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ASSESSMENT OF RADIOMETRIC CORRECTION TECHNIQUES IN ANALYZING
VEGETATION VARIABILITY AND CHANGE USING TIME SERIES OF LANDSAT
IMAGES
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gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the
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information received by satellites, making it difficult to differentiate the surface signal from noise
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and complicates attempts to obtain homogeneous time series. We compare different procedures
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developed to create homogeneous time series of Landsat images, including sensor calibration,
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atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of
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Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–
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2007. Different processing steps were tested and the best option selected according to quantitative
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statistics obtained from invariant areas, simultaneous medium-resolution images, and field
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measurements. The optimum procedure includes cross-calibration between Landsat sensors,
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atmospheric correction using complex radiative transfer models, a non-lambertian topographic
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correction, and a relative radiometric normalization using an automatic procedure. Finally, three
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case studies are presented to illustrate the role of the different radiometric correction procedures
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when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as
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temporal variability. We have shown that to analyse different vegetation processes with Landsat
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data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of
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complex radiometric correction procedures. Failure to follow such a procedure may mean that the
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analyzed processes are non-recognizable and that the obtained results are invalid.
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Key-words. Landsat time series, TM-ETM+ cross-calibration, atmospheric correction, relative
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normalization, vegetation change, Ebro Valley, Spain
Sergio M. Vicente-Serrano1*, Fernando Pérez-Cabello2 and Teodoro Lasanta1
Instituto Pirenaico de Ecología, CSIC (Spanish Research Council), Campus de Aula Dei, P.O. Box
202, Zaragoza 50080, Spain
2
Departamento de Geografía. Universidad de Zaragoza. C/ Pedro Cerbuna 12. 50009. Zaragoza.
Spain.
* svicen@ipe.csic.es
Abstract. The homogeneity of time series of satellite images is crucial when studying abrupt or
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1. Introduction
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surface characteristics over the past four decades (Cohen and Goward, 2004). Landsat images have
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been widely used for land cover mapping and the creation of vegetation inventories at different
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spatial scales (e.g., Bossard et al., 2000). Moreover, the systematic archiving of Landsat data makes
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this information highly valuable for retrospective analyses of land surface characteristics. Together
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with other types of satellite images such as NOAA-AVHRR (National Oceanic and Atmospheric
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Administration-Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution
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Imaging Spectroradiometer), multitemporal Landsat data have been widely used in recent decades
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to identify changes in land cover (e.g., Lenney et al., 1996). The spatial and spectral resolution of
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Landsat images makes these data highly suitable in analyzing both abrupt and gradual changes in
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vegetation cover and monitoring environmental processes such as degradation and desertification
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(Almeida-Filho and Shimabukuro, 2002), deforestation (Cohen et al., 2002; Huang et al., 2007),
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habitat fragmentation (Millington et al., 2003), forest succession (Song and Woodcock, 2003; Song
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et al., 2007), overgrazing (Jano et al., 1998; Pickup and Chewing, 1994), rangeland monitoring
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(Hostert et al., 2003), and vegetation recovery after natural disturbance such as volcanism
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(Lawrence and Ripple, 1999) and forest fires (Viedma et al., 1997; Lozano et al., 2007).
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Nevertheless, there exist constraints in using Landsat data for multitemporal studies because of
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problems in obtaining homogeneous time series, not affected by non-surface noise, and in which the
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images are comparable between different dates since the data only report on surface conditions.
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Multitemporal satellite-image datasets are affected by different sources of noise related to the
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stability of sensors, changes in satellite responsivity, changes in illumination, atmospheric effects,
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etc. These problems are not unique to Landsat images, but they are more difficult to overcome in
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Landsat images compared with images compiled by other satellites because the low temporal
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resolution of Landsat images makes it impossible to apply simple homogenization procedures such
The Landsat program for Earth observation has provided invaluable information on the Earth’s
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as composite creation (Holben, 1986) and temporal filtering (Van Dijk et al., 1987), which
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markedly reduce the non-surface noise.
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Notable efforts have been made to reduce non-surface noise in Landsat images, including attempts
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to calibrate the sensor to correct lifetime radiometric trends (Teillet et al., 2004; de Vries et al.,
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2007), cross-calibrate the images obtained from different sensors (Teillet et al., 2006; Röder et al.,
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2005), minimize atmospheric noise (Chavez, 1989; Ouaidrari and Vermote, 1999; Liang et al.,
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2001), and reduce the influence of topography (Civco, 1989; Gu and Gillespie, 1998; Pons and
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Solé, 1994); however, other studies have shown that the application of accurate sensor calibrations
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and complex atmospheric corrections does not guarantee the multitemporal homogeneity of Landsat
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datasets (Schroeder et al., 2006) because complete atmospheric properties are difficult to quantify
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and simplifications are commonly assumed. This problem has led to the development of relative
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radiometric normalization techniques based on adjustments to the radiometric properties of an
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image time series to match that of a single reference image. In recent years, efforts have been made
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to develop methods to select invariant pixels for a reliable application of relative normalization
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techniques (Du et al., 2002; Chen et al., 2005; Canty et al., 2004).
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Protocols have been proposed in processing multitemporal Landsat datasets (e.g., Hall et al., 1991;
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Hill and Strum, 1991; Han et al., 2007), comprising the following steps: i) geometric correction, ii)
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calibration of the satellite signal to obtain Top of the Atmosphere Radiance, iii) atmospheric
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correction to estimate surface reflectance, iv) topographic correction, and v) relative radiometric
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normalization between images obtained on different dates. Radiometric processing is recommended
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to be done prior to geometric processing since this resampling step generally smoothness the data
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set. Nevertheless, Landsat users in Europe commonly do not have access to data that have not
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already been geometrically corrected (the Level 1 System Corrected -1G- from the European Space
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Agency (ESA) is considered the standard product for most users since previous levels require
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extensive processing). When these protocols are applied, several decisions must be made due to the
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different procedures available at each step.
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The high degree of interest in using multitemporal Landsat datasets is in contrast with the small
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number of studies that have tested different procedures of radiometric correction with the aim of
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obtaining temporally homogeneous images. The majority of studies have tested the influence of
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atmospheric correction (Song and Woodcock, 2003; Norjamäki and Tokola, 2007) or relative
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normalization (Yuan and Elvidge, 1996; Tokola et al., 1998; Heo and Fitzhugh, 2000; Olthof et al.,
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2005) on the temporal stability of time series of Landsat images. In contrast, few studies have tested
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the influence of each of the above steps in obtaining temporally stable time series of images and
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few have assessed the relative importance of using simple or complex techniques at each step.
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Existing studies have shown that relative normalization is the most critical step in obtaining
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temporal stability in a series of images (Schroeder et al., 2006; Janzen et al., 2006), although the
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calibration procedure also has a noticeable effect on the final results (Paolini et al., 2006) and
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atmospheric correction is important in obtaining accurately estimate surface reflectance's and
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accurate magnitudes of vegetation indices over time (Song and Woodcock, 2003).
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Few studies have analyzed the role of complete radiometric correction protocols in processing
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multitemporal Landsat data when a number of different vegetation processes are of interest.
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Nevertheless, this is a crucial topic when analysing vegetation multitemporal dynamic using
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Landsat data. Thus different results have been found for land classification (Song et al., 2001;
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Paolini et al., 2006; Norjamäki and Tokola, 2007) and forest succession (Song and Woodcock,
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2003; Schroeder et al., 2006) as a function of the radiometric correction applied.
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The present study involves a complete evaluation of various radiometric correction processes
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required to obtain accurate time series of Landsat imagery, including calibration, atmospheric
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correction, topographic correction, and relative normalization. The objective is to identify the
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influence of different processing steps on multitemporal spectral reflectance trajectories developed
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with Landsat data and also to detect their performance to analyse different vegetation processes.
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Three case studies related to different processes of vegetation change and variability are provided to
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illustrate how different results can be obtained as a function of the employed radiometric correction
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protocol and the ecological process of interest: land cover change, forest regeneration after fire and
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ecosystem response to climate variability. The case studies were carried out in a complex ecological
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region in which several natural and human-induced processes of changes in vegetation cover have
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been recorded in recent decades.
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2. Study area
The study area is the central Ebro Valley, Spain (Landsat Path 199 Row 31), located in the
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northernmost semiarid region in Europe. Figure 1 shows the location of the study area, including a
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detailed land cover map. Surrounded by mountain chains, the Ebro Valley has a Mediterranean
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climate with continental characteristics, with marked spatial and seasonal variations in precipitation;
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the dry season occurs during the summer months. The elevation ranges from less than 300 m above
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sea level in the middle of the Ebro Depression to more than 2000 m a.s.l. in the Prepyrenees (North)
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and 2000 m a.s.l. in the highest peaks of the Iberian Range (South). In the central part of the valley,
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mean annual precipitation is 326 mm, with marked seasonality. The study area shows a strongly
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negative water balance (precipitation minus potential evapotranspiration), greater than 900 mm. The
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dominant land covers include steppes and herbaceous cultivation in dry farming areas. Coniferous
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forests (mainly Pinus halepensis) cover the few slopes of the region. Vegetation distribution is
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strongly controlled by aridity (Vicente-Serrano et al., 2006), and drought conditions have a marked
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influence on vegetation cover and activity (Vicente-Serrano, 2007). The lithology of the area is
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characterised by limestones and gypsums (Peña et al., 2002) that contribute to its aridity, since the
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soils are unable to retain the water as a consequence of the high hydraulic conductivity.
