应用Sentinel-2时序影像光谱特征的大豆识别提取方法

罗旺; 彭代亮; 刘锦绣; 徐俊锋; 楼子杭; 刘国华; 高爽; 俞乐; 王福民

doi:10.11834/jrs.20233056

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应用Sentinel-2时序影像光谱特征的大豆识别提取方法
Soybean Identification and Extraction Method Using Spectral Characteristics of Sentinel-2 Time Series Images
2023年页码：1-12
网络出版日期： 2023-10-10 ，
DOI： 10.11834/jrs.20233056

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罗旺，彭代亮，刘锦绣，徐俊锋，楼子杭，刘国华，高爽，俞乐，王福民.XXXX.应用Sentinel-2时序影像光谱特征的大豆识别提取方法.遥感学报，XX（XX）： 1-12

LUO Wang，PENG Dailiang，LIU Jinxiu，XU Junfeng，LOU Zihang，LIU Guohua，GAO Shuang，YU Le，WANG Fumin. XXXX. Soybean Identification and Extraction Method Using Spectral Characteristics of Sentinel-2 Time Series Images. National Remote Sensing Bulletin， XX（XX）：1-12
罗旺，彭代亮，刘锦绣，徐俊锋，楼子杭，刘国华，高爽，俞乐，王福民.XXXX.应用Sentinel-2时序影像光谱特征的大豆识别提取方法.遥感学报，XX（XX）： 1-12 DOI： 10.11834/jrs.20233056.

LUO Wang，PENG Dailiang，LIU Jinxiu，XU Junfeng，LOU Zihang，LIU Guohua，GAO Shuang，YU Le，WANG Fumin. XXXX. Soybean Identification and Extraction Method Using Spectral Characteristics of Sentinel-2 Time Series Images. National Remote Sensing Bulletin， XX（XX）：1-12 DOI： 10.11834/jrs.20233056.

摘要

大豆是世界上最主要的作物之一，是重要的高蛋白食品、牲畜饲料的原料以及食用油来源。中国面临着大豆高度依赖于进口的粮食安全结构性挑战，对大豆的监测识别能为中国制定有效的农业经济发展策略提供基础数据支持。本研究提出了一种基于时序谱标准曲线的大豆识别方法，基于时间序列植被指数曲线并添加气象权重因子建立大豆时序谱标准曲线精确识别大豆，重点分析大豆与玉米的时序谱差异，以及气象因素对曲线的影响，并建立时序谱标准曲线信息映射到样本的方法，解决大范围大豆制图样本不足的问题。本研究设计实验验证了在时间尺度以及灾害情况下基于时序谱标准曲线识别大豆方法的鲁棒性，通过时序谱标准曲线提取的物候特征结合随机森林分类器对2020年黑龙江省大豆进行分类制图，分类混淆矩阵显示大豆识别的总体精度为86.95%，用户精度为90.91%，制图精度为86.14%，F1-Score为0.8846。本研究方法能够通过气象因子的变化表现出地域差异和灾害影响，为大豆识别（尤其是一季大豆种植区）方法适应不同研究区和灾害情况提供可行思路。

Abstract

ObjectiveSoybean is the world’s most important legume crop that serves as a major source of high-protein food

the primary ingredient for livestock feed

and an essential source of edible oil. They play a crucial role in the world's food production

with a global annual production of around 370 million metric tons. China is one of the major soybean producing countries globally

with an annual production of around 17 million metric tons. However

China's domestic soybean production is insufficient to meet production and living needs

and it is highly reliant on imports

accounting for more than 80% of its soybean consumption. Consequently

China's food security faces significant structural challenges. Remote sensing technology is a powerful tool for monitoring soybean cultivation and can provide basic data support for various countries to release signals of changes in agricultural product markets

strengthen the guidance of agricultural product markets

and formulate effective agricultural economic development strategies. The traditional method of estimating soybean planting area through agricultural surveys is usually time-consuming

labor-intensive

and subject to subjective factors

leading to inaccurate and imprecise results. In contrast

remote sensing technology utilizes satellite

aerial

or drone-based sensors to capture and analyze the electromagnetic radiation reflected or emitted by the Earth's surface

providing a more objective and efficient way of monitoring crop planting areas. While methods based on vegetation indices time series and phenology are widely used for crop recognition including soybeans

and the focus has been primarily on the impact of the vegetation index time series feature or phenological feature in soybean recognition

and there has been limited research on the time series curve itself. There is a lack of analysis and research on the spectral characteristics of the key growth stages for soybeans

