Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification

By I. El-Magd, T. Tanton. 2003

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Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems (2011, background): “Crop types have mostly been distinguished using high spatial resolution data recorded by multispectral sensors such as onboard Landsat (El-Magd and Tanton 2003 , Price 2003 ) or IRS (Panigrahy and Sharma 1997 ).”
A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI (2013, methodology): “The general classification methods can be realized by maximum likelihood supervised classification [35], decision tree classification [3,16], support vector machine classification [31], artificial neural network classification [30,37], etc.”
Remote Sensing of Irrigated Agriculture: Opportunities and Challenges (2010, methodology): “Common methods include multi-stage classification [31,41], unsupervised clustering [31,33,42], density slicing with thresholds [7,43,44], and decision tree classifications [45].”
Remote Sensing and Cropping Practices: A Review (2018, methodology): “Consequently, automated classification approaches were developed, including unsupervised classification [101], single or multi-stage supervised classification [102], decision tree [103], and supervised learning models such as Random Forest or Support Vector Machines [104,105].”