6 February 2018 Estimating winter wheat planting area in north China driven by the East Asia monsoon
Yazhen Jiang, Xiaoguang Jiang, Songcai You, Chen Ru
Author Affiliations +
Abstract
Mapping and monitoring cropland areas and distributions from remote sensing data could provide early warning information of threats to the global and regional food security. Winter wheat is traditionally the most cultivated food crop in China, and the Huang-Huai-Hai (HHH) plain is an important winter wheat production base. Due to a long latitudinal distance, the same winter wheat growth stage delays from the north to the south of the plain. Influenced by the monsoon climate, isolines of the winter wheat phenology are oriented in a northeast–southwest direction, which is similar to that of the temperature distribution. In this paper, Moderate Resolution Imaging Spectroradiometer-enhanced vegetation index (EVI) was applied to estimate the winter wheat planting information on the HHH plain, using a model built according to seasonal change of the winter wheat EVI. The result shows that the average accuracy of the estimation was 75.4% with a standard deviation of 26.1%, when the impacts from the phenology delay and the monsoon climate were not considered. When winter wheat phenology delay was considered with and without the influences from the monsoon climate, the accuracy was 93.2% with a standard deviation of 6.1% and 84.7% with a standard deviation of 11.0%, respectively. The accuracy increased evidently. Therefore, both the phenology delay and monsoon climate impacts should be taken into consideration when estimating the winter wheat planting information in a large monsoon climate region.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2018/$25.00 © 2018 SPIE
Yazhen Jiang, Xiaoguang Jiang, Songcai You, and Chen Ru "Estimating winter wheat planting area in north China driven by the East Asia monsoon," Journal of Applied Remote Sensing 12(2), 022205 (6 February 2018). https://doi.org/10.1117/1.JRS.12.022205
Received: 27 September 2017; Accepted: 10 January 2018; Published: 6 February 2018
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KEYWORDS
Climatology

MODIS

Vegetation

Data modeling

Environmental sensing

Remote sensing

Statistical analysis

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