A spectral reflectance-based approach to quantification of grassland cover from Landsat TM imagery

Zha, Yong; Gao, Jay; Song, Ning; Liu, Yansui; Jiang, Jianjun; Wei, Yuchun

doi:10.1016/j.rse.2003.05.001

Cited by 62 publications

(28 citation statements)

References 5 publications

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“…This method was originally designed for Landsat imagery, although it has also been applied to other platforms and sensors, such as Terra/Aqua-MODIS, SPOT-HRG/HRV, IRS-LISS-III, PROBA-CHRIS or the airborne CASI (Zha et al, 2005;Román-Cuesta et al, 2005;García-Millán et al, 2013). Some applications using this radiometric correction model include forest mapping (Vázquez, 2008;Pérez-Cabello et al, 2010;Zabala and Pons, 2011), crop mapping (Barbosa et al, 1996;Nuarsa et al, 2010;Moré et al, 2011), energy flux modelling (Cristóbal et al, 2011), grassland studies (Zha et al, 2003;Liu et al, 2005), climate-related modelling (Collado et al, 2002), biomass estimation (Lopes et al, 2009;Barrachina et al, 2010), water monitoring (Sánchez et al, 2010; and forest fire research Oliveras et al, 2009), among others.…”

Section: Model Presentationmentioning

confidence: 99%

Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

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Cristóbal

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tRadiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric + topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation.(2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC-off. Reflectance products have been validated with some example applications: time series robustness (for a pixel in a pseudoinvariant area, deviations are only 1.04% on average along the series), spectral signatures generation (visually coherent with the MODIS ones, but more similar between dates), and classification (up to 4 percent points better than those obtained with the original manual method or the CDR products). In conclusion, this new approach, that could also be applied to other sensors with similar band configurations, offers a fully automatic and reasonably good procedure for the new era of long time-series of spatially detailed global remote sensing data.

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Section: Model Presentationmentioning

confidence: 99%

Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

Pons

Pesquer

Cristóbal

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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“…Previous studies on the estimation of vegetation cover relied on examinations at a rather coarse spatial resolution of 30 m 3 30 m. Such a scale is unlikely to show the heterogeneity of grass cover (Zha et al 2003), as variations occur within a few meters in mountainous terrain (Asner and Lobell 2000). Consequently, there is a need to detect small-scale vegetation damage points, in order to prevent further erosion in mountainous regions (Alewell et al 2008).…”

Section: Systems Knowledgementioning

confidence: 99%

Remote Sensing for Grassland Management in the Arid Southwest

Marsett¹,

Heilman

et al. 2006

Rangeland Ecology & Management

213

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“…Las mediciones de reflectividad superficial en el campo pueden ser aplicadas en un gran número de aproximaciones analíticas de la teledetección como el análisis de mezclas espectrales (Smith et al, 2005), clasificación de imágenes de satélite, modelos predictivos, validación superficial (Zha et al, 2003), modelos de intercambio de energía (Chuvieco et al, 2005;Schaepman et al, 2005) o modelado de la reflectividad de la vegetación buscando longitudes de onda que incrementen la sensibilidad de los índices de vegetación o la creación de nuevos (Gitelson et al, 2002;Hansen y Schjoerring, 2003).…”

Section: Introductionunclassified

Evaluación de las consecuencias del fuego en quemas controladas mediante radiometría de campo

Llovería

Pérez‐Cabello

García-Martín

et al. 2013

CIG

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A spectral reflectance-based approach to quantification of grassland cover from Landsat TM imagery

Cited by 62 publications

References 5 publications

Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

Remote Sensing for Grassland Management in the Arid Southwest

Evaluación de las consecuencias del fuego en quemas controladas mediante radiometría de campo

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