多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

doi:10.3864/j.issn.0578-1752.2024.02.003

中国农业科学 ›› 2024, Vol. 57 ›› Issue (2): 250-263.doi: 10.3864/j.issn.0578-1752.2024.02.003

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

童婉婷¹(), 魏浩东², 杨靖雅³, 金文捷¹, 宋茜³(), 胡琼⁴, 尹高飞⁵, 徐保东¹()

¹ 华中农业大学资源与环境学院/宏观农业研究院，武汉 430070
² 华中农业大学植物科学技术学院，武汉 430070
³ 中国农业科学院农业资源与农业区划研究所/北方干旱半干旱耕地高效利用全国重点实验室，北京 100081
⁴ 华中师范大学城市与环境科学学院，武汉 430079
⁵ 西南交通大学地球科学与环境工程学院，成都 610031

收稿日期:2023-05-25 接受日期:2023-08-17 出版日期:2024-01-16 发布日期:2024-01-19
通信作者:
宋茜，E-mail：songqian01@caas.cn。
徐保东，E-mail：xubaodong@mail.hzau.edu.cn
联系方式: 童婉婷，E-mail：twt@webmail.hzau.edu.cn。
基金资助:
国家重点研发计划(2021YFD1600503); 国家自然科学基金(42271360); 国家自然科学基金(42001303); 中央高校基本科研业务费专项基金(2662021JC013); 中央高校基本科研业务费专项基金(CCNU22JC013); 中央高校基本科研业务费专项基金(CCNU22QN018); 四川省杰出青年科技人才项目(2021JDJQ0007); 中央级公益性科研院所基本科研业务费专项(1610132021010)

Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification

TONG WanTing¹(), WEI HaoDong², YANG JingYa³, JIN WenJie¹, SONG Qian³(), HU Qiong⁴, YIN GaoFei⁵, XU BaoDong¹()

¹ College of Resources and Environment/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
² College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070
³ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Beijing 100081
⁴ School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079
⁵ Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031

Received:2023-05-25 Accepted:2023-08-17 Published:2024-01-16 Online:2024-01-19

摘要/Abstract

摘要：

【背景】中高空间分辨率（≤30 m）影像是在耕地破碎、种植结构复杂的中国南方开展农作物遥感识别研究的重要数据。然而，要克服中高分辨率传感器重访周期较长以及南方多云雨天气的影响，对影像进行时间窗口合成以协同使用多源中高分辨率遥感数据，是获取时空连续农作物制图结果的必要保障。由于不同卫星影像获取的周期不同，且不同农作物物候季相节律存在较大差异，如何选择影像合成时间窗口是农作物准确识别的关键前提。【目的】通过探究影像合成时间窗口对于农作物识别的影响机制，为大尺度复杂农作物种植结构制图提供重要参考依据。【方法】以农作物类型多样且云雨天气频繁的湖北省江汉平原为研究区，通过协同Landsat-8和Sentinel-2A/2B卫星影像，设置7种时间合成窗口（15、20、25、30、40、50和60 d）情景，分别从影像的覆盖度、不同农作物时序光谱特征以及不同农作物分类精度等角度，深入分析影像时间合成窗口对农作物遥感识别的影响。【结果】在影像20 d时间合成窗口的情景下，江汉平原农作物（冬油菜、冬小麦、水稻、稻虾田和其他作物）的总体分类精度最高，为93.13%。对比而言，在影像较短时间合成窗口（如15 d）的情景下，时间序列密集但高质量影像覆盖度较低，农作物总体分类精度较低（90.91%）；而在影像较长时间合成窗口（如60 d）的情景下，影像覆盖度高但时间序列稀疏，导致农作物识别的关键物候信息丢失，降低了总体分类精度（86.06%）。此外，不同农作物的识别效果受影像时间合成窗口的影响程度不同，依次为其他作物>冬油菜>水稻>冬小麦>稻虾田。其他作物类内时序光谱特征变异性较大，因此对时间窗口极其敏感。油菜准确识别的关键物候期为开花期，该时期长度较短，影像合成时间超过30 d会极大降低其识别效果，主要体现为与小麦的混淆。区分稻虾田与单季稻的关键物候期为稻虾田的稻闲季淹水期，其持续时间较长，受时间合成窗口影响较小。【结论】影像时间合成窗口20 d时可兼顾高质量影像覆盖度和捕获农作物识别的关键物候特征，但不同作物识别的最优时间合成窗口受到作物关键物候期影响。研究结果可为多源中高分辨率影像协同下时间合成窗口的选择提供理论参考和方法支撑，进而有效服务于宏观尺度农作物高精度遥感制图研究。

