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Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images

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Abstract

Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.

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Acknowledgments

We appreciate the anonymous reviewers who have spent their time helping improve the clarity and relevance of my research presentation.

Authorship statement

Jia Tang makes substantial contributions to data acquisition, processing and analysis as well as drafting and critically revisiting of the manuscript. Qianfeng Wang contributes to the development of ideas and experimental instruction as well as revision of all stages and takes intellectual responsibility for its content. Other authors participate in data processing and technical support of the manuscript.

Funding

This research received financial support from the National Natural Science Foundation of China (41601562; 41761014), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA13020506), the China Postdoctoral Science Foundation Project (2018M630728), and China Scholarship Council.

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Authors and Affiliations

  1. College of Environment and Resources, Fuzhou University, Fuzhou, 350116, China

    Jia Tang, Jingyu Zeng, Rongrong Zhang, Yue Zeng, Wei Shui, Zhanghua Xu & Qianfeng Wang

  2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth,, Chinese Academy of Science, Beijing, 100094, China

    Qing Zhang

  3. Climate Change Cluster, University of Technology Sydney, Sydney, New South Wales, 2007, Australia

    Song Leng

  4. Joint Global Change Research Institute,, University of Maryland, College Park, MD, 20740, USA

    Qianfeng Wang

  5. Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, Fuzhou University, Fuzhou, China

    Qianfeng Wang

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  1. Jia Tang
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  3. Qing Zhang
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  9. Qianfeng Wang
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Correspondence to Qianfeng Wang.

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Tang, J., Zeng, J., Zhang, Q. et al. Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images. Int J Biometeorol 64, 1273–1283 (2020). https://doi.org/10.1007/s00484-020-01904-1

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