An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data

Li, Fuqin; Jupp, David L.B.; Reddy, Shanti; Lymburner, Leo; Mueller, Norman; Tan, Peter J.; Islam, Anisul

doi:10.1109/jstars.2010.2042281

Cited by 111 publications

(65 citation statements)

References 23 publications

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“…To achieve topographic normalization, the sloping reflectance derived from the sensor must be converted to a flat reflectance based on atmospheric and topographic correction, as expressed in Equation (8).There are two main methods for validating the merit of the topographic correction: visual inspection and quantitative accuracy analysis. Figure 5 shows the result of the partially enlarged images for a clear visual interpretation of the topographic normalization.…”

Section: Validation and Discussionmentioning

confidence: 99%

“…Therefore, four actual angles on tiled surfaces must be changed in the new coordinate system based on the DEM, which can be calculated according to the literature [5]. Considering the small variation in the Landsat sensor view angle, the observed zenith and azimuth are assumed to be zero for each pixel [8]. The calculations for the actual observed zenith and azimuth angles on a given inclined surface can be simplified as:…”

Section: Improved Topographic Normalizationmentioning

confidence: 99%

“…Masek et al [7] derived the Landsat surface reflectance product by atmospheric correction based on the 6S methodology, which is the basis of the Landsat Climate Data Record (CDR) distributed by USGS. Some researchers recognize that the radiance received by the satellite is also distorted due to the surface BRDF or sun-surface-sensor geometry for each image pixel, and several atmospheric-correction-coupled BRDFs are utilized for Landsat images [8]. In recent years, with the development of moderate-resolution satellites, many space-based sensors (such as the Moderate Resolution Imaging Spectroradiometer, MODIS) provide atmospheric products and multi-angle observations, which yield reliable data on land surface BRDFs [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Zhang

Wen

et al. 2015

Remote Sensing

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Abstract:In rugged terrain, the accuracy of surface reflectance estimations is compromised by atmospheric and topographic effects. We propose a new method to simultaneously eliminate atmospheric and terrain effects in Landsat Thematic Mapper (TM) images based on a 30 m digital elevation model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products. Moreover, we define a normalized factor of a Bidirectional Reflectance Distribution Function (BRDF) to convert the sloping pixel reflectance into a flat pixel reflectance by using the Ross Thick-Li Sparse BRDF model (Ambrals algorithm) and MODIS BRDF/albedo kernel coefficient products. Sole atmospheric correction and topographic normalization were performed for TM images in the upper stream of the Heihe River Basin. The results show that using MODIS atmospheric products can effectively remove atmospheric effects compared with the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model and the Landsat Climate Data Record (CDR). OPEN ACCESSRemote Sens. 2015, 7 6559Moreover, superior topographic effect removal can be achieved by considering the surface BRDF when compared with the surface Lambertian assumption of topographic normalization.

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Section: Validation and Discussionmentioning

confidence: 99%

Section: Improved Topographic Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Zhang

Wen

et al. 2015

Remote Sensing

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“…Another method is land-type-based (LB) BRDF fitting using aerial images, which are commonly acquired by an optical scanner with a wide field of view and one observation geometry per pixel, such as the airborne HYMAP [8] and airborne compact airborne spectrographic imager (CASI) [20,21]. The BRDF's shape [22], generally extracted from the relatively homogeneous pixels of MODIS or POLDER BRDF product, is required in LB BRDF fitting as prior knowledge of one land type [23,24]. Thus, the fitting performance of airborne BRDF is limited by the quality of the prior knowledge, as well as its spatiotemporal scale discrepancy.…”

Section: Introductionmentioning

confidence: 99%

Development of a High Resolution BRDF/Albedo Product by Fusing Airborne CASI Reflectance with MODIS Daily Reflectance in the Oasis Area of the Heihe River Basin, China

et al. 2015

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A land-cover-based linear BRDF (bi-directional reflectance distribution function) unmixing (LLBU) algorithm based on the kernel-driven model is proposed to combine the compact airborne spectrographic imager (CASI) reflectance with the moderate resolution imaging spectroradiometer (MODIS) daily reflectance product to derive the BRDF/albedo of the two sensors simultaneously in the foci experimental area (FEA) of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), which was carried out in the Heihe River basin, China. For each land cover type, an archetypal BRDF, which characterizes the shape of its anisotropic reflectance, is extracted by linearly unmixing from the MODIS reflectance with the assistance of a high-resolution classification map. The isotropic coefficients accounting for the differences within a class are derived from the CASI reflectance. The BRDF is finally determined by the archetypal BRDF and the corresponding isotropic coefficients. Direct comparisons of the cropland archetypal BRDF and CASI OPEN ACCESSRemote Sens. 2015, 7 6785 albedo with in situ measurements show good agreement. An indirect validation which compares retrieved BRDF/albedo with that of the MCD43A1 standard product issued by NASA and aggregated CASI albedo also suggests reasonable reliability. LLBU has potential to retrieve the high spatial resolution BRDF/albedo product for airborne and spaceborne sensors which have inadequate angular samplings. In addition, it can shorten the timescale for coarse spatial resolution product like MODIS.

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“…For example, BRDF effects cannot be directly derived from Landsat data due to the small variation in illumination and viewing angles and infrequent revisits of the sensor (Li et al, 2010). Other sensors with similar or higher spatial resolutions than the 30m provided by Landsat also typically have small variations in angular sampling and infrequent revisits limit their ability to derive BRDF effects.…”

Section: Introductionmentioning

confidence: 99%

Characterising Vegetated Surfaces Using Modis Multiangular Satellite Data

McCamley

Grant

Jones

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Bidirectional Reflectance Distribution Functions (BRDF) seek to represent variations in surface reflectance resulting from changes in a satellite's view and solar illumination angles. BRDF representations have been widely used to assist in the characterisation of vegetation. However BRDF effects are often noisy, difficult to interpret and are the spatial integral of all the individual surface features present in a pixel. This paper describes the results of an approach to understanding how BRDF effects can be used to characterise vegetation. The implementation of the Ross Thick Li Sparse BRDF model using MODIS is a stable, mature data product with a 10 year history and is a ready data source. Using this dataset, a geometric optical model is proposed that seeks to interpret the BRDF effects in terms of Normalised Difference Vegetation Index (NDVI) and a height-to-width ratio of the vegetation components. The height-to-width ratio derived from this model seeks to represent the dependence of NDVI to changes in view zenith angle as a single numeric value.The model proposed within this paper has been applied to MODIS pixels in central Australia for areas in excess of 18,000 km 2 . The study area is predominantly arid and sparsely vegetated which provides a level of temporal and spatial homogeneity. The selected study area also minimises the effects associated with mutual obscuration of vegetation which is not considered by the model.The results are represented as a map and compared to NDVI derived from MODIS and NDVI derived from Landsat mosaics developed for Australia's National Carbon Accounting System (NCAS). The model reveals additional information not obvious in reflectance data. For example, the height-to-width ratio is able to reveal vegetation features in arid areas that do not have an accompanying significant increase in NDVI derived from MODIS, i.e. the height-to-width ratio reveals vegetation which is otherwise only apparent in NDVI derived from the higher resolution Landsat NCAS dataset.

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An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data

Cited by 111 publications

References 23 publications

Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Improved Topographic Normalization for Landsat TM Images by Introducing the MODIS Surface BRDF

Development of a High Resolution BRDF/Albedo Product by Fusing Airborne CASI Reflectance with MODIS Daily Reflectance in the Oasis Area of the Heihe River Basin, China

Characterising Vegetated Surfaces Using Modis Multiangular Satellite Data

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