and no standard spectral time series curve for soybeans has been established to summarize their changing patterns. Additionally

mainstream crop recognition methods face difficulties in obtaining samples

especially in large-scale mapping

where the quality and quantity of samples are the main limiting factors.MethodThis study proposes a soybean recognition method based on the standard spectral time series curve on the Google Earth Engine (GEE) cloud platform. By adding the climate weight factor

the standard spectral time series curve of soybean can be accurately recognized.ResultUsing the features of the standard spectral time series curve combined with the random forest classifier

a soybean distribution map in Heilongjiang Province in 2020 was extracted. The classification confusion matrix shows that the overall accuracy of soybean recognition is 86.95%

the user accuracy is 90.91%

the producer accuracy is 86.14%

and the F1-Score is 0.8846. Compared to statistical area data

the area accuracy reaches 95.94%.ConclusionThe study focuses on analyzing the difference between the spectral time series curves of soybean and corn and the impact of meteorological factors on the curves

and establishes a method for mapping the standard spectral time-series curve information to samples

thereby solving the problem of insufficient samples for mapping a large area of soybean. Furthermore

this paper designs experiments to verify the robustness of this soybean recognition method based on the standard spectral time series curve in terms of time scale and disaster situations. These results provide scientific evidence and technical support for the monitoring of soybean growth

disaster evaluation

and the formulation of international agricultural product trade strategies.

关键词

大豆时序谱标准曲线物候气象

Keywords

SoybeanTemporal spectrumStandard curvePhenologyMeteorology

references

Abdi, Abdulhakim Mohamed. 2020. "Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data." GIScience & Remote Sensing 57 (1):1-20. doi: 10.1080/15481603.2019.1650447http://dx.doi.org/10.1080/15481603.2019.1650447.

Arvor, Damien, Milton Jonathan, Margareth Simões Penello Meirelles, Vincent Dubreuil, and Laurent Durieux. 2011. "Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil." International Journal of Remote Sensing 32 (22):7847-71. doi: 10.1080/01431161.2010.531783http://dx.doi.org/10.1080/01431161.2010.531783.

Beguería, Santiago, and Marco P. Maneta. 2020. "Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction." Proceedings of the National Academy of Sciences 117 (31):18317-23. doi: 10.1073/pnas.1917774117http://dx.doi.org/10.1073/pnas.1917774117.

Bolton, Douglas K., Josh M. Gray, Eli K. Melaas, Minkyu Moon, Lars Eklundh, and Mark A. Friedl. 2020. "Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery." Remote Sensing of Environment 240:111685. doi: 10.1016/j.rse.2020.111685http://dx.doi.org/10.1016/j.rse.2020.111685.

Chen, Yaoliang, Xiaodong Song, Shusen Wang, Jingfeng Huang, and Lamin R. Mansaray. 2016. "Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data." ISPRS Journal of Photogrammetry and Remote Sensing 119:451-61. doi: 10.1016/j.isprsjprs.2016.07.007http://dx.doi.org/10.1016/j.isprsjprs.2016.07.007.

Ghorbanian, Arsalan, Mohammad Kakooei, Meisam Amani, Sahel Mahdavi, Ali Mohammadzadeh, and Mahdi Hasanlou. 2020. "Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples." ISPRS Journal of Photogrammetry and Remote Sensing 167:276-88. doi: 10.1016/j.isprsjprs.2020.07.013http://dx.doi.org/10.1016/j.isprsjprs.2020.07.013.

Grzegozewski, Denise Maria, Jerry Adriani Johann, Miguel Angel Uribe-Opazo, Erivelto Mercante, and Alexandre Camargo Coutinho. 2016. "Mapping soya bean and corn crops in the State of Paraná, Brazil, using EVI images from the MODIS sensor." International Journal of Remote Sensing 37 (6):1257-75. doi: 10.1080/01431161.2016.1148285http://dx.doi.org/10.1080/01431161.2016.1148285.

Guo, Shibo, Xiaoguang Yang, Zhentao Zhang, Fangliang Zhang, and Tao Liu. 2020. "Spatial Distribution and Temporal Trend Characteristics of Agro-Climatic Resources and Extreme Climate Events during the Soybean Growing Season in Northeast China from 1981 to 2017." Journal of Meteorological Research 34 (6):1309-23. doi: 10.1007/s13351-020-0061-3http://dx.doi.org/10.1007/s13351-020-0061-3.

