Sampling the surface bidirectional reflectance distribution function (BRDF): 1. Evaluation of current and future satellite sensors

Barnsley, Michael J.; Strahler, A.H.; Morris, K; Müller, Jan-Peter

doi:10.1080/02757259409532205

Cited by 95 publications

(31 citation statements)

References 51 publications

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“…Furthermore, bidirectional corrections require satellite measurements at di erent viewing geometries with the surface conditions remaining constant to maximize the accuracy of the inversion procedure (e.g. Barnsley et al 1994 ). Such measurements are generally not available and this approach may become practically feasible only after the launch of EOS when the bidirectional space is sampled simultaneously by MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multi-Angle Imaging Spectroradiometer) (Martonchik et al 1998 ).…”

Section: Coarse Resolution Datamentioning

confidence: 99%

Land cover mapping of large areas from satellites: Status and research priorities

Cihlar¹

2000

International Journal of Remote Sensing

538

220

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Abstract. Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and ' ne' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies-including pixel or scene compositing, radiometric corrections, classi cation and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processingand classi cation in relation to forthcoming improvements in data sources as well as new requirements for land cover information. Introduction and objectiveLand cover, i.e. the composition and characteristics of land surface elements, is key environmental information. It is important for many scienti c, resource management and policy purposes and for a range of human activities. It is an important determinant of land use and thus of value of land to the society. Land cover varies at a range of spatial scales from local to global, and at temporal frequencies of days to millennia. As the need for environmental planning and management became important, an accompanying call for land cover information emerged in parallel.Land cover mapping is a product of the development of remote sensing, initially through aerial photography (Colwell 1960 ). This is because 'viewing' large areas repeatedly is necessary for acquiring information about land cover. For the same reason, land cover mapping has been perhaps the most widely studied problem employing satellite data, beginning with Landsat 1. However, most of the studies using ' ne' resolution data (i.e. 20-100 m) were methodological in nature, exploring various information extraction techniques and applying these over limited areas. Applications over large areas were hampered by the lack of suitable technology, an absence of a user community with a strong need for such information, a lack of appropriate analysis methodologies, and the cost of data. Thus, at the global level, land cover data sets compiled from ground surveys or various national sources (Mathews 1983, Olson et al. 1983 were, for a number of years, the major source of information.

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Section: Coarse Resolution Datamentioning

confidence: 99%

Land cover mapping of large areas from satellites: Status and research priorities

Cihlar¹

2000

International Journal of Remote Sensing

538

220

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“…For general circulation models or global climate models (GCMs) it is important to use good estimates of the surface hemispheric albedo [Barnsley et al, 1994]. This parameter is often estimated by assuming that the underlying surface is Lambertian (isotropic) and using the nadir reflectance as measured from an airborne or satellite instrument as a surrogate.…”

Section: •Joint Center For Earth Systems Technology University Of Mamentioning

confidence: 99%

Airborne spectral measurements of surface‐atmosphere anisotropy during the SCAR‐A, Kuwait oil fire, and TARFOX experiments

Soulen¹,

King²,

Tsay³

et al. 2000

J. Geophys. Res.

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“…Generally, sufficient and well-distributed observations are needed to accurately retrieve BRDF from multi-angular remotely sensed data [9]. However, constrained by the observation capacity of sensors, the orbital characteristics of observation platforms [10], clouds, and their shadows, most spacecraft or satellite remote sensors cannot collect sufficient and Tile h17v06 is located in northwestern Africa, and most of the area of this tile is desert. The MODIS Land Cover Type product (MCD12Q1), which maps global land cover using spectral and temporal information derived from MODIS [24], contains multiple classification schemes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Extracting Prior BRDF from MODIS BRDF Data

et al. 2016

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Abstract:Many previous studies have attempted to extract prior reflectance anisotropy knowledge from the historical MODIS Bidirectional Reflectance Distribution Function (BRDF) product based on land cover or Normalized Difference Vegetation Index (NDVI) data. In this study, the feasibility of the method is discussed based on MODIS data and archetypal BRDFs. The BRDF is simplified into six archetypal BRDFs that represent different reflectance anisotropies. Five-year time series of MODIS BRDF data over three tiles are classified into six BRDF archetype classes according to the Anisotropy Flat indeX (AFX). The percentage of each BRDF archetype class in different land cover classes or every 0.1-NDVI interval is determined. Nadir BRDF-Adjusted Reflectances (NBARs) and NDVIs simulated from different archetypal BRDFs and the same multi-angular observations are compared to MODIS results to study the effectiveness of the method. The results show that one land cover type, or every 0.1-NDVI interval, contains all the potential BRDF shapes and that one BRDF archetypal class makes up no more than 40% of all data. Moreover, the differences between the NBARs and NDVIs simulated from different archetypal BRDFs are insignificant. In terms of the archetypal BRDF method and MODIS BRDF product, this study indicates that the land cover or NDVI is not necessarily related to surface reflectance anisotropy.

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Sampling the surface bidirectional reflectance distribution function (BRDF): 1. Evaluation of current and future satellite sensors

Cited by 95 publications

References 51 publications

Land cover mapping of large areas from satellites: Status and research priorities

Land cover mapping of large areas from satellites: Status and research priorities

Airborne spectral measurements of surface‐atmosphere anisotropy during the SCAR‐A, Kuwait oil fire, and TARFOX experiments

Analysis of Extracting Prior BRDF from MODIS BRDF Data

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