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

• Published in: International journal of digital earth Vol. 10; no. 9; pp. 944 - 977
• Main Authors: Teluguntla, Pardhasaradhi, Thenkabail, Prasad S., Xiong, Jun, Gumma, Murali Krishna, Congalton, Russell G., Oliphant, Adam, Poehnelt, Justin, Yadav, Kamini, Rao, Mahesh, Massey, Richard
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.09.2017; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Agricultural history; Agricultural land; Agronomy; Algorithms; Australia; automated cropland classification algorithms; Automation; Classification; Continuous cropping; cropland; Croplands; Data; fallow; Food; Food security; Geography; History; humans; Imaging techniques; machine learning algorithms; Mapping; Matching; Mathematical models; methodology; moderate resolution imaging spectroradiometer; MODIS; normalized difference vegetation index; Products; quantitative spectral matching techniques; Remote sensing; Seasonal variations; Seasonality; Seasons; Security; Time series; time series analysis; Vigor; Water use; Australia
• ISSN: 1753-8947; 1753-8955; 1753-8955
• DOI: 10.1080/17538947.2016.1267269

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