Reconstructing Seasonal Variation of Landsat Vegetation Index Related to Leaf Area Index by Fusing with MODIS Data
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AbstractIn the development of an empirical relationship between the leaf area index (LAI) and the vegetation index (VI), the infrequency of the medium resolution VI often makes it difficult, sometimes impossible, to find VI observations acquired close to the LAI measurement date. To overcome this dilemma, this paper presents a method, named reduced simple ratio (RSR), to reconstruct seasonal time series of a VI at the Landsat resolution. Each RSR time series is represented by a double logistic (D-L) curve with seven unknown parameters. The methodology solves these parameters using a multi-objective optimization method by blending frequent MODIS observations with Landsat observations acquired at a few dates (usually fewer than seven) in a year. We tested the reconstructing approach in a boreal forest in Canada and a cropland area in Australia. The reconstructed Landsat RSR compared well with the observed RSR even when only two Landsat images were used for reconstruction, and better accuracy was achieved when more Landsat images were used. Ground LAI measurements were taken at a date not coincident with any of the Landsat dates in the Canada study area. Results of LAI retrieval showed that the measured LAI had a higher correlation with the reconstructed RSR at the measurement date than with the observed Landsat RSR at the three acquisition dates.
All Author(s) ListZhang HK, Chen JM, Huang B, Song HH, Li YR
Journal nameIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year2014
Month3
Day1
Volume Number7
Issue Number3
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages950 - 960
ISSN1939-1404
eISSN2151-1535
LanguagesEnglish-United Kingdom
KeywordsDouble logistic; fusion; leaf area index; multi-objective optimization
Web of Science Subject CategoriesEngineering; Engineering, Electrical & Electronic; Geography, Physical; GEOGRAPHY, PHYSICAL; Imaging Science & Photographic Technology; IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY; Physical Geography; Remote Sensing; REMOTE SENSING

Last updated on 2020-31-03 at 00:49