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

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

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas