A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: Application in the Brazilian Amazon region


Accurate mapping of land cover at continental to global scales is currently limited by our ability to exploit the spatial, temporal, and radiometric characteristics of the available satellite data. Many ecologically and biogeochemically important landscape features are spatially extensive, but occur at scales much smaller than the ∼1-km footprint of wide-swath, polar orbiting radiometers. This is especially true for land cover changes associated with human activities. Satellite instruments that offer the appropriate spatial detail have much smaller swaths and longer repeat times, resulting in compositing intervals that are too large to resolve the time scales of these changes. In addition, the cost and effort associated with acquisition and processing of high-resolution data for large areas is often prohibitive. Methods for taking advantage of information contained in multiple-scale observations by combining data from high-resolution and moderate resolution sensors are thus of great current interest.

In this paper, we retrieve land cover distributions in two different parts of the Brazilian Amazon region by estimating relationships between land cover fractions derived from 30-m resolution ETM+ and reflectance data from ∼1-km resolution MODIS and MISR. The scaling relationships are derived using a Bayesian-regularized artificial neural network (ANN) and compared to results using linear unmixing (LU). We explore the simultaneous use of two significant independent variables in terrestrial optical remote sensing, wavelength, and sun-sensor geometry, by combining nadir-adjusted MODIS reflectances in seven bands (VIS-SWIR) with multiangular (−71° to +71°) bidirectional reflectance data from MISR. This research was motivated by evidence from modeling and field studies demonstrating that: (a) the angular dependence of reflectance (e.g., from MISR) contains information about the structural composition of canopies that is complementary to the wavelength dependence; and (b) the SWIR portion of the spectrum (e.g., from MODIS) is sensitive to canopy moisture and shading conditions and, therefore, to the successional status of the ecosystem. This case study, using the Bayesian artificial neural network with combined MODIS-MISR data to estimate sub-pixel land cover fractions, yielded a quantitative improvement over spectral linear unmixing of single-angle, multispectral data. Our results suggest potential for broad-scale applicability despite a number of challenges related to tropical atmospheric conditions.


Earth Sciences, Earth Systems Research Center

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Remote Sensing of Environment



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