Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China
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Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China. / Xu, Feng; Li, Zhaofu; Zhang, Shuyu; Huang, Naitao; Quan, Zongyao; Zhang, Wenmin; Liu, Xiaojun; Jiang, Xiaosan; Pan, Jianjun; Prishchepov, Alexander V.
I: Remote Sensing, Bind 12, Nr. 12, 2065, 2020.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China
AU - Xu, Feng
AU - Li, Zhaofu
AU - Zhang, Shuyu
AU - Huang, Naitao
AU - Quan, Zongyao
AU - Zhang, Wenmin
AU - Liu, Xiaojun
AU - Jiang, Xiaosan
AU - Pan, Jianjun
AU - Prishchepov, Alexander V.
PY - 2020
Y1 - 2020
N2 - Winterwheat is one of themajor cereal crops inChina. The spatial distribution ofwinterwheat 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 datawere combined. As our results also revealed, itwas always good to classifymulti-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.
AB - Winterwheat is one of themajor cereal crops inChina. The spatial distribution ofwinterwheat 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 datawere combined. As our results also revealed, itwas always good to classifymulti-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.
KW - Crop development phase
KW - Google earth engine
KW - Multi-temporal
KW - Temporal aggregation
KW - Winter wheat
U2 - 10.3390/RS12122065
DO - 10.3390/RS12122065
M3 - Journal article
AN - SCOPUS:85088273943
VL - 12
JO - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 12
M1 - 2065
ER -
ID: 245320547