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

Teluguntla, P and Thenkabail, P S and Xiong, J and Gumma, M K and Congalton, R G and Oliphant, A and Poehnelt, J and Yadav, K and Rao, M and Massey, R (2017) Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data. International Journal of Digital Earth. pp. 1-34. ISSN 1753-8947

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Abstract

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 water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

Item Type: Article
Divisions: Research Program : Innovation Systems for the Drylands (ISD)
CRP: CGIAR Research Program on Dryland Cereals
CGIAR Research Program on Dryland Systems
CGIAR Research Program on Grain Legumes
CGIAR Research Program on Water, Land and Ecosystems
Uncontrolled Keywords: Croplands; Food security; Automated cropland classification algorithms; Machine learning algorithms; quantitative spectral matching techniques; Australia
Subjects: Others > Agriculture
Others > Remote Sensing
Depositing User: Mr Ramesh K
Date Deposited: 19 Jan 2017 09:34
Last Modified: 18 Apr 2017 07:29
URI: http://oar.icrisat.org/id/eprint/9859
Official URL: http://dx.doi.org/10.1080/17538947.2016.1267269
Projects: UNSPECIFIED
Funders: UNSPECIFIED
Acknowledgement: The authors gratefully acknowledge the project funding provided by the National Aeronautics and Space Administration (NASA) grant number: NNH13AV82I through its MEaSUREs (Making Earth System Data Records for Use in Research Environments) initiative. The United States Geological Survey (USGS) provided supplemental funding from other direct and indirect means through its Land Change Science (LCS), and Land Remote Sensing (LRS) programs as well as its Climate and Land Use Change Mission Area. Finally, authors would like to thank Prof. Alfredo Huete and Dr Rakhesh Devadas, University of Technology Sydney, Sydney for the comprehensive field work support and coordination in Australia.This work was supported by NASA MEaSUREs (grant number NNH13AV82I); U.S. Geological Survey provided supplemental funding from other direct and indirect means through its Land Change Science (LCS), and Land Remote Sensing (LRS) programs as well as its Climate and Land Use Change Mission Area.
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