Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human wa...

Full description

Bibliographic Details
Main Authors: Pardhasaradhi Teluguntla, Prasad S. Thenkabail, Jun Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh Rao, Richard Massey
Format: Article
Language:English
Published: Taylor & Francis Group 2017-09-01
Series:International Journal of Digital Earth
Subjects:
croplands
food security
automated cropland classification algorithms
machine learning algorithms
quantitative spectral matching techniques
australia
Online Access:http://dx.doi.org/10.1080/17538947.2016.1267269

Internet

http://dx.doi.org/10.1080/17538947.2016.1267269

Similar Items