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The normalized difference vegetation index (NDVI) time-series data, derived from satellite sensors, has been used to support land cover change detection and monitor crops successfully, but further applications are hindered by residual noise in the NDVI data. Methods for reducing noise and constructing high-quality NDVI time-series data sets can be broadly grouped into three general types, including...
Crop growth stage is important information for decision making in many related agricultural sectors. In-time accurate estimation of crop growth stage is desired. Kernel-fitting of time series vegetation indices have shown potential in estimating crop growth stage while tolerant to noisy data and missing data. The challenge to apply such models is dealing with current year when incomplete data are...
Early and accurate reporting of crop condition is desired for its importance and sensitivity to commodity exchange markets and other related sectors. Traditional approach to create the weekly report on crop condition relies on the survey of selected farmers. The results are subjective and inconsistent throughout the crop growing season. Selected remote sensing approaches have been implemented and...
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