Abstract
Abstract. Early-season crop identification is of great importance for monitoring crop growth and predicting yield for decision makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18 % of the global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces, which produce more than 98 % of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer's and user's accuracies higher than 89.30 % and 90.59 %, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately 3 months before harvest, and the optimal identifiable time of winter wheat was at the end of April with an overall accuracy of 89.88 %. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: https://doi.org/10.6084/m9.figshare.12003990, Dong et al., 2020a).
Highlights
Wheat is one of the most important cereal crops in the world (FAOSTAT, 2018; Guo et al, 2019)
The methodological workflow consists of the following steps: (1) image preprocessing to construct monthly maximum composite NDVI images and extraction of cropland based on the FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) product; (2) data processing, which produces standard seasonal change of NDVI for winter wheat for each province based on the winter wheat samples; (3) winter wheat identification, where time-weighted dynamic time warping (TWDTW) is used to measure the similarity of seasonal changes of NDVI for known winter wheat fields with investigated fields, and area statistical data use at the province level to determine the thresholds of similarity measurements; and (4) evaluation for assessing the classification accuracies (Fig. 2)
To examine the potential for early-season identification of winter wheat and explore how early we could produce the distribution maps before the harvest, we investigated the method with shorter time windows and assessed its performance based on all the survey samples collected, which correspond to 33 776 pixels in total
Summary
Wheat is one of the most important cereal crops in the world (FAOSTAT, 2018; Guo et al, 2019). According to the statistics provided by the Food and Agriculture Organization (FAO), the harvested area of wheat reached 215×106 ha in 2018 worldwide, accounting for 30 % of the global grain area and 29 % of the grain production (FAOSTAT, 2018). As a major type of wheat, winter wheat dominates the wheat production in many countries including China, United States, France, Russia, Ukraine, Argentina, and Australia (National Bureau of Statistics of China, 2018; USDA-ERS, 2018). It accounts for more than 70 % of the total wheat production in the United States (USDA-ERS, 2018). Dong et al.: Early-season mapping of winter wheat in China ing the detailed location and planting area of winter wheat provides the basis for forecasting winter wheat yield, understanding winter wheat management, and assessing food security (Franch et al, 2015, 2019; Huang et al, 2015; Wang et al, 2019; Zhang et al, 2019; Zhuo et al, 2019)
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