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3. Methodology
3.1. Satellite imagery database
To determine the patterns of vegetation variability and change, it is important to take into account
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seasonal variations in the distributions of different vegetation types. In the middle Ebro Valley,
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herbaceous species, shrubs, and forests show contrasting seasonal variations in vegetation activity
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(Vicente-Serrano et al., 2006). The peak activity for herbaceous species and shrubs occurs in
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spring; for forests in summer. These seasonal fluctuations make it difficult to capture the full range
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of vegetation processes with just a single season of imagery.
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Abrupt changes in vegetation activity commonly occur as a consequence of climate seasonality;
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consequently, it is important that the capture dates of images are similar in different years. It is not
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possible to combine images taken in different months, as this would have a strong influence on the
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temporal homogeneity of the dataset.
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We reviewed all of the available Landsat-Thematic Mapper (TM) and -Enhanced Thematic Mapper
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(ETM+) images in the archives of the European Space Agency (ESA). Since most studies based on
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Landsat data consider ETM+ and TM radiometry to be comparable, We used TM data from 1984
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and, then, switched to the ETM+ data when it became available in 1999 due to the better calibration
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of this sensor (Teillet et al., 2001); we subsequently switched back to using TM date due to the
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ETM+ SLC failure in 2003. Frequent cloud cover in spring means that only the month of March has
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a sufficient number of images to enable an analysis of vegetation activity. Months in summer pose
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fewer problems in terms of obtaining reliable clouds-free images; therefore, we selected the month
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of August to create a second time series. A total of 28 Landsat-TM and -ETM+ images taken
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between 1984 and 2007 were acquired from ESA, 16 corresponding to the summer season and 12 to
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spring. Table 1 lists the dates and types (TM or ETM+) of the selected images.
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3.2. Geometric correction
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The images were orthorectified using control points and a 1-m digital elevation model obtained
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from stereo pairs of aerial photographs, and resampled to 30 m to match the TM and ETM+
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resolutions. The image taken on 8 August 2000 with good visibility and being free of clouds, was
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registered using orthorectified digital aerial photographs as a reference. The rest of the images were
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co-registered to this image using control points. The images were orthorectified following the
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method of Palà and Pons (1995), which includes elevation data in performing a polynomial
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geometric correction. The X and Y root mean square error was less than 15 m (0.5 pixels) for all
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images, guaranteeing a precise geometric match among them. After geometric correction, cloud-
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covered and cloud shadows were manually digitized and eliminated.
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3.3. Calibration
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A precise calibration is required to convert DN (Digital Numbers) to satellite radiances in W m –2 sr–
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constant over time. A large variation, on the order of 20%, has been observed between the
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prelaunch gain coefficients and postlaunch cross-calibration (Chander et al., 2004; Chander et al.,
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2007). Moreover, the different spectral responses of TM and ETM+ sensors introduces
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comparability problems between Landsat 5 and Landsat 7 images (Teillet et al., 2001).
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For Landsat 5 imagery, the European Spatial Agency (ESA) has used constant calibration dynamic
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ranges since 1984 (http://earth.esa.int/pub/ESA_DOC/landsat_FAQ/#_Toc69122047), embedded
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within the product format. Chander et al. (2004) and Teillet et al. (2004) demonstrated an
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exponential decay of the solar reflective bands of Landsat 5-TM since 1984, with some differences
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between bands. We corrected the calibration coefficients embedded in the ESA products with
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reference to the time after launch of Landsat 5 in 1984, applying the equations formulated by Teillet
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et al. (2004). This procedure is recommended by the ESA in recalibrating Landsat products
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(Saunier and Rodríguez, 2006). The coefficients of calibration for Landsat 7-ETM+ were obtained
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according to upper and lower at-satellite radiance indicated in the Landsat-7 Science Data Users
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Handbook (http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html) using the values
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corresponding to the date (before or after July 1, 2000) and the type of gain (high or low, as
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embedded within the product format).
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Although the Landsat 5-TM and Landsat 7-ETM+ bands are commonly considered to be
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comparable, previous studies have demonstrated important differences between the two that may
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affect comparability, suggesting the need to apply cross-calibration procedures to both sensors
m–1. This is highly problematic for Landsat 5 imagery because calibration coefficients are not
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because for the majority of bands (2, 3, 4, and 7) the TM sensor underestimates the radiance values
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regarding ETM+ (Teillet et al., 2001; Vogelmann et al., 2001).
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Vogelmann et al. (2001) developed a simple procedure to cross-calibrate the Landsat-TM and
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ETM+ images, applicable to Level 1G formats. These authors proposed empirically derived slope
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and intercept values to convert Landsat 7 ETM+ DNs to Landsat 5 TM DNs based on two
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simultaneous images taken on June 2, 1999. We used these slope and intercept values in the present
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study to adjust the Landsat 7-ETM+ DNs to Landsat 5-TM DNs.
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Satellite radiance was obtained for Landsat 5-TM quantized calibrated pixel values in DNs, Landsat
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7-ETM+ quantized calibrated pixel values in DNs, and the cross-calibrated Landsat 7 ETM+
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quantized calibrated pixel values in DNs to Landsat 5-TM quantized calibrated pixel values in DNs
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according to:
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Lsat   Grescale  DN  Brescale (1)
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where Lsat is the satellite radiance in W m–2 sr–1 μm–1 for band . Grescale and Brescale are the
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calculated band-specific rescaling factors. Satellite radiances were converted to Top Of the
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Atmosphere (TOA) reflectances according to
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TOA 
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where is the TOA reflectance for band , d is the earth–sun distance in astronomical units,
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ESUN is the mean solar exoatmospheric irradiance for band , and s is the solar zenith angle in
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degrees. ESUN values were obtained from Chander and Markham (2003) for TM images and from
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the
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(http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html).
  Lsat   d 2 (2)
ESUN cos  s
Landsat-7
Science
Data
User
Handbook
for
ETM+
images
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3.4. Atmospheric correction
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Although TOA reflectance values are widely used in inventory and ecosystem studies, they do not
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take into account signal attenuation by the atmosphere, which strongly affects the
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intercomparability of satellite images taken on different dates. Upward and downward irradiance is
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modified by two atmospheric processes: absorption by gases and scattering by aerosols and water
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molecules (Vermote et al., 1997a; Lu et al., 2002). There are noticeable daily variations in
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atmospheric gas concentrations and aerosol volumes; these must be taken into account in
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multitemporal studies. Moreover, the effect of atmospheric processes is spectral-dependent (Teillet
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and Holben, 1993; Cachorro et al., 2000), affecting the magnitude of band ratio operations such as
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vegetation indices (Myneni and Asrar, 1994).
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Different models have been developed to minimize the noise introduced by atmospheric processes
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on the signal received by the satellite, ranging from simple methods based on information contained
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in the image [e.g., Dark Object Subtraction (DOS)-based methods (Chavez, 1996)] to complex
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radiative transference models such as SMAC (Rahman and Dedieu, 1994), 6S (Vermote et al.,
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1997a), MODTRAN (Berk et al., 1999), and ATCOR (Richter, 1996) that simulate the
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atmosphere/light interactions between the sun surface and surface-sensor trajectories.
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In this paper we tested two methods developed to minimize the atmospheric effects on Landsat time
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series: i) one based on a modification of the DOS method that includes some atmospheric
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information (Dark Target Approach -DTA-), and ii) one based on a complex radiative transfer code
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that includes some atmospheric information commonly available in global climate databases and
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some parameters obtained from the images themselves.
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3.4.1. DTA method
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Dark Target Approach (DTA)-based methods assume that some areas of the images have near-zero
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reflectance. Although there are various DTA-based methods (Chavez, 1996), Song et al. (2001)
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reported better results when including in the method atmospheric transmittance and Rayleigh
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scattering. We therefore followed this approach in the present study. Surface reflectance (  ) can be
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obtained as follows (Song et al., 2001):
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 
e
 0 / uv
 ( Lsat   Lhaze )
(3)
( ESUN cos  s e  / u  Edown )
0
0
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where Lhaze is the radiance (W m–2 sr–1 m–1) in areas with zero or near-zero reflectance, 0 is the
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optical depth of the atmosphere, u0 is the cosine of the solar zenith angle, uv is the cosine of the
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zenithal view angle, and Edown is the diffuse irradiance at the surface due to the scattered solar flux
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in the atmosphere (W m–2 m–1). The most critical step is related to the calculation of Lhaze, for
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which there are a number of alternatives (Chavez, 1989, 1996). In this study, we calculated Lhaze
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according to the method of Song et al. (2001) and Schroeder et al. (2006):
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Lhaze  Ldark  0.01 ESUN cos  s e  0 / u0  Edown e  0 / uv / 
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where Ldark is the lowest radiance in the image whose value is taken from at least 1000 pixels
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(Teillet and Fedosejevs, 1995). The estimation of 0 is complex as it requires in situ measurements,
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which are unavailable for the dates of the selected images. We therefore used constant values of 0
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for the entire set of images, based on USA standard values (Dozier, 1989) that are in agreement
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with the values reported by Yang and Vidal (1990) for NE Spain. 0 values can be consulted in Pons
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and Solé-Sugrañes (1994). Edown for a Rayleigh atmosphere was estimated as zero aerosol optical
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depth at 550 nm using the 6S radiative transfer code, following Schroeder et al. (2006).


(4)
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3.4.2. Radiative transfer model
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Among the available codes, we used the Second Simulation of the Satellite Signal in the Solar
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Spectrum (6S) code (Vermote et al., 1997a) to invert the TOA radiance and obtain surface
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reflectance values from TM and ETM+ data assuming lambertian targets. 6S code takes into
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account gaseous transmission and Rayleigh and aerosol transmission and intrinsic reflectance.
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Although 6S enables the inclusion of detailed atmospheric conditions in several layers, as obtained
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from radiosonde data, such data are commonly unavailable. 6S code also enables the user to restrict
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the number of inputs and to assume certain constants. In this study, we used ozone concentrations
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and column water vapor as gaseous inputs for 6S calculations because they are the most critical
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parameters for atmospheric correction (Arino et al., 1997). Daily ozone concentrations (in atm cm–
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2
) can be obtained from Total Ozone Mapping Spectrometer (TOMS) data, at a resolution of 1.25 
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1.00,
from
the
NASA
GSFC
Data
Active
Archive
Center
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(http://toms.gsfc.nasa.gov/ozone/ozone_v8.html). For the period for which ozone data were
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unavailable (between 1994 and 1996), the data were calculated following Van Heuklon (1979) as a
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function of the day of year:
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O3  235  sin 2 (1.25  lat )  [150  40  sin( 0.9856  (d  30))  20  sin( 3  (long  20))]
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where lat is the latitude in degrees, long is the longitude in degrees, and d is the day of year.
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The column water vapor (in g cm–2) is obtained from NCEP reanalysis daily data at a resolution of
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2.5  2.5 (http://dss.ucar.edu/datasets/ds090.0/). Elevation was also included by means of the
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DEM described in Section 3.2.
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The most critical parameter for atmospheric correction is the Aerosol Optical Thickness (AOT).
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Although it is ideal is to obtain simultaneous measurements corresponding to the overpass of the
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satellite, in the study area there are no systematic measurements of this parameter, this being the
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case in most regions of the world. To solve this problem, various methods have been developed to
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obtain reliable estimations of AOT (Liang et al., 1997; Wen et al., 1999). Most such methods are
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based on the relatively strong aerosol radiance effect in low-surface-reflectance areas compared
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with bright areas, as it is then possible to apply an inversion procedure in assessing AOT. In this
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study, we chose to use the Dense Dark Vegetation (DDV) method (Liang et al., 1997). This
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approach is based on the empirical relationship observed between the visible and infrared (IR)
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bands in areas with dark and dense vegetation cover. The method is based on the weak influence of
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aerosols on the mid-IR signal, as most aerosol particles sizes are smaller than the wavelength in this
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part of the spectrum. Kaufman et al. (1996) proposed the following relationship for dark vegetation
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canopies:
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1  0.25 7
3  0.50  7
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where 1 ,  3 , and  7 are the surface reflectances for bands 1, 3, and 7 of Landsat-TM images,
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respectively.
(5)
(6)
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We assumed that each Landsat scene has uniform AOT, and, following Song et al. (2001), dense
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and dark vegetation areas were identified over the whole image ( 1  0.25 and NDVI (Normalized
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Difference Vegetation Index) > 0.1). Average TOA reflectance values were obtained from bands 1,
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3, and 7 in these regions, and band 7 was used to predict surface reflectance values for bands 1 and
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3 according to the empirical relationship stated above. We iteratively ran the 6S code with the
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ozone and water vapor values corresponding to each image and considering a continental aerosol
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model. Following Song et al. (2001), AOT for 550 nm was defined in a range from 0.01 to 2.0 with
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a step size of 0.01 for each iteration. AOT was set at 550 nm when modeled reflectance in bands 1
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and 3 matched the reflectance obtained via the empirical relationship indicated in the equation.
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Values of AOT for images with different dates ranged from 0.048 to 0.96, with generally higher
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values for the August time series. AOT estimations, together with ozone and water vapor values,
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were included as input for the 6S code to atmospherically correct the 28 selected images.
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3.5. Topographic correction
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Another source of artificial noise is the modification of illumination conditions by topography.
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Although the effect of topography on illumination conditions is complex, even including reflections
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from adjacent slopes (Proy et al., 1989), it is usually simplified for the purpose of analysis: shaded
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areas show less than expected reflectance, whereas sunny areas show the opposite pattern.
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Various methods are available to topographically correct satellite imagery (Civco, 1989; Pons and
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Solé, 1994; Gu and Gillespie, 1998). In this paper, we tested two of the most commonly used
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methods: the first assumes a lambertian behavior of the surface and the second considers non-
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lambertian effects.
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The illumination conditions can be modeled following the cosine law of spherical geometry (Civco,
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1989):
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  cos  S cos  n  sin  S sin  n cos(n  S )
(7)
12
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where  is the cosine angle between the solar incident angle and the local surface normal,  S is the
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solar zenith angle,  n is the zenith angle of the normal to the surface, n is the topographic aspect
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angle, and  S is the solar azimuth angle.
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Illumination conditions corresponding to the date and timing of satellite overpass were determined
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for each pixel of the Landsat images using the DEM described in Section 3.2 and by applying the
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formulation stated above. The DEM was used at a spatial resolution of 15 m to guarantee the
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reliability of the derived DTMs (Digital Terrain Models) in the topographic correction procedure.
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To calculate the spatially distributed values of  n and n , we employed the Geographic Information
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System (GIS) MiraMon.
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Based on a lambertian assumption, the reflectance of a horizontal surface  h, can be determined by
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Eq. 8.
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 h,   
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Lambertian models usually overestimate radiance for the high incidence angles of slopes facing
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away from the sun (Vincini and Frazzi, 2003). Simple models have been proposed to take into
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account the non-lambertian properties of the surface. These models are based on Minnaert’s theory,
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in which a constant K, derived empirically for each band, enables the characterization of the non-
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isotropic conditions of the surface. Minnaert’s original proposal has been modified in a number of
337
studies (Colby, 1991; Teillet et al., 1982; Vinzini and Frazzi, 2003); among these methods, the C-
338
correction (Teillet et al., 1982) has shown superior performance because it successfully retains the
339
spectral characteristics of each band and significantly reduces reflectance variability for
340
homogeneous vegetation classes (Riaño et al., 2003). Following C-correction, the reflectance
341
corresponding to a horizontal surface can be obtained as follows:
342
 h,    
 cos  S 

  
 cos  S  c
   c
(8)
 (9)


13
343
where c is obtained empirically from the entire image following c  bk / mk , where    bk  mk  .
344
This procedure removes the common dependence of   to  more efficiently than other methods
345
(Vinzini and Frazzi, 2003).
346
347
3.6. Relative radiometric normalization
348
The commonly assumed simplifications employed in atmospheric and topographic corrections mean
349
that they usually fail to completely remove non-surface noise. To obtain improved temporal
350
homogeneity of satellite imagery, it is common to apply relative normalization between images
351
(e.g., Yuan and Elvidge, 1996; Coppin and Bauer, 1994; Tokola et al., 1998). Relative radiometric
352
normalization is sometimes used as the sole correction procedure (Caselles and López-García,
353
1989), and is usually preferred to atmospheric correction for the purpose of change detection
354
(Olsson, 1995; Janzen et al., 2006). Thus, relative normalization can be applied directly to DNs,
355
radiance, TOA reflectance, or surface reflectance values.
356
Relative normalization is generally based on a linear comparison of image statistics for images
357
obtained on different dates. One of the images, commonly the most recent or least affected by
358
atmospheric effects, is considered as the reference to which the rest of the images are adjusted.
359
Among the methods proposed for relative normalization, linear regression is the most commonly
360
used and widely recommended (Yuan and Elvidge, 1996). The validity of the assumption of a linear
361
relationship between the reference image and the image to be normalized has been confirmed in
362
several studies (Schott et al., 1988; Caselles and López, 1989; Hall et al., 1991; Heo and FitzHugh,
363
2000). This assumption greatly simplifies the normalization process, based on matching the
364
reflectance values of the image to be normalized to the reference image as follows:
365
 reference,  a  b normalise, (10)
366
where a and b are the linear regression parameters.
367
The most critical decision to be made is the sampling of targets/pixels in estimating the regression
368
parameters, as it is necessary to identify constant reflectors between dates and to assume that
14
369
reflectance differences in these targets are due to non-surface noise. Several procedures can be
370
followed in selecting the constant targets, of which the most widely used is based on a visual
371
selection of non-variant targets [Pseudo-Invariant Features (PIFs)] (Schott et al., 1988). These areas
372
should contain minimum amounts of vegetation, be located in relatively flat areas to minimize the
373
effects of illumination differences, and cover a wide range of reflectance values (from dark to bright
374
areas) (Eckhardt et al., 1990). Targets such as sand, asphalt, and water are commonly selected as
375
PIFs (Caselles and García, 1989; Coppin and Bauer, 1994). In this paper, we followed a PIFs
376
normalization (Schott et al., 1988) based on the selection of 280 invariant pixels to be used for the
377
calculation of a and b in each TM and ETM+ band and image. PIFs were identified visually in
378
urban areas and areas of asphalt, water, dark vegetation and sand, for which an invariant reflectance
379
can be assumed over the study period. The last images for the series of March (13/03/2007) and
380
August (01/08/2006) were considered as references, and the rest were matched to these images.
