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II. LITERATURE REVIEW
2.1 Remote Sensing for Rice Plant Growth Stage
2.1.1 Rice Plant Growth Stage Mapping
Remote Sensing data provide timely, accurate, synoptic and objective estimation of crop growing conditions or crop growth for developing yield models
and issuing yield forecasts at a range of spatial scales Dadhwal, 2004. The advantage of remote sensing methods is the ability to provide repeated measures
from a field without destructive sampling of the crop, which can provide valuable information for precision agriculture applications Hatfield et al., 2010.
Remote sensing techniques play important roles in crop identification, acreage and production estimation, disease and stress detection, soil and water
resources characterization Patil et al., 2002. Remote sensing technique is dependant from reflectance response of object. To discriminate different rice plant
growth stage, we have to differentiate the signature for each growth stage in a region from representative samples at specific times. However, some crop types
have quite similar spectral responses at equivalent growth stages Yang et al., 2008.
Supervised classification algorithms aim at predicting the class label. Supervised classification is one of the most commonly undertaken analyses of
remotely sensed data. The output of a supervised classification is effectively a thematic map that provides a snapshot representation of the spatial distribution of
a particular theme of interest such as land cover Imdad et al., 2010. In general, a supervised classification algorithm consists of two phases: 1
the learning phase, in which the algorithm identifies a classification scheme based on spectral signatures obtained from “training” sites having known class labels
e.g. land cover types, and 2 the prediction phase, in which the classification scheme is applied to other locations with unknown class membership Samaniego
et al., 2008.
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