Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia
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 mean...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/10/12/2057 |
| _version_ | 1818083788779945984 |
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| author | Pengyu Hao Fabian Löw Chandrashekhar Biradar |
| author_facet | Pengyu Hao Fabian Löw Chandrashekhar Biradar |
| author_sort | Pengyu Hao |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-10T19:43:34Z |
| format | Article |
| id | doaj.art-b3fddbde829e4b3ca1167deb1b8a1a5e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T19:43:34Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b3fddbde829e4b3ca1167deb1b8a1a5e2022-12-22T01:35:56ZengMDPI AGRemote Sensing2072-42922018-12-011012205710.3390/rs10122057rs10122057Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central AsiaPengyu Hao0Fabian Löw1Chandrashekhar Biradar2Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaMapTailor Geospatial Consulting, 53113 Bonn, GermanyInternational Centre for Agricultural Research in Dry Areas (ICARDA), Cairo 11431, EgyptMapping 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).https://www.mdpi.com/2072-4292/10/12/2057Central AsiaXinjiangAral Sea Basincropland mappingGoogle Earth EngineLandsatreference time series |
| spellingShingle | Pengyu Hao Fabian Löw Chandrashekhar Biradar Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia Remote Sensing Central Asia Xinjiang Aral Sea Basin cropland mapping Google Earth Engine Landsat reference time series |
| title | Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia |
| title_full | Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia |
| title_fullStr | Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia |
| title_full_unstemmed | Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia |
| title_short | Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia |
| title_sort | annual cropland mapping using reference landsat time series a case study in central asia |
| topic | Central Asia Xinjiang Aral Sea Basin cropland mapping Google Earth Engine Landsat reference time series |
| url | https://www.mdpi.com/2072-4292/10/12/2057 |
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