381
We also used a simple radiometric correction method termed Temporally Invariant Cluster (TIC)
382
(Chen et al., 2005), based on the visual identification of centers of high frequency in density
383
scatterplots. The method assumes that these centers correspond to areas in which there are no
384
changes between the reference image and the image to be normalized (Chen et al., 2005). This
385
method is less time-consuming than PIFs because although identification of the TIC centers is not
386
automatic, it is faster than the identification of PIFs. Using this procedure, the values of only two
387
points are needed to obtain the regression line and the a and b coefficients that intersect the centers
388
of high density.
389
Finally, we used an automatic procedure for relative normalization. Automatic techniques have the
390
advantage of being less time-consuming than other techniques and maintaining criteria for target
391
selection. Pseudo-Invariant Feature Regression (PIFR) (Du et al., 2001, 2002) is an automatic
392
method that has provided excellent results in the relative normalization of Landsat images (Janzen
393
et al., 2006; Olthof et al., 2005; Paolini et al., 2006). The PIFR method applies Principal
394
Component Analysis (PCA) between each pair of images to obtain the PIFs. The first component
15
395
represents a least-square regression between overlap areas and the second residual variation. It is
396
recommended to remove outliers prior to model calculation, as they may affect model performance
397
(Heo and FitzHugh, 2000). Although the problem of outliers is minimized in our dataset (as cloud
398
cover was removed previously), the PIFR method removes outliers iteratively on the second
399
principal component to discard changed pixels. We used an iterative process in which the
400
standardized residuals obtained in the first run where used to remove those pixels that exceeded a
401
certain threshold. An initial threshold of +/–1.28 (10% probability according to the normal
402
distribution) was set to remove outliers after the first run. Thresholds corresponding to +/–1%
403
probability were added in subsequent runs to remove pixels to be excluded from the model. The
404
percentage of variance explained by the model was assessed for each run and a minimum of 95%
405
was chosen in selecting the model and the a and b parameters.
406
407
3.7. Validation
408
3.7.1. Calibration and relative normalization
409
To validate the calibration and relative normalization procedures, we identified Pseudo-Invariant
410
(PI) validation pixels for which it is assumed that reflectivity did not vary over time. The
411
identification of these pixels was independent regarding the PIFs used for relative normalization.
412
These pixels were selected from topographically corrected reflectance values and prior relative
413
radiometric normalization using a combined automatic and manual technique. First, the Coefficients
414
of Variation (CV) in each of the six Landsat reflective bands was calculated for each pixel in the
415
time series for March and August. Average CV values were obtained in the different bands of the
416
two series, with the aim of obtaining a unique image that represents the average variability of each
417
pixel independently of the spectral band and season. We considered as PI validation pixels those
418
with average CV values below 0.05. We sampled 300 such pixels, including those in urban areas,
419
dark coniferous forests, water bodies, areas of human infrastructure, and areas of bright sand. None
420
of the selected pixels was subjected to modifications in land cover over the period of interest;
16
421
reflectance values can be considered to have been stable over time. Temporal differences in
422
reflectance values may be attributed to non-surface noise related to factors such as calibration and
423
atmospheric influence.
424
The performance of TM and ETM+ calibration and the cross-calibration between ETM+ and TM
425
images was assessed by comparing the TOA reflectance obtained for the PI validation pixels. The
426
Mean Absolute Error (MAE) was calculated for each spectral band, considering as a reference the
427
last images in the time series of August (08/01/2006) and March (03/13/2007). PI validation pixels
428
were also used to assess the relative radiometric normalization methods.
429
430
3.7.2. Atmospheric correction
431
Atmospheric correction methods described in section 3.4 were applied to geometrically corrected
432
and calibrated/cross-calibrated images. Atmospheric correction applied to these images was
433
evaluated following two different procedures. The first approach involved a comparison of
434
atmospherically corrected surface reflectance values with near-simultaneous MODIS reflectances.
435
Although MODIS includes spectral bands with a number of differences compared with Landsat
436
bands (in terms of bandpasses and spectral response), they are generally considered to be equivalent
437
(Masek et al., 2006). Atmospheric correction of MODIS images can be performed with a greater
438
degree of robustness than Landsat images due to the improved onboard capabilities of MODIS
439
(Vermote et al., 1997b, 2002). MODIS surface reflectances were obtained from the MOD09GAV5
440
daily reflectance dataset (http://lpdaac.usgs.gov/modis/mod09ghkv4.asp) for the same region as that
441
covered by the Landsat dataset. The Landsat–MODIS comparison period is 2001–2007, for which 5
442
MODIS images were near nadir. Landsat TOA and surface reflectance values obtained from DTA
443
and 6S were aggregated to 500 m spatial resolution to match the MODIS reflectance dataset.
444
Atmospheric correction was also evaluated by comparing Landsat surface reflectance with field
445
surface reflectances obtained simultaneous with the overpass of Landsat 5. For this purpose, we
446
selected a homogeneous area characterized by xeric vegetation and very low degrees of herbaceous
17
447
and shrub cover. The study area (0°40'W, 41°27'N) covers 28.2 ha and is a plane platformal area
448
unaffected by topographic influence on illumination conditions. On 08/01/2006 and 03/13/2007 we
449
used an Ocean Optics USB2000 field spectroradiometer to obtain a random sample of field
450
reflectance measures (~200) simultaneous with the overpass of the Landsat-5 satellite (+/– 0.5 h).
451
This spectroradiometer makes 10 IFOV observations over the 400–900 nm bandwidth with a 0.3
452
nm sampling interval. A spectralon reference panel was used to obtain reflectance values. To ensure
453
a high signal-to-noise ratio, integration time was adjusted according to illumination conditions, and
454
each measure was calculated as the mean of 20 individual spectra. The spectral curves were
455
integrated to simulate the TM bands, taking into account the relative spectral response of each TM
456
band (Teillet et al., 2001) and compared with the TOA reflectance and DTA and 6S surface
457
reflectances obtained from Landsat imagery. We applied a one-way analysis of variance to
458
determine if DTA and 6S surface reflectance are significantly different from the field surface
459
reflectances. Tamhane’s pairwise comparisons test was used to produce the multiple comparisons
460
where the variances are unequal. The significance threshold was set at 0.05.
461
462
3.7.3. Topographic correction
463
Topographic corrections were assessed via temporal comparisons of the reflectance trajectories
464
before and after topographic corrections in an homogeneous Pinus halepensis forest located on
465
slopes of 20 in the central Ebro Valley (semi-arid conditions, average annual precipitation = 320
466
mm, 41°43'N,0°28'W). The forest has the same density in south-facing and north-facing slopes. Ten
467
pixels were selected in a south-facing slope of the forest, besides additional ten pixels in a north-
468
slope facing to assess the effects of topographic correction procedures on the derived trajectories.
469
470
471
472
473
474
475
4. Results
4.1. Sensor calibration and cross-calibration
Figure 2 shows the average TOA reflectance values for each image and band corresponding to the
time series of August for the PI validation pixels. Figure 2 also compares original TOA reflectances
18
476
obtained from ETM+ images with TOA reflectances obtained from ETM+/TM cross-calibration,
477
employing the equations of Vogelmann et al. (2001). The temporal trends reveal that comparisons
478
of TOA reflectance values between TM and ETM+ are highly problematic without prior cross-
479
calibration. In general, ETM+ images provide higher TOA reflectance values than TM-TOA
480
reflectance. Differences are greater for IR bands than visible bands, especially bands 4 and 5
481
(15.3% and 27.2% higher for average ETM+ than TM TOA reflectance in the August time series,
482
respectively). A similar behavior has been found for the March time series (results not shown).
483
The application of cross-calibration to Landsat 7-ETM+ images improves the temporal stability of
484
the series and comparisons between TOA reflectances. The procedure reduces TOA reflectance
485
values for most of the bands in ETM+ images, showing a better match with TM-TOA reflectances.
486
Table 2 shows the MAE obtained from time series of each band. The error is calculated for each
487
image relative to the reference image in each dataset (01/08/2006 for August and 13/03/2007 for
488
March). Original ETM+ calibrated bands show higher MAE values than cross-calibrated bands,
489
both in visible and IR bands. These results support the validity of employing cross-calibrated ETM+
490
TOA reflectances in the following radiometric correction steps.
491
492
493
494
4.2. Atmospheric correction
Figure 3 shows the average values of TOA reflectances and DTA and 6S surface reflectances for
495
the PI validation pixels for the time series of August. DTA and 6S atmospheric-correction methods
496
yield different results in terms of the magnitude of reflectance values and temporal stability. DTA
497
shows clearly higher reflectance values than TOA reflectances and 6S surface reflectances. In
498
contrast, 6S shows lower values than TOA reflectances for visible bands but higher values for IR
499
bands. The behavior is similar in the March time series (not shown).
500
TOA reflectances are commonly higher than surface reflectances in visible bands because of
501
aerosol scattering and atmospheric Rayleigh scattering. Therefore, for visible bands it is expected
502
that atmospheric correction would reduce TOA reflectance values and that the reduction would be
503
spectrally dependant, being more pronounced in the blue band. The 6S method leads to reduced
19
504
reflectance values relative to TOA reflectances for visible bands (1–3), with the reduction being
505
greatest for band 1, in which aerosol scattering is more pronounced. In contrast, the DTA method
506
always overcorrects TOA reflectances for the visible bands.
507
The behaviors of the near- and middle-IR bands are expected to be different from those of the
508
visible bands because they are unaffected by aerosol scattering and because atmosphere molecular
509
absorption plays a major role. Given that absorption by gasses reduces the radiance received by the
510
sensor, it is expected that atmospheric correction leads to increased surface reflectance values for
511
near- and middle-IR bands relative to TOA reflectances. Both 6S and DTA surface reflectances
512
show higher values than TOA reflectances; however, DTA overcorrects for the influence of water
513
vapor absorption in band 4, as the influence of gas absorption in band 4 is similar to that in bands 5
514
and 7 (Arino et al., 1997).
515
Figure 5 shows, as an example, the relationship between TOA reflectances and DTA and 6S surface
516
reflectances derived for bands 1, 3, 4, and 7 in the image of 07/26/2001, versus the corresponding
517
MODIS surface reflectances for the same day in the region shown in Figure 4. Band 1 shows large
518
differences between TOA reflectances and MODIS surface reflectances. The DTA method
519
overcorrects surface reflectances, mainly in highly reflective areas, and 6S also shows higher values
520
than MODIS reflectances, although there is greater agreement relative to estimates obtained using
521
TOA and DTA. For bands 3 and 4, TOA reflectances underestimate the true values and DTA
522
clearly overestimates the surface reflectances relative to estimates obtained using MODIS. In
523
contrast, 6S and MODIS show strong agreement for most of the bands. For band 7, the difference
524
between TOA, 6S, and DTA surface reflectances and MODIS reflectances is lower than that
525
observed for the other bands; this is explained by the lower atmospheric influence in the mid-IR
526
region. The pattern apparent in the image of 26/07/2001 is also observed in other images taken
527
between 2001 and 2007, showing a high agreement between 6S and MODIS surface reflectances.
528
Although MODIS and Landsat images are corrected with the same 6S model, the used AOT
20
529
estimations are independent, showing high performance of the model to derive physically robust
530
surface reflectances under different input data.
531
Table 3 shows the average MAE obtained when comparing the MODIS surface reflectance and the
532
corresponding TOA reflectance and surface reflectances obtained from DTA and 6S in the five
533
analyzed images. 6S surface reflectances show a better match to MODIS reflectances than TOA
534
reflectances and DTA surface reflectances, especially for the visible and near-IR bands, in which
535
atmospheric aerosol and Rayleigh scattering are relatively pronounced. In contrast, for mid-IR
536
bands there is little difference in the MAE values obtained using the DTA and 6S methods; this
537
finding is attributed to the minor role of aerosols over these spectral regions.
538
Figure 5 shows a box plot of reflectance values collected in the field on 01/08/2006 and 13/03/2007
539
at the Mediana site (see Section 3.7.2). Also shown are simultaneous Landsat-TM TOA reflectances
540
and DTA and 6S surface reflectances collected over the same area. Field surface reflectance shows
541
greater spatial variability than that collected by satellite imagery. This phenomenon is commonly
542
observed in multi-scalar studies that employ remote sensing data, as finer spatial scales result in
543
greater spatial variability in reflectance values (Foody, 1991; Foody and Curran, 1994).
544
Independently of the range of reflectance values, the average 6S surface reflectances show a better
545
match with the field reflectances than DTA surface reflectances. The one-way analysis of variance
546
and the multiple comparisons between means do not statically show differences between the field
547
surface reflectances and the 6S surface reflectances in any of the four bands analysed for the two
548
day of measurements. On the contrary, the same analysis indicates statistically significant
549
differences between the DTA surface reflectances and the field surface reflectances for the different
550
bands. DTA, as previously shown with MODIS data, tends to overestimate the surface reflectance
551
values. The observed behavior is similar in the March and August images, and is the same as that
552
observed using MODIS data; no seasonal effects are inferred in the atmospheric correction process.
21
553
These results indicate that the 6S method is superior to the DTA method in terms of providing
554
physically coherent and robust surface reflectance values; accordingly, the 6S method is used for
555
subsequent radiometric corrections of the time series.
556
557
558
559
4.3. Topographic correction
Figure 6 shows the temporal evolution of March and August average reflectance values for bands 3
560
and 4 obtained in the Pinus halepensis forest indicated in section 3.7.3. The figure compares south-
561
facing and north-facing slopes, showing the time series of 6S surface reflectances and the surface
562
reflectance corresponding to a horizontal surface, as obtained via the Lambertian and C-correction
563
methods. In the series for March, non-topographically corrected reflectances show marked
564
differences between south-facing and north-facing slopes, mainly for band 4. Lambertian and non-
565
lambertian topographic correction improves adjustment of reflectances between south-facing and
566
north-facing slopes. The behavior observed after topographic correction provides a better match
567
with observed data, as in early spring the vegetation activity of these forests is very low, and no
568
differences should be observed in the reflectance values of north-facing and south-facing slopes.
569
Regarding the type of topographic correction, in March, a month in which the sun elevation angle is
570
low, band 3 shows similar results for the lambertian and non-lambertian models, whereas band 4
571
shows contrasting results. C-correction shows a good adjustment between north-facing and south-
572
facing slope reflectances, indicating the greater capacity of the model in correcting relief
573
perturbations under low sun-elevation angles.
574
The series for August shows higher reflectance values for south-facing than north-facing slopes.
575
These differences are less pronounced than those observed for March because of the higher sun-
576
elevation angle. Topographic correction leads to a reduction in the differences between north-facing
577
and south-facing slopes in band 3, with only minor differences observed between the lambertian
578
and non-lambertian models. For band 4, both topographic correction methods provide higher
579
reflectance values for north-facing than south-facing slopes. This pattern is more consistent with
580
real vegetation behavior because Pinus halepensis forests in the central Ebro Valley suffer water
22
581
stress during the summer months, and a north-facing exposure favors vegetation activity due to
582
lower evapotranspiration rates.
583
It is not only absolute reflectance values that are affected by topography: temporal variability is also
584
affected by topographic correction. This is clearly shown for the March time series, in which the
585
temporal variability of band 4 reflectance changes noticeably as a function of topographic
586
correction. Topographic effects are therefore spectrally dependant, and the temporal variability
587
observed in the series is strongly affected by topography during the season with a low sun-elevation
588
angle, as observed in March.
589
590
591
592
4.4. Relative radiometric normalization
Table 4 shows the Average MAE for each band relative the reference image in the dataset
593
(08/01/2006 for August and 03/13/2007 for March) in the time series for 6S, PIFs, PIFR, and TIC
594
reflectances for the PI validation pixels. For the August series, the PIFs method improves the
595
temporal homogeneity of the series for visible bands (1–3) relative to non-normalized reflectances,
596
but for near- and mid-IR bands (4–7) it yields similar errors to those in non-normalized images.
597
PIFR and TIC yield improved temporal homogeneity of the reflectance series, mainly for IR bands.
598
There are few differences in the MAE values between the two methods, although the PIFR method
599
generally yields lower MAE values.
600
For March, the differences between the non-normalized and normalized images are less pronounced
601
than those for August. PIFs yields inferior results compared with non-normalized images for bands
602
2–7. Although the PIFR method does not provide the best temporal homogeneity in certain bands, it
603
provides the best accuracy in terms of the average MAE errors between bands (0.0095 for non-
604
normalized images, 0.0083 for TIC, and 0.0075 for the PIFR method).
605
606
607
4.5. Case studies
23
608
Here, we provide a number of examples that illustrate the role of radiometric correction procedures
609
applied to time series of reflectance-based indices in evaluating land cover changes, forest
610
regeneration after forest fires, and ecosystem response to climate variability.
611
612
613
4.5.1. Case study 1: Detection of forest fire and subsequent recovery
614
The Zuera hills of the central Ebro Valley are residual Tertiary platforms with soils characterized by
615
a strong presence of limestone and gypsum and semiarid conditions (average annual precipitation =
616
475 mm). The area is covered entirely by Pinus halepensis forest that experiences occasional forest
617
fires. The most important historical fire burnt a total of 3093 ha in June of 1995. Following the fire,
618
no reforestation activities were undertaken, and the area was left to regenerate naturally. After 12
619
years of regeneration, a dense vegetation cover has formed. Most of the burnt area now supports a
620
high density of Pinus halepensis trees (235000 trees/ha upon some north-facing slopes with deep
621
soils), with ages between 10 and 12 years, heights between 1 and 2.5 m, and basal diameters
622
between 2 and 7 cm. The few areas in which Pinus halepensis has not regenerated are covered by
623
xeric shrubs.
624
The Normalized Burn Ratio (NBR) index (Miller and Yool, 2002) was calculated for each pixel and
625
image of the August series from TOA reflectances, DTA and 6S surface reflectances, and relative
626
radiometric normalized reflectances following the PIFs, PIFR, and TIC methods:
627
NBR 
628
where 4 and 7 are the reflectance values for bands 4 and 7.
629
This index is particularly effective in determining those areas affected by fire and burn severity
630
(Cocke et al., 2005; Wimberly and Reilly, 2007), and it has been used here to compare the different
631
radiometric correction procedures in the case that the occurrence of forest fire must be detected.
632
To analyze vegetation regeneration after forest fire, we used NDVI (Rousse et al., 1974), which is
633
widely used for this purpose (e.g., Díaz-Delgado et al., 2003; Viedma et al., 1997):
 4   7 (10)
4  7
24
 4  3
 4  3
634
NDVI 
635
where 4 and 3 are the reflectance values for bands 4 and 3.
