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• W2289825559 abstract "This study employs sub-pixel classification methods to estimate crop acreage using low resolution satellite images. Linear mixture and Neural Network Methods examined the relationship between the value of Normalized Difference Vegetation Index (NDVI) in the context of Iran and crop acreage that reported by administrative office. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of sub-pixel heterogeneity, the application of traditional hard classification approach to MODIS may result in significant errors in crop area estimation. Fars province in south of Iran was selected as test zone, because of the cover type of the large majority of agricultural fields. Neural network model and Linear Mixture model were investigated and result in area fraction images (AFIs). The AFIs contain for each 250 m pixel the estimated area proportions occupied by the different cover types (crops or other land use). The algorithm was trained with both of reference data and in situ data which collected by GPS in Marvdasht District. For the major classes (winter wheat, maize and other crops) the obtained acreage estimates showed good agreement with the true values (NNR2390% and LMMR2385%). These methods seem attractive for wide-scale, regional area estimation in the countries that appropriate data are not available." @default.
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• W2289825559 date "2007-01-01" @default.
• W2289825559 modified "2023-09-26" @default.
• W2289825559 title "Comparison between Linear and Non-Linear Crop Acreage Estimation Methods" @default.
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