Taylor (2005). Furthermore, the Landsat 7 ETM+ data were georeferenced to the same planimetric database as the IKONOS data (i.e., Universal Transverse Mercator, zone 15N, North American Datum 1983) with an overall root-mean-square (rms) error of approximately 8 m.…”

Section: Remote Sensing Data Preprocessingmentioning

confidence: 99%

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

Laidler¹,

Treitz²,

Atkinson³

2009

ARCTIC

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ABSTRACT. Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.Key words: tundra, biophysical remote sensing, vegetation indices, NDVI, percent cover, Landsat 7 ETM+, IKONOS, Boothia Peninsula, Canadian Arctic RÉSUMÉ. On croit que les environnements de la toundra arctique sont particulièrement sensibles aux changements climatiques, en ce sens que toute altération du fonctionnement de l'écosystème est susceptible d'être exprimée dans le réarrangement de la phénologie de la végétation, de la composition des espèces et de la productivité nette de l'écosystème (PNÉ). La télédétection s'avère un outil efficace de quantification et de surveillance des variables biophysiques dans le temps et dans l'espace. Cette étude explore la relation entre l'indice d'activité végétale et le pourcentage de couverture végétale en milieu de toundra, où les variations propres à l'humidité du sol, au sol exposé et au till de gravier ont une influence considérable sur la réponse spectrale et, par conséquent, sur la caractérisation des communautés végétales.

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A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+

Cited by 139 publications

References 23 publications

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

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