636
Figure 7 shows the temporal evolution of average and standard deviation August NBR and NDVI
637
values obtained from TOA reflectances, DTA and 6S surface reflectances and PIFs, PIFR, and TIC
638
normalized reflectances for the burned areas in 1995. The fire event is well identified in 1995 by
639
NBR, regardless of the radiometric correction procedure. The six time series show a sharp decrease
640
in NBR values in 1995 and a progressive recovery to pre-fire values between 1997 and 2006. There
641
are no noticeable differences between time series as a function of the radiometric correction
642
procedure, and the magnitudes of NBR values are similar in all cases if directly calculated from
643
TOA reflectances or atmospherically corrected and normalized surface reflectances. Nevertheless,
644
noticeable differences have been found in the standard deviation values, because PIFR and PIFs
645
methods reduce the variability found of the NBR index of each image.
646
Time series of NDVI show important differences depending on the applied radiometric correction.
647
For 1995, all of the methods show a decrease in NDVI values after the fire. The behaviors of the
648
series show a marked change following the fire. The Pinus halepensis forests of the central Ebro
649
Valley show a highly stable interannual leaf vegetation activity, given the high resistance of these
650
trees to drought (Baquedano and Castillo, 2007). The occurrence of summertime hydric stress
651
caused by the calcareous and gypsum substrate and high evapotranspiration rates (Vicente-Serrano
652
et al., 2007) means that no vegetation activity is recorded in the underbrush species; only the Pinus
653
halepensis trees, which have the deepest root systems, maintain vegetation activity in summer. In
654
the period before the fire event, TOA, 6S, DTA, and PIFs reflectance-based NDVI values show
655
large variability, whereas the NDVI time series calculated from PIFR and TIC normalized
656
reflectances show a high degree of stability, more in accordance with reality. After the fire, the
657
NDVI values obtained from TOA reflectances are similar to those before the fire, indicating the
658
immediate (and therefore unrealistic) recovery of vegetation after the fire. NDVI values obtained
659
from 6S surface reflectances show a similar pattern. In contrast, NDVI values obtained after relative
25
660
normalization show a gradual increase following the fire; this pattern is observed for all three of the
661
applied methods (PIFs, TIC, and PIFR). Given that only Pinus halepensis trees are active in
662
summer and given the gradual recovery of tree density observed in the field, the pattern obtained
663
from PIFs, PIFR, and TIC reflectance is strongly consistent with the observed evolution of the
664
forest. In agreement with NBR, PIFR and PIFs methods also reduce the variability of the NDVI
665
values regarding 6S and TIC methods.
666
667
668
669
670
4.5.2. Case study 2: Identification of areas of dry land converted to irrigated land
671
drylands to highly modernized irrigated lands, intensively cultivated in the summer months for
672
corn, rice, alfalfa, and various vegetables. Figure 8 shows the average August NDVI values for a
673
sample of 58 pixels transformed in 1993 from dry herbaceous cultivation (winter cereals: wheat and
674
barley) to irrigated cultivation in the Monegros II irrigated areas. The series were obtained from
675
TOA reflectances, DTA and 6S surface reflectances, and relative normalized reflectances using the
676
PIFR, PIFs, and TIC methods.
677
For all radiometric correction procedures, the dry land converted to irrigated land in 1993 is readily
678
identified in the time series. The post-conversion trends of the various time series are similar for the
679
different methods, although the NDVI series derived from TOA reflectances does not record the
680
commonly observed interannual variability in vegetation activity within irrigated lands that arises
681
from the change in cultivation type. Greater differences are observed between the series in the
682
period prior to conversion. Cereals in drylands are harvested in June, meaning that in August the
683
soil is devoid of vegetation cover, and no interannual variability is expected in NDVI values.
684
Therefore, the variability in NDVI values obtained from TOA, DTA and 6S surface reflectances
685
prior to irrigation are interpreted to represent non-surface noise. The differences in the standard
686
deviation values are less important among the different methods than those observed in the previous
687
case study, but the values are in general lower for the PIFR and PIFs procedures.
During the 1990s, hundreds of hectares of land in the central Ebro Valley were transformed from
26
688
689
690
691
692
4.5.3. Case study 3: Response of steppes and areas of dryland cultivation to variability in
precipitation
Precipitation plays an important role in generating interannual variability in herbaceous cover in
693
steppes and areas of wheat and barley cultivation within the Ebro Valley, and as such has been
694
widely analyzed in field studies (Austin et al., 1998) and studies based on NOAA-AVHRR images
695
(Vicente-Serrano, 2007). The maximum vegetation activity of steppe regions is recorded in March–
696
April, being mainly dependent on soil moisture, which is highly variable and determined by the
697
precipitation accumulated over winter (Austin et al., 1999). A strong correlation was expected
698
between reflectance-based vegetation and/or moisture indices and wintertime precipitation. We
699
calculated a vegetation water index [Normalized Difference Infrared Index (NDII)] (Hunt and
700
Rock, 1989), which is a normalized ratio, using Landsat bands 4 and 5:
701
NDII 
702
where 4 and 5 are the reflectance values for bands 4 and 5, respectively. Because band 5 is located
703
in a strong water-absorption region, reflectance is inverse to leaf water content: low index values
704
indicate low water content. NDII was calculated from TOA reflectances, DTA and 6S surface
705
reflectance, and PIFs, PIFR, and TIC normalized surface reflectances for the steppes and dry
706
herbaceous lands of the centre of the Ebro valley.
707
Figure 9 shows the temporal evolution of the average and standard deviation NDII values for the
708
March time series and the November–February precipitation recorded at the observatory in
709
Zaragoza, which is representative of that for the entire region. The choice of radiometric correction
710
not only affects the magnitude of NDII values, but also the degree of variability in the series. NDII
711
values obtained from TOA reflectances and DTA and 6S surface reflectance show similar
712
behaviors. Although the maximum and minimum NDII values correspond to those years in which
713
maximum and minimum wintertime precipitation was recorded, respectively; the degree of
714
agreement between NDII and wintertime precipitation is relatively low for NDII values obtained
715
from relative normalized reflectances. Pearson coefficient of correlation values between NDII and
 4  5
 4  5
(11)
27
716
wintertime precipitation are R = 0.70, R = 0.71, and R = 0.73 (p < 0.01) for NDII values derived
717
from TOA and 6S and DTA surface reflectances, respectively.
718
The NDII values obtained from relative normalized surface reflectances show noticeable
719
differences between the PIFs, PIFR, and TIC methods. The PIFs approach shows the highest
720
temporal variability, with high NDII values recorded for years without excessive humidity, such as
721
1990. Although the correlation between NDII and wintertime precipitation is stronger than that
722
using TOA and 6S surface reflectance values (R = 0.74, p < 0.01), it is weaker than that obtained
723
using the PIFR and TIC methods (R = 0.77 and R = 0.81, p < 0.01, respectively). The NDII values
724
obtained from PIFR and TIC reflectance show good agreement with the wintertime precipitation
725
series. It is also observed in the time series of standard deviations that the PIFR method reduces the
726
deviation around the means in comparison with other methods, reducing the residual scatter in this
727
very spatially homogeneous region.
728
729
730
731
5. Discussion and conclusions
In this paper, we assessed the performances of different radiometric-correction procedures in
732
obtaining physically reliable and homogeneous time series of Landsat images. We tested the
733
different steps involved in the process, including the calibration of sensors, atmospheric and
734
topographic corrections, and relative radiometric normalization. We demonstrated that each step is
735
critical and necessary if the physical robustness and homogeneity of the time series are to be
736
maintained.
737
For sensor calibration, we documented the difficulties that exist in directly comparing the
738
reflectance values obtained from TM and ETM+ sensors. In general, higher reflectance values are
739
obtained from ETM+ than TM reflectances for the majority of bands, in accordance with previous
740
studies (Teillet et al., 2006; Vogelmann et al., 2001). Although most studies based on Landsat data
741
consider ETM+ and TM reflectances to be comparable, with the outcome that cross-calibration
742
procedures are rarely applied, we demonstrated that a change in satellite causes a noticeable
743
instability in the time series. Moreover, important magnitude differences exist as a function of
28
744
Landsat band, which may have noticeable effects when band-based operations are applied, such as
745
the calculation of vegetation indices. These results indicate the need of TM/ETM+ cross-calibration
746
to improve the temporal homogeneity of Landsat series. Despite the limitations of Landsat data
747
provided by ESA in 1G formats, the application of cross-calibration coefficients developed by
748
Vogelmann et al. (2001) shows great potential in reducing the temporal inhomogeneities related to
749
differences in sensor response, although not in all bands (e.g., bands 1 and 7).
750
We found that the temporal homogeneity of the series is unaffected by the method employed for
751
atmospheric correction. This may suggest that the atmospheric correction step is not critical in the
752
case that the objective is to create multitemporal Landsat series. Other studies have reported similar
753
results; for example, Schroeder et al. (2006) found no differences in the temporal stability of
754
Landsat series obtained after applying DTA and 6S atmospheric correction techniques. The authors
755
reported the same error for the two procedures (RMSE = 0.024) and no difference in TOA
756
reflectance (RMSE = 0.023). Moreover, we have shown that the application of atmospheric
757
correction only fails to produce homogeneous multitemporal series of Landsat surface reflectances.
758
This has been observed for both simple atmospheric correction methods such as DTA and complex
759
models such as 6S. Based on these results, we can confirm that the temporal stability of a series
760
must be a secondary criterion when applying atmospheric correction: the physical robustness of
761
reflectance values must be the prime factor. The subsequent application of relative normalization
762
techniques is required to solve the problem of temporal homogeneity.
