Next Article in Journal
Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin
Previous Article in Journal
Calibrations and Wind Observations of an Airborne Direct-Detection Wind LiDAR Supporting ESA’s Aeolus Mission
Previous Article in Special Issue
Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(12), 2057;

Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia

Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
MapTailor Geospatial Consulting, 53113 Bonn, Germany
International Centre for Agricultural Research in Dry Areas (ICARDA), Cairo 11431, Egypt
Authors to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 5 December 2018 / Accepted: 11 December 2018 / Published: 18 December 2018
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
Full-Text   |   PDF [6658 KB, uploaded 18 December 2018]   |  


Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM), and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC). View Full-Text
Keywords: Central Asia; Xinjiang; Aral Sea Basin; cropland mapping; Google Earth Engine; Landsat; reference time series Central Asia; Xinjiang; Aral Sea Basin; cropland mapping; Google Earth Engine; Landsat; reference time series

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Hao, P.; Löw, F.; Biradar, C. Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia. Remote Sens. 2018, 10, 2057.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top