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Abstract
Multi‐temporal compositing of SPOT‐4 VEGETATION imagery over tropical regions was tested to produce spatially coherent monthly composite images with reduced cloud contamination, for the year 2000. Monthly composite images generated from daily images (S1 product, 1‐km) encompassing different land cover types of the state of Mato Grosso, Brazil, were evaluated in terms of cloud contamination and spatial consistency. A new multi‐temporal compositing algorithm was tested which uses different criteria for vegetated and non‐vegetated or sparsely vegetated land cover types. Furthermore, a principal components transformation that rescales the noise in the image—Maximum Noise Fraction (MNF)—was applied to a multi‐temporal dataset of monthly composite images and tested as a method of additional signal‐to‐noise ratio improvement. The back‐transformed dataset using the first 12 MNF eigenimages yielded an accurate reconstruction of monthly composite images from the dry season (May to September) and enhanced spatial coherence from wet season images (October to April), as evaluated by the Moran's I index of spatial autocorrelation. This approach is useful for land cover change studies in the tropics, where it is difficult to obtain cloud‐free optical remote sensing imagery. In Mato Grosso, wet season composite images are important for monitoring agricultural crop cycles.
Acknowledgments
João M. B. Carreiras's work was partially developed at Instituto Nacional de Pesquisas Espaciais (INPE, Brazil), as a contribution to the Global Land Cover 2000 (GLC 2000) project and to the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA). This work was funded by a doctoral grant from the Ministério da Ciência e Tecnologia, Fundação para a Ciência e a Tecnologia, Portugal (Ref. PRAXIS XXI/BD/21507/99). VEGETATION images were obtained in the framework of the GLC 2000 and GBA 2000 projects of the Joint Research Centre (JRC, European Commission).