Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

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

doi:10.3390/rs12122065

Feng Xu, Zhaofu Li + Show 8 more

Open Access

https://doi.org/10.3390/rs12122065

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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.

Highlights

Wheat is the most widely grown cereal crop in the world and plays an important role in the food supply, accounting for approximately 20% of human energy consumption [1,2]
Spectral differences existed between winter wheat and other surface types (Figure 4)
In the NIR band, the curve of winter wheat had two peaks in the overwintering (December to February in the following year) and jointing-heading (April) phases, the curve of water remained constant and the other land-cover types have a valley in the over-wintering phase

Summary

Introduction

Wheat is the most widely grown cereal crop in the world and plays an important role in the food supply, accounting for approximately 20% of human energy consumption [1,2]. Previous studies have demonstrated that multi-temporal imagery can accurately extract crops by benefiting on the phenological characteristics of vegetation types [18,19,20,21] These studies at fine resolution often were limited to a small scale (prefecture-level city or county). It is unclear how the extraction of phenological characteristics can be robust and result in accurate mapping of crop types across large areas, for instance, winter wheat

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