JOURNAL ARTICLE

Early-season mapping of winter wheat in China based on Landsat and Sentinel images

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).

Keywords:
Phenology Winter wheat Environmental science Growing season Crop Vegetation (pathology) Agriculture China Spatial distribution Yield (engineering) Satellite Physical geography Agronomy Geography Remote sensing Forestry Biology

Metrics

198
Cited By
12.55
FWCI (Field Weighted Citation Impact)
54
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Remote Sensing in Agriculture
Physical Sciences →  Environmental Science →  Ecology
Plant Water Relations and Carbon Dynamics
Physical Sciences →  Environmental Science →  Global and Planetary Change
Remote Sensing and LiDAR Applications
Physical Sciences →  Environmental Science →  Environmental Engineering

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