Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework
Abstract: Monitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making. This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted for broad landcover types in one of the areas of interest (AOIs). We implemented a pixel-wise k-nearest neighbor classification model with DTW distance to identify paddy rice pixels. Standard rice phenological profiles of the SAR VH band were defined by ground monitoring of a sample rice field. Based on rice planting maps and the standard phenological profiles, rice phenological phases were estimated by pattern matching strategy with the sub-DTW algorithm. Experiments on six counties in Northeast China presented promising results. The overall producer and user accuracy reached 92.9 and 91.9% for rice mapping, respectively. The mean root mean square error (RMSE) for phenology estimation was 3.5 days. Rice planting and rice phenology maps were generated for the six AOIs. The phenological variances of the AOIs implied the effects of climate and rice cultivars on phenological development.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework ; volume:14 ; number:1 ; year:2022 ; pages:414-428 ; extent:15
Open Geosciences ; 14, Heft 1 (2022), 414-428 (gesamt 15)
- Urheber
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Wang, Mo
Wang, Jing
Chen, Li
Du, Zhigang
- DOI
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10.1515/geo-2022-0369
- URN
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urn:nbn:de:101:1-2022071914100140711740
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:27 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wang, Mo
- Wang, Jing
- Chen, Li
- Du, Zhigang
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