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Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China
- Source :
- Remote Sensing; Volume 12; Issue 12; Pages: 2065, Remote Sensing, Vol 12, Iss 2065, p 2065 (2020), Xu, F, Li, Z, Zhang, S, Huang, N, Quan, Z, Zhang, W, Liu, X, Jiang, X, Pan, J & Prishchepov, A V 2020, ' Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China ', Remote Sensing, vol. 12, no. 12, 2065 . https://doi.org/10.3390/RS12122065
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.
- Subjects :
- Multi-temporal
010504 meteorology & atmospheric sciences
Science
Winter wheat
0211 other engineering and technologies
02 engineering and technology
Spatial distribution
01 natural sciences
Satellite data
temporal aggregation
crop development phase
021101 geological & geomatics engineering
0105 earth and related environmental sciences
multi-temporal
winter wheat
Google earth engine
Phenology
fungi
digestive, oral, and skin physiology
food and beverages
Sowing
Reflectivity
Random forest
Temporal aggregation
Crop development phase
General Earth and Planetary Sciences
Environmental science
Physical geography
Subjects
Details
- ISSN :
- 20724292
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....6b8ab7694430c95e73bc5a1b7a242899