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Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles

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Abstract

The accurate and timely mapping of summer maize is vital for agricultural management and food security. Time-series remotely sensed imagery provides a promising data resource for crop mapping by characterizing growth cycles over time. Therefore, this study explores summer maize mapping with composited time-series Sentinel-1A imagery of a typical crop field in China. First, time-series backscattering coefficients of major land-cover types (i.e. summer maize, peanut, forest, settlements, and water) are extracted from multi-temporal Sentinel-1A imagery. Second, according to the growth cycles of summer maize and peanut, the multi-temporal Sentinel-1A images are composited to enhance the characteristics of the summer maize growth cycle, while also eliminating redundant information and differences in phenology. Third, the decision-tree method is used to perform pixel-level classification; samples with an area of 1 km2 are collected as validation datasets. The results show that Sentinel-1A VH-polarized images are more sensitive to the summer maize growth cycle than VV-polarized images. The summer maize cropping areas are estimated with an overall accuracy of 96.55% and a kappa coefficient of 0.93. The results suggest that multi-temporal Sentinel-1A imagery is capable of characterizing the growth cycle of summer maize, and provides a promising solution for accurate summer maize mapping, irrespective of weather conditions.

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Acknowledgements

This study was funded by the China Postdoctoral Science Foundation (2019M662478), Major project of Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education (2020M19), Natural Science Foundation of Henan (202300410075), National Demonstration Center for Experimental Environment and Planning Education (Henan University) Funding Project (2020HGSYJX009), and the National Natural Science Foundation of China (41871347).

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Authors and Affiliations

  1. National Demonstration Center for Environment and Planning, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China

    Haifeng Tian & Yaochen Qin

  2. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing, 100101, China

    Haifeng Tian, Zheng Niu & Li Wang

  3. College of Resource and Environment, University of Chinese Academy of Sciences, Yuquan Road 19, Shijingshan, Beijing, 100049, China

    Haifeng Tian & Zheng Niu

  4. Center for Modern Chinese City Studies, School of City and Regional Science, East China Normal University, Zhongshan North Road 3663, Putuo, Shanghai, 200062, China

    Shishuai Ge

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  1. Haifeng Tian
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Tian, H., Qin, Y., Niu, Z. et al. Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles. J Indian Soc Remote Sens 49, 2863–2874 (2021). https://doi.org/10.1007/s12524-021-01428-0

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