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An Automated Cropland Classification Algorithm (ACCA) for Tajikstan by Combining Landsat, MODIS, and Secondary Data

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Prasad S Thenkabail, and Zhuoting Wu, 2012, An Automated Cropland Classification Algorithm (ACCA) for Tajikstan by Combining Landsat, MODIS, and Secondary Data: Remote Sensing, v. 4, no. 10, p. 2890-2918.

Summary

Abstract: The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data [...]

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Communities

  • Global Croplands and Their Water Use for Food Security in the Twenty-first Century
  • John Wesley Powell Center for Analysis and Synthesis

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Remote Sens. 2012, 4(10), 2890-2918; doi:10.3390/rs4102890

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DOI https://www.sciencebase.gov/vocab/term/528e9a2ce4b05d51c7038afe 10.3390/rs4102890

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noteThenkabail, P.S., and Wu, Z. (2012). An Automated Cropland Classification Algorithm (ACCA) for Tajikstan by Combining Landsat, MODIS, and Secondary Data: Remote Sensing, 4(10), 2890-2918. doi: 10.3390/rs4102890
journalRemote Sensing
parts
typeVolume
value4
typeNumber
value10
typePages
value2890-2918
citationTypeJournal

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