Huang, Huabing, Jie Wang, Caixia Liu, Lu Liang, Congcong Li, and Peng Gong. 2020. "The migration of training samples towards dynamic global land cover mapping." ISPRS Journal of Photogrammetry and Remote Sensing 161:27-36. doi: 10.1016/j.isprsjprs.2020.01.010http://dx.doi.org/10.1016/j.isprsjprs.2020.01.010.

Huete, A, K Didan, T Miura, E. P Rodriguez, X Gao, and L. G Ferreira. 2002. "Overview of the radiometric and biophysical performance of the MODIS vegetation indices." Remote Sensing of Environment 83 (1):195-213. doi: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2.

Kang, Yupeng, Xinli Hu, Qingyan Meng, Youfeng Zou, Linlin Zhang, Miao Liu, and Maofan Zhao. 2021. "Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data." Remote Sensing 13 (22):4522. doi: 10.3390/rs13224522http://dx.doi.org/10.3390/rs13224522.

Keisling T. C. 1982. "Calculation of the Length of Day1." Agronomy Journal 74 (4):758-9. doi: 10.2134/agronj1982.00021962007400040036xhttp://dx.doi.org/10.2134/agronj1982.00021962007400040036x.

Li, Erzhu, Peijun Du, Alim Samat, Junshi Xia, and Meiqin Che. 2015. "An automatic approach for urban land-cover classification from Landsat-8 OLI data." International Journal of Remote Sensing 36 (24):5983-6007. doi: 10.1080/01431161.2015.1109726http://dx.doi.org/10.1080/01431161.2015.1109726.

Li, Ruiyuan, Miaoqing Xu, Ziyue Chen, Bingbo Gao, Jun Cai, Feixue Shen, Xianglin He, Yan Zhuang, and Danlu Chen. 2021. "Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model." Soil and Tillage Research 206:104838. doi: 10.1016/j.still.2020.104838http://dx.doi.org/10.1016/j.still.2020.104838.

Li, Xiyu, Le Yu, Dailiang Peng, and Peng Gong. 2021. "A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang Province as a test case." International Journal of Remote Sensing 42 (19):7332-56. doi: 10.1080/01431161.2021.1957177http://dx.doi.org/10.1080/01431161.2021.1957177.

Liu, Xiaoxuan, Le Yu, Liheng Zhong, Pengyu Hao, Bo Wu, Hongshuo Wang, Chaoqing Yu, and Peng Gong. 2019. "Spatial-temporal patterns of features selected using random forests: a case study of corn and soybeans mapping in the US." International Journal of Remote Sensing 40 (1):269-83. doi: 10.1080/01431161.2018.1512769http://dx.doi.org/10.1080/01431161.2018.1512769.

Liu, Xin, Tanzeelur Rahman, Chun Song, Benying Su, Feng Yang, Taiwen Yong, Yushan Wu, Cuiying Zhang, and Wenyu Yang. 2017. "Changes in light environment, morphology, growth and yield of soybean in maize-soybean intercropping systems." Field Crops Research 200. doi: 10.1016/j.fcr.2016.10.003http://dx.doi.org/10.1016/j.fcr.2016.10.003.

Muñoz Sabater J. 2019. "ERA5-Land monthly averaged data from 1981 to present." Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi: (Accessed on < 02-FEB-2022>)10.24381/cds.68d2bb3.

Mutanga, Onisimo, and Lalit Kumar. 2019. "Google Earth Engine Applications." Remote Sensing 11 (5):591. doi: 10.3390/rs11050591http://dx.doi.org/10.3390/rs11050591.

Paludo, Alex, Willyan Ronaldo Becker, Jonathan Richetti, Laíza Cavalcante De Albuquerque Silva, and Jerry Adriani Johann. 2020. "Mapping summer soybean and corn with remote sensing on Google Earth Engine cloud computing in Parana state – Brazil." International Journal of Digital Earth 13 (12):1624-36. doi: 10.1080/17538947.2020.1772893http://dx.doi.org/10.1080/17538947.2020.1772893.

Roy D. P., and L. Yan. 2020. "Robust Landsat-based crop time series modelling." Remote Sensing of Environment 238:110810. doi: 10.1016/j.rse.2018.06.038http://dx.doi.org/10.1016/j.rse.2018.06.038.

Sun, Jing, Harold Mooney, Wenbin Wu, Huajun Tang, Yuxin Tong, Zhenci Xu, Baorong Huang, et al. 2018. "Importing food damages domestic environment: Evidence from global soybean trade." Proceedings of the National Academy of Sciences 115 (21):5415-9. doi: 10.1073/pnas.1718153115http://dx.doi.org/10.1073/pnas.1718153115.

