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Remote Sens. 2018, 10(12), 2057; https://doi.org/10.3390/rs10122057

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

1
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
MapTailor Geospatial Consulting, 53113 Bonn, Germany
3
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)
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Abstract

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
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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.

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