763
We have shown that the DTA method overcorrects reflectance values for all bands, regardless of the
764
atmospheric conditions and season. This overcorrection has a noticeable impact on the calculation
765
of vegetation indexes such as NDVI, resulting in a general underestimation and leading to
766
subsequent problems in extracting biophysical properties and undertaking multitemporal studies of
767
vegetation cover (Song and Woodcock, 2003). Therefore, the use of TOA reflectances and DTA
768
surface reflectances is inappropriate for multitemporal analyses for which an accurate estimation of
769
vegetation parameters or robust vegetation indices are required.
29
770
Improved physical performance was obtained in using the 6S model, which reduces the magnitude
771
of reflectance values for visible bands (1–3) and increases the magnitude in near- and mid-IR bands
772
(4–7), as is physically expected due to the contrasting influences on the signal of aerosols and the
773
molecular atmosphere (Arino et al., 1997). Here, we used indirect AOT estimates from the image
774
itself. This is the most critical parameter used in the model (Ouaidrari and Vermote, 1999), as some
775
authors have reported contrasting surface reflectance values depending on whether direct AOT
776
measures or indirect estimations were used (Song et al., 2001; Schroeder et al., 2006). Nevertheless,
777
other studies have reported similar 6S surface reflectances regardless of whether AOT was obtained
778
indirectly from the DDV method or via direct measurements (Song and Woodcock, 2003). Masek et
779
al. (2006) compared DDV-AOT estimates with AERONET observations at 18 sites in North
780
America, showing a reasonable agreement between image-based AOT estimates and observations
781
(R = 0.81). In our study, a comparison of quasi-simultaneous MODIS surface reflectances and two
782
samples of surface reflectances obtained in the field also demonstrated the strong performance of
783
the 6S model in estimating surface reflectance values. Given that MODIS images are
784
atmospherically corrected using AOT estimates, which are obtained with a high degree of accuracy
785
(Chu et al., 2002), our results also indicate the excellent accuracy of the AOT estimates obtained for
786
the Ebro Valley using Landsat data.
787
Topographic normalization is seldom taken into account in multitemporal processing protocols
788
applied to Landsat data. We found noticeable differences in the temporal evolution of surface
789
reflectance values between north-facing and south-facing slopes upon a relatively flat, directly
790
related to illumination effects. Our results suggest that the spatial comparability of images is not
791
assured without the application of topographic corrections, which ensure that the evolution of
792
surface reflectance values between north-facing and south-facing slopes are comparable. In
793
considering topographic correction methods, there are few differences between the lambertian and
794
non-lambertian procedures. Nevertheless, the non-lambertian C-correction provides a better match
795
between the temporal evolution of surface reflectance values for north-facing and south-facing
30
796
slopes. Given the readily satisfied requirements for topographic correction (requiring only a digital
797
elevation model at the same spatial resolution of the Landsat images), it is highly recommended that
798
this step is included in multitemporal processing protocols, especially for areas with highly complex
799
topography in which the sun-surface-sensor geometry is also complex.
800
We have shown that relative radiometric normalization is essential to ensure the homogeneity of
801
multitemporal Landsat datasets. Previous studies have compared the temporal stability of
802
multitemporal Landsat datasets before and after relative radiometric normalization. In most of these
803
studies, normalization was applied without prior atmospheric correction (e.g., Olsson, 1993; Yuan
804
and Elvidge, 1996; Olthof et al., 2005), indicating the irrelevance of this step in ensuring the
805
temporal homogeneity of the series. Recent studies have also compared the homogeneity of
806
multitemporal atmospherically corrected Landsat datasets before and after relative radiometric
807
normalization (Janzen et al., 2006; Schroeder et al., 2006), confirming the need to apply this step
808
because atmospheric correction alone is unable to remove the non-surface noise in the series. The
809
results of this paper are in accordance with the findings of these studies; nevertheless, it is also
810
obtained evidence of the need for the prior cross-calibration and atmospheric correction of images.
811
In contrast, accurate biophysical parameters cannot be obtained from inversion models (González-
812
Sanpedro et al., 2008), and it is not possible to calculate the true magnitude of band-based indices
813
such as NDVI (Myneni and Asrar, 1994).
814
Among the three methods tested in applying relative radiometric corrections, the most manual—that
815
based on the visual identification of PIFs—provided the worst results. The temporal stability of the
816
series was improved when using the PIFR and TIC methods, which yielded similar results, although
817
the low time and cost requirements of PIFR and its automatic nature and automatic operation make
818
it preferable over TIC. Moreover, the PIFR method reduced the deviation scatter around the mean
819
values in more depth than the other methods for the different vegetation and moisture indices
820
indicating a higher success in the radiometric correction given the spatial homogeneity of the case
821
study analysed.
31
822
In summary, for analyzing abrupt vegetation changes such as forest fire events or land cover
823
changes based on different band indices, it is unnecessary to apply complex radiometric correction
824
procedures to Landsat datasets: the use of simple TOA reflectances provides good results.
825
Nevertheless, when the spectral signal is not sufficiently strong to minimize noise from real
826
vegetation dynamics and change, it is necessary to undertake highly accurate radiometric
827
corrections. To analyze vegetation dynamics before and after events that involve change, such as
828
the rate of vegetation recovery after a fire or the role of climate variability on vegetation activity, it
829
is necessary to accurately ensure the homogeneity of multitemporal Landsat datasets via complete
830
radiometric correction procedures that include sensor calibration and cross-calibration, atmospheric
831
correction, topographic correction and relative radiometric normalization using objective statistical
832
techniques. Failure to follow such a procedure may mean that the analyzed processes are not
833
recognizable and that the obtained results are invalid.
834
835
836
837
Acknowledgements
838
Spanish Commission of Science and Technology and FEDER, and “Programa de grupos de
839
investigación consolidados” financed by the Aragón Government. Authors would like to thank to
840
the four anonymous reviewers for their helpful comments.
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
This work has been supported by the research project CGL2005-04508/BOS, financed by the
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1217
Table 1. Dates of the Landsat 5-TM and Landsat 7-ETM+ images used in this study
March
Date
11/03/1989
30/03/1990
06/03/1993
09/03/1994
28/03/1995
17/03/1997
20/03/1998
23/03/1999
17/03/2000
10/03/2003
07/03/2005
13/03/2007
August
Sensor
TM
TM
TM
TM
TM
TM
TM
TM
ETM+
ETM+
TM
TM
Date
20/08/1984
07/08/1985
13/08/1987
02/08/1989
24/08/1991
10/08/1992
29/08/1993
03/08/1995
24/08/1997
14/08/1999
08/08/2000
26/07/2001
30/08/2002
27/08/2004
14/08/2005
01/08/2006
Sensor
TM
TM
TM
TM
TM
TM
TM
TM
TM
TM
ETM+
ETM+
ETM+
TM
TM
TM
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
Table 2. Mean Average Error for each band relative to the reference image in the dataset
(08/01/2006 for August and 03/13/2007 for March)
August
March
Original Cross-calibrated Original Cross-calibrated
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
0.0142
0.0122
0.0112
0.0111
0.0169
0.0106
0.0132
0.0110
0.0090
0.0062
0.0088
0.0095
0.0115
0.0083
0.0106
0.0123
0.0146
0.0090
0.0125
0.0070
0.0087
0.0074
0.0073
0.0076
Table 3. Average Mean Absolute Error (MAE) between MODIS and Landsat reflectances. Average
MAE values were obtained for the following images: 07/26/2001, 08/30/2002, 03/10/2003,
03/07/2005, and 08/01/2006.
TOA
BAND 1
BAND 2
BAND 3
BAND 4
BAND 5
BAND 7
DTA
0.06
0.03
0.04
0.08
0.10
0.09
6S
0.16
0.08
0.07
0.07
0.04
0.05
0.02
0.03
0.02
0.03
0.05
0.04
1237
1238
1239
40
1240
1241
1242
1243
1244
Table 4. Average MAE for each band relative the reference image in the dataset (08/01/2006 for
August and 03/13/2007 for March) in the time series for 6S, PIFs, PIFR, and TIC reflectances.
AUGUST
band 1
band 2
band 3
band 4
band 5
band 7
MARCH
band 1
band 2
band 3
band 4
band 5
band 7
6S
0.0080
0.0114
0.0113
0.0121
0.0123
0.0130
6S
0.0131
0.0076
0.0096
0.0097
0.0079
0.0088
PIFs
0.0045
0.0057
0.0063
0.0104
0.0110
0.0125
PIFs
0.0063
0.0115
0.0112
0.0104
0.0110
0.0100
TIC
0.0060
0.0076
0.0065
0.0071
0.0063
0.0042
TIC
0.0038
0.0076
0.0114
0.0081
0.0100
0.0086
PIFR
0.0048
0.0054
0.0069
0.0054
0.0050
0.0046
PIFR
0.0050
0.0080
0.0093
0.0042
0.0079
0.0107
41
1245
1246
1247
1248
1249
Figure 1. Location of path 199, row 31 and the spatial distribution of the main land-cover types
42
1250
1251
0.5
0.5
Band 1
Band 2
0.4
Reflectance
Reflectance
0.4
Original
Cross-calibrated
0.3
0.2
0.1
0.3
0.2
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
0.0
0.5
Band 3
Band 4
0.4
Reflectance
Reflectance
0.4
0.3
0.2
0.1
0.3
0.2
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
0.0
L7-ETM+
L5-TM
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
0.5
0.5
Band 7
Band 5
0.4
Reflectance
0.4
Reflectance
L5-TM
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
0.5
0.3
0.2
0.3
0.2
0.1
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
L7-ETM+
L5-TM
0.0
0.0
1252
1253
1254
1255
1256
L7-ETM+
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
Figure 2. Time series of August average TOA reflectance values for the PI validation pixels for
each band in the TM and ETM+ images and cross-calibrated ETM+ images.