Wang, Cong, Jin Chen, Jin Wu, Yanhong Tang, Peijun Shi T. Andrew Black, and Kai Zhu. 2017. "A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems." Remote Sensing of Environment 196:1-12. doi: 10.1016/j.rse.2017.04.031http://dx.doi.org/10.1016/j.rse.2017.04.031.

Wang, Sherrie, Stefania Di Tommaso, Jillian M. Deines, and David B. Lobell. 2020. "Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive." Scientific Data 7 (1):307. doi: 10.1038/s41597-020-00646-4http://dx.doi.org/10.1038/s41597-020-00646-4.

Wardlow, Brian D., Stephen L. Egbert, and Jude H. Kastens. 2007. "Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains." Remote Sensing of Environment 108 (3):290-310. doi: 10.1016/j.rse.2006.11.021http://dx.doi.org/10.1016/j.rse.2006.11.021.

Weiss M., F. Jacob, and G. Duveiller. 2020. "Remote sensing for agricultural applications: A meta-review." Remote Sensing of Environment 236:111402. doi: https://doi.org/10.1016/j.rse.2019.111402https://doi.org/10.1016/j.rse.2019.111402.

Xie, Shuai, Liangyun Liu, Xiao Zhang, Jiangning Yang, Xidong Chen, and Yuan Gao. 2019. "Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine." Remote Sensing 11 (24). doi: 10.3390/rs11243023http://dx.doi.org/10.3390/rs11243023.

Yang, Lingbo, Limin Wang, Jingfeng Huang, Lamin R. Mansaray, and Ruzemaimaiti Mijiti. 2019. "Monitoring policy-driven crop area adjustments in northeast China using Landsat-8 imagery." International Journal of Applied Earth Observation and Geoinformation 82:101892. doi: 10.1016/j.jag.2019.06.002http://dx.doi.org/10.1016/j.jag.2019.06.002.

Yin, Leikun, Nanshan You, Geli Zhang, Jianxi Huang, and Jinwei Dong. 2020. "Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping." Remote Sensing 12 (1):162. doi: 10.3390/rs12010162http://dx.doi.org/10.3390/rs12010162.

You, Nanshan, Jinwei Dong, Jianxi Huang, Guoming Du, Geli Zhang, Yingli He, Tong Yang, Yuanyuan Di, and Xiangming Xiao. 2021. "The 10-m crop type maps in Northeast China during 2017–2019." Scientific Data 8 (1):41. doi: 10.1038/s41597-021-00827-9http://dx.doi.org/10.1038/s41597-021-00827-9.

Yu, Lingxue, Tingxiang Liu, Kun Bu, Fengqin Yan, Jiuchun Yang, Liping Chang, and Shuwen Zhang. 2017. "Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015." Scientific Reports 7 (1):14770. doi: 10.1038/s41598-017-14918-4http://dx.doi.org/10.1038/s41598-017-14918-4.

Zhang, Hankui K., and David P. Roy. 2017. "Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification." Remote Sensing of Environment 197:15-34. doi: 10.1016/j.rse.2017.05.024http://dx.doi.org/10.1016/j.rse.2017.05.024.

Zhao, Jie, Ji Chen, Damien Beillouin, Hans Lambers, Yadong Yang, Pete Smith, Zhaohai Zeng, Jørgen E. Olesen, and Huadong Zang. 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers." Nature Communications 13 (1):1-9. doi: 10.1038/s41467-022-32464-0http://dx.doi.org/10.1038/s41467-022-32464-0.

Zhao, Longcai, Qiangzi Li, Qingrui Chang, Jiali Shang, Xin Du, Jiangui Liu, and Taifeng Dong. 2022. "In-season crop type identification using optimal feature knowledge graph." ISPRS Journal of Photogrammetry and Remote Sensing 194:250-66. doi: 10.1016/j.isprsjprs.2022.10.017http://dx.doi.org/10.1016/j.isprsjprs.2022.10.017.

Zhong, Liheng, Lina Hu, Le Yu, Peng Gong, and Gregory S. Biging. 2016. "Automated mapping of soybean and corn using phenology." ISPRS Journal of Photogrammetry and Remote Sensing 119:151-64. doi: 10.1016/j.isprsjprs.2016.05.014http://dx.doi.org/10.1016/j.isprsjprs.2016.05.014.

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