Journal of Integrative Agriculture ›› 2024, Vol. 23 ›› Issue (4): 1164-1178.DOI: 10.1016/j.jia.2023.05.035

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基于Harmonized Landsat and Sentinel-2数据和物候特征构建新型植被指数提升再生稻遥感制图精度

  

  • 收稿日期:2023-02-15 接受日期:2023-05-05 出版日期:2024-04-20 发布日期:2024-03-29

A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Yunping Chen1*, Jie Hu1*, Zhiwen Cai2, Jingya Yang3, Wei Zhou2, Qiong Hu4, Cong Wang4, Liangzhi You1, 5, Baodong Xu2#   

  1. 1 Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

    2 College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

    3 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

    4 Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

    5 International Food Policy Research Institute, NW, Washington, D.C. 20005, USA

  • Received:2023-02-15 Accepted:2023-05-05 Online:2024-04-20 Published:2024-03-29
  • About author:#Correspondence Baodong Xu, E-mail: xubaodong@mail.hzau.edu.cn * These authors contributed equally to this study.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (42271360 and 42271399), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST)  (2020QNRC001), and the Fundamental Research Funds for the Central Universities, China (2662021JC013, CCNU22QN018).


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摘要：

再生稻是指收获头季水稻后，利用稻茬上的休眠芽萌发成穗，从而再次收获一季水稻的种植模式。与传统的单季稻和双季稻相比，再生稻以较小的农业投入收获双季稻谷，在粮食安全和农业生态方面都发挥着重要作用。然而，由于再生稻与其他水稻种植模式（如双季稻）的光谱相似性，利用遥感技术高精度识别再生稻仍具有较大挑战。此外，再生稻通常种植在耕地破碎且云雨频发区域，需要高时空分辨率的遥感影像以捕获其独特的光谱物候特征。基于此，本研究提出了一种基于物候特征的再生稻植被指数（PRVI），以湖北省蕲春县为例，利用Harmonized Landsat and Sentinel-2 (HLS)数据实现30 m空间分辨率的再生稻遥感制图。再生稻与其他地物类型间的光谱-物候特征的分离性和相关性分析表明，红波段（Red）、近红外波段（NIR）和短波红外1波段（SWIR 1）是再生稻识别的敏感波段，因此本研究采用上述三个波段构建PRVI。为了全面评估PRVI识别再生稻的潜力，基于实地样本数据比较PRVI与常用的水稻识别植被指数（归一化差值植被指数（NDVI）、增强型植被指数（EVI）和地表水分指数（LSWI））识别再生稻的准确性。结果表明，PRVI能够充分捕捉到再生稻独特的光谱物候特征，较好的区分再生稻与其他土地覆盖类型。此外，再生稻与其他地物类型区分的关键物候期是头季灌浆成熟期-再生季分蘖期（GHS-TS2）。最后，在GHS-TS2阶段，PRVI的表现优于NDVI、EVI、LSWI及其组合，其制图精度和用户精度分别为92.22%和89.30%。以上结果表明，本文基于HLS数据构建的PRVI可以在关键物候期高精度识别耕地破碎区域的再生稻，有益于进行再生稻早期识别以及能够为农业管理活动提供重要的基础数据集。

Abstract:

Ratoon rice, which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop, plays an important role in both food security and agroecology while requiring minimal agricultural inputs.  However, accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems (e.g., double rice).  Moreover, images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.  In this study, taking Qichun County in Hubei Province, China as an example, we developed a new phenology-based ratoon rice vegetation index (PRVI) for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2 (HLS) images.  The PRVI that incorporated the red, near-infrared, and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.  Based on actual field samples, the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and land surface water index (LSWI).  The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice, leading to a favorable separability between ratoon rice and other land cover types.  Furthermore, the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop (GHS-TS2), indicating that only several images are required to obtain an accurate ratoon rice map.  Finally, the PRVI performed better than NDVI, EVI, LSWI and their combination at the GHS-TS2 stages, with producer’s accuracy and user’s accuracy of 92.22 and 89.30%, respectively.  These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages, which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Key words: ratoon rice ,  phenology-based ratoon rice vegetation index (PRVI) ,  phenological phase ,  feature selection ,  Harmonized Landsat