43
0.5
0.5
Band 1
0.4
Reflectance
0.4
Reflectance
Band 2
TOA
6S
DTA
0.3
0.2
0.1
0.3
0.2
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
0.5
Band 3
Band 4
0.4
Reflectance
Reflectance
0.4
0.3
0.2
0.1
0.3
0.2
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
0.0
L7-ETM+
L5-TM
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
0.5
0.5
Band 7
Band 5
0.4
Reflectance
0.4
Reflectance
L5-TM
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
0.5
0.3
0.2
0.3
0.2
0.1
0.1
L5-TM
L7-ETM+
L5-TM
L5-TM
L7-ETM+
L5-TM
0.0
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
1257
1258
1259
1260
1261
L7-ETM+
0.0
84 85 87 89 91 92 93 95 97 99 00 01 02 04 05 06
19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20
Figure 3. Time series (August) of average TOA reflectances and 6S and DTA surface reflectances
for the PI validation pixels.
44
1262
1263
1264
1265
1266
1267
Figure 4. Relationship between TOA (left) and DTA (middle) and 6S (right) surface reflectances
from the 07/26/2001 Landsat-ETM+ image and corresponding MODIS-derived surface reflectances
45
01/082006
13/03/2007
0.5
0.5
0.3
0.2
0.1
0.1
TOA
DTA
TOA
DTA
0.1
TOA
DTA
6S
0.0
FIELD
TOA
DTA
FIELD
6S
0.4
Reflectance
0.4
0.3
0.2
DTA
6S
0.2
0.0
0.0
TOA
0.3
0.1
0.1
FIELD
6S
Band 4
0.4
0.2
DTA
Band 3
0.5
0.3
TOA
0.6
0.5
0.0
FIELD
0.2
0.5
0.1
0.0
1268
1269
1270
1271
1272
1273
0.3
0.1
Band 4
Reflectance
Reflectance
0.2
0.2
0.6
Band 3
0.4
0.3
0.3
6S
0.6
0.5
0.4
0.0
FIELD
6S
0.4
Reflectance
FIELD
0.5
0.1
0.0
0.0
0.5
Reflectance
Reflectance
Reflectance
0.2
Reflectance
0.4
0.4
Band 2
Band 1
Band 2
Band 1
0.3
0.6
0.6
FIELD
TOA
DTA
6S
FIELD
TOA
DTA
6S
Figure 5: Box plot of field surface reflectances measured at the Mediana site on 01/08/2006 and
13/03/2007, and TOA reflectances and DTA and 6S surface reflectance values. The median, first
and third quartiles are indicated in the shaded boxes, and the bars represent 10th and 90th centiles.
1274
46
March
0.04
A)
B)
C)
Band 3
0.03
0.02
0.01
0.18
0.00
Band 4
0.16
0.14
0.12
0.10
0.08
89 90 93 94 95 97 98 99 00 03 05 07
19 19 19 19 19 19 19 19 20 20 20 20
89 90 93 94 95 97 98 99 00 03 05 07
19 19 19 19 19 19 19 19 20 20 20 20
89 90 93 94 95 97 98 99 00 03 05 07
19 19 19 19 19 19 19 19 20 20 20 20
August
0.07
Band 3
0.06
A)
B)
0.05
C)
Northern slope
Southern slope
0.04
0.03
0.02
0.24
0.01
Band 4
0.22
0.20
0.18
0.16
0.14
19
8
19 4
8
19 5
8
19 7
8
19 9
9
19 1
9
19 2
9
19 3
9
19 5
9
19 7
9
20 9
0
20 0
0
20 1
0
20 2
0
20 4
05
19
8
19 4
8
19 5
8
19 7
8
19 9
9
19 1
9
19 2
9
19 3
9
19 5
9
19 7
9
20 9
0
20 0
0
20 1
0
20 2
0
20 4
05
1275
1276
1277
1278
1279
1280
1281
19
8
19 4
8
19 5
8
19 7
8
19 9
9
19 1
9
19 2
9
19 3
9
19 5
9
19 7
9
20 9
0
20 0
0
20 1
0
20 2
0
20 4
05
0.12
Figure 6. Band 3 and Band 4 6S surface reflectance (A), reflectance of a horizontal surface
(lambertian model) (B), and reflectance of a horizontal surface (C-correction model) (C) for a Pinus
halepensis forest within the semi-arid central Ebro Valley. Reflectance values represent the average
of 10 pixels in a south-facing slope and 10 pixels in a north-facing slope
47
1282
0.8
TOA
0.6
TOA
0.6
NDVI
NBR
0.4
0.2
0.4
0.0
0.2
-0.2
0.8
DTA
0.6
DTA
0.6
NDVI
NBR
0.4
0.2
0.4
0.0
0.2
-0.2
0.8
6S
0.6
NDVI
NBR
0.4
0.2
0.0
6S
0.6
0.4
-0.2
0.2
0.8
PIFs
0.6
PIFs
NDVI
NBR
0.4
0.2
0.0
0.6
0.4
-0.2
0.2
0.8
0.4
NDVI
NBR
0.6
0.2
0.0
0.4
PIFR
PIFR
-0.2
0.2
0.8
TIC
0.6
TIC
0.4
NDVI
NBR
0.6
0.2
0.6
0.4
0.0
-0.2
0.2
19
84 985 987 989 991 992 993 995 997 999 000 001 002 004 005 006
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
19
84 985 987 989 991 992 993 995 997 999 000 001 002 004 005 006
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
Standard deviation (NBR)
0.20
0.18
0.16
0.14
0.12
PIFR
TIC
PIFs
6S
0.10
0.08
0.06
19
84
19
85
19
87
19
89
19
91
19
92
19
93
19
95
19
97
19
99
20
00
20
01
20
02
20
04
20
05
20
06
Standard deviation (NDVI)
0.20
1283
1284
1285
1286
1287
1288
1289
0.18
0.16
0.14
0.12
0.10
0.08
0.06
19
84
19
85
19
87
19
89
19
91
19
92
19
93
19
95
19
97
19
99
20
00
20
01
20
02
20
04
20
05
20
06
Figure 7. Average and standard deviation August NBR and NDVI values obtained from TOA
reflectances and DTA, 6S, PIFs, PIFR, and TIC surface reflectances for the burned areas of Montes
de Zuera in 1995. Time series of standard deviation values in the 6S, PIFs, PIFR and TIC methods
are also shown.
48
1.0
0.8
0.8
0.6
0.6
NDVI
NDVI
1.0
0.4
0.2
0.2
TOA
PIFs
0.0
1.0
1.0
0.8
0.8
0.6
0.6
NDVI
NDVI
0.0
0.4
0.2
0.4
0.2
DTA
0.0
PIFR
0.0
1.0
1.0
0.8
0.8
0.6
0.6
NDVI
NDVI
0.4
0.4
0.2
0.4
0.2
6S
0.0
19
TIC
0.0
84 985 987 989 991 992 993 995 997 999 000 001 002 004 005 006
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
19
84 985 987 989 991 992 993 995 997 999 000 001 002 004 005 006
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
Standard deviation (NDVI)
0.30
0.25
0.20
0.15
PIFR
TIC
PIFs
6S
0.10
0.05
0.00
84
19
1290
1291
1292
1293
1294
1295
1296
1297
85
19
87
19
89
19
91
19
92
19
93
19
95
19
97
19
99
19
00
20
01
20
02
20
04
20
05
20
06
20
Figure 8. Average NDVI and standard deviation obtained from TOA reflectances and DTA, 6S,
PIFs, PIFR, and TIC surface reflectances for those parts of the Monegros II area transformed from
non-irrigated arable lands to irrigated lands. Time series of standard deviation values in the 6S,
PIFs, PIFR and TIC methods are also shown. OJO PIFs
49
1298
0
-0.1
-0.2
-200
-0.2
0.3
300
0.2
100
0.0
0
-0.1
-100
-0.2
0.2
-200
300
6S
0.1
200
100
0.0
0
-0.1
-100
-0.2
-200
19
90
19
93
19
94
19
95
19
97
19
98
19
99
20
00
20
03
20
05
20
07
-200
300
PIFR
NDII
0.1
-100
0.1
200
100
0.0
0
-0.1
-100
-0.2
-200
0.2
400
TIC
0.1
NDII
200
Precipitation
DTA
0.2
Precipitation
-100
100
0.0
Precipitation
0
200
300
Precipitation
0.0
300
PIFs
0.1
NDII
100
-0.1
NDII
200
Precipitation
NDII
0.1
NDII
0.2
300
TOA
Precipitation
0.2
200
0.0
100
0
-0.1
-100
-0.2
-200
19
90
19
93
19
94
19
95
19
97
19
98
19
99
20
00
20
03
20
05
20
07
Standard deviation (NDII)
0.20
0.15
PIFR
TIC
PIFs
6S
0.10
0.05
0.00
1299
1300
1301
1302
1303
1304
1305
1306
Figure 9. Average and standard deviation NDII obtained from TOA reflectances and DTA, 6S,
PIFs, PIFR, and TIC surface reflectances for steppes and non-irrigated arable lands of Monegros
(bold lines) and total wintertime precipitation (November–February) (thin lines). Time series of
standard deviation values in the 6S, PIFs, PIFR and TIC methods are also shown. OJO PIFs
50