Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  • Feng Xu
  • Zhaofu Li
  • Shuyu Zhang
  • Naitao Huang
  • Zongyao Quan
  • Wenmin Zhang
  • Xiaojun Liu
  • Xiaosan Jiang
  • Jianjun Pan
  • Prishchepov, Alexander

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.

TidsskriftRemote Sensing
Udgave nummer12
StatusUdgivet - 2020

ID: 245320547