关键词: 农作物遥感识别, 时间合成窗口, 随机森林, Sentinel-2, Landsat-8

Abstract:

【Background】 The decametric-resolution (≤30 m) image is an important data source to identify crop types in South China dominated by fragmented croplands and complex cropping patterns. Due to the relative long revisit frequency of decametric-resolution sensors and persistent rainy/cloud weather in South China, it is critical to integrate multi-source decametric-resolution images using the temporal composition method for the generation of spatiotemporal continuous crop type map. Due to the different temporal resolutions of different satellites, and the significant differences in phenological quaternal rhythms of various crop types, selecting the optimal temporal composition window for integrating multi-source images is vital to map crop type distribution accurately.【Objective】 This study aims to explore the impact of image temporal composition windows on crop type identification, and to provide significant references for large-scale crop type mapping in regions with complex terrain.【Method】 In this study, Landat-8 and Sentinel-2 data were integrated to extract the crop type distribution in the Jianghan Plain, Hubei Province, characterized by the various crop types and cloudy and rainy weather. Then, seven scenarios (15, 20, 25, 30, 40, 50, and 60 d) were established to analyze the effect of different temporal composition windows on crop type identification. Specifically, three aspects, including image coverage rate, spectral-temporal feature curves for different crops and classification accuracies, were combined to understand the performances of different scenarios comprehensively.【Result】 The crop type mapping using 20-day composition window performed the best, with the overall accuracy (OA) of 93.13%. In contrast, the scenarios that used narrower temporal composition window derived lower accuracy of crop type identification (e.g., OA=90.91% for the 15-day composition window), which can be primarily attributed to the low coverage rate of good observations in the study area. Meanwhile, since time series images composited in the wide window blurred the key phenological information for different crops, the classification accuracy of crop type mapping scenarios using wide temporal interval was also lower (e.g., OA=86.06% for the 60-day composition window). Additionally, the effect of temporal interval on different crops classification was ranked as following: other crops>rapeseed>rice>wheat>rice-crayfish. In detail, the reason why the classification performance of other crops was the most sensitive to the temporal composition window can be due to the high intra-class phenological variance of this type. Flowering period is the key phenology window to identify rapeseed, therefore, the classification accuracy of rapeseed decreased while the temporal composition window exceeds 30-day, and rapeseed was easily confused with wheat. Furthermore, because the key phenology window to distinguish rice-crayfish from single-cropping rice (i.e., the flooding stage of rice-crayfish fields) lasted a long period (e.g., from October 2020 to June 2021), the classification accuracy of rice-crayfish was less sensitive to the temporal composition window.【Conclusion】 In general, the 20-day impact of the temporal composition window can take into account the high-quality image coverage and capture of key phenological characteristics of crop identification, but the optimal temporal composition window of different crops identification is affected by the key phenological period of crops. This study provides theoretical reference and method support for selecting the optimal temporal composition window to generate multi-source image time series, which is promising to improve the efficiency and accuracy of large-scale crop type mapping.

Key words: crop type mapping, temporal composition window, random forest, Sentinel-2, Landsat-8

童婉婷, 魏浩东, 杨靖雅, 金文捷, 宋茜, 胡琼, 尹高飞, 徐保东. 多源中高分辨率影像协同下时间合成窗口对农作物识别的影响[J]. 中国农业科学, 2024, 57(2): 250-263.

TONG WanTing, WEI HaoDong, YANG JingYa, JIN WenJie, SONG Qian, HU Qiong, YIN GaoFei, XU BaoDong. Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification[J]. Scientia Agricultura Sinica, 2024, 57(2): 250-263.

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植被指数 Vegetation index	公式 Equation	参考文献 Reference
归一化植被指数 NDVI	$N D V I=\frac{\rho_{N I R}-\rho_{R E D}}{\rho_{N I R}+\rho_{R E D}}$	[40]
陆表水分指数 LSWI	$L S W I=\frac{\rho_{\text {NIR }}-\rho_{\text {SWIR } 1}}{\rho_{\text {NIR }}+\rho_{\text {SWIR } 1}}$	[47]
增强型植被指数 EVI	$E V I=\frac{2.5 \times\left(\rho_{\text {SWIR1 }}-\rho_{\text {RED }}\right)}{\rho_{\text {NIR }}+6 \times \rho_{\text {RED }}-7.5 \times \rho_{\text {BLUE }}+1}$	[45]
绿度指数 VIgreen	$\text { VIgreen }=\frac{\rho_{\text {GREEN }}-\rho_{\text {RED }}}{\rho_{\text {GREEN }}+\rho_{\text {RED }}}$	[46]

多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification

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