Please wait a minute...
Journal of Integrative Agriculture  2023, Vol. 22 Issue (6): 1645-1657    DOI: 10.1016/j.jia.2022.10.008
Crop Science Advanced Online Publication | Current Issue | Archive | Adv Search |
Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index
TAO Jian-bin, ZHANG Xin-yue, WU Qi-fan#, WANG Yun
Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  利用遥感数据进行大范围作物制图对于农业生产、粮食安全和人类可持续发展具有重要意义。冬油菜是中国重要的油料作物,主要分布在长江流域。传统的冬油菜制图方法主要利用冬油菜关键物候期的光谱特征,获取遥感数据的时间窗口有限,因而不能满足大范围应用的需要。本研究提出了一种新的基于物候特征的冬油菜指数(PWRI)来进行长江中游地区的冬油菜制图。PWRI扩大了冬油菜和冬小麦的区分时间窗口,在冬油菜整个花期具有良好的分离性。PWRI还扩大了两种冬季作物的可分离性。采用基于PWRI的方法,利用时间序列合成Sentinel-2数据,在Google Earth Engine平台上实现了对长江中游地区冬季油菜的制图。该方法取得了良好的结果,总体精度和kappa系数分别超过92%和0.85。基于PWRI的方法为大范围高空间分辨率冬油菜制图提供了一种新的解决方案。

Abstract  Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index (PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.
Keywords:  phenology-based winter rapeseed index       winter rapeseed mapping        Sentinel-2        Google Earth Engine  
Received: 02 April 2022   Online: 11 October 2022   Accepted: 29 August 2022
Fund: This work was supported by the National Natural Science Foundation of China (41971371) and the Central Universities Fundamental Research Funds (CCNU19TS004 and CCNU19TD002).
About author:  TAO Jian-bin, E-mail: taojb@mail.ccnu.edu.cn; #Correspondence WU Qi-fan, Mobile: +86-13016439939, E-mail: wuqifan6991@163. com

Cite this article: 

TAO Jian-bin, ZHANG Xin-yue, WU Qi-fan, WANG Yun. 2023. Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index. Journal of Integrative Agriculture, 22(6): 1645-1657.

Ashourloo D, Shahrabi H S, Azadbakht M, Aghighi H, Nematollahi H, Alimohammadi A, Matkan A A. 2019. Automatic canola mapping using time series of sentinel 2 images. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 63-76.

Cao J F, Chen L C, Wang M, Tian Y. 2018. Implementing a parallel image edge detection algorithm based on the otsu-canny operator on the hadoop platform. Computational Intelligence & Neuroscience, 3, 1-12.

D’Andrimont R, Taymans M, Lemoine G, Ceglar A, Yordanov M, van der Velde M. 2020. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and-2 time series. Remote Sensing of Environment, 239, 111660.

Delegido J, Verrelst J, Alonso L, Moreno J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11, 7063-7081.

Dong J W, Xiao X M, Menarguez M A, Zhang G L, Qin Y W, Thau D, Biradar C, Moore III B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142-154.

Gitelson A A, Viña A, Arkebauer T J, Rundquist D C, Keydan G, Leavitt B. 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30, 1248.

Griffiths P, Nendel C, Hostert P. 2019. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151.

Han J C, Zhang Z, Cao J. 2020. Developing a new method to identify flowering dynamics of rapeseed using landsat 8 and sentinel-1/2. Remote Sensing, 13, 105.

Han J C, Zhang Z, Luo Y C, Cao J, Zhang L L, Zhang J, Li Z Y. 2021. The RapeseedMap10 database: Annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data. Earth System Science Data, 13, 2857-2874.

Han T, Pan J J, Luo C, Zhou T, Zhang P Y. 2018. Differences between multi-temporal Sentinel-2A and SPOT-7 imagery in rape identification. Journal of Nanjing Agricultural University, 41, 691-700 (in Chinese).

Herbertsson L, Rundlöf M, Smith H G. 2017. The relation between oilseed rape and pollination of later flowering plants varies across plant species and landscape contexts. Basic and Applied Ecology, 24, 77-85.

Jun C, Ban Y F, Li S N. 2014. Open access to Earth land-cover map. Nature, 514, 434-434.

Kang Y H, Özdoğan M, Zipper S C, Román M O, Walker J, Hong S Y, Marshall M, Magliulo V, Moreno J, Alonso L. 2016. How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment. Remote Sensing, 8, 597.

Qiu B W, Li W J, Tang Z H, Chen C C, Qi W. 2015. Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicators, 56, 79-86.

Rouse J, Haas R, Schell J, Deering D. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In:  Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. NASA Goddard Space Flight Center 3DERTS-1 Symp. pp. 301-317.

Roy D P, Wulder M A, Loveland T R, Woodcock C E, Allen R G, Anderson M C, Helder D, Irons J R, Johnson D M, Kennedy R. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172.

Sadri S, Pan M, Wada Y, Vergopolan N, Sheffield J, Famiglietti J S, Kerr Y, Wood E. 2020. A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP. Remote Sensing of Environment, 246, 111864.

Shen M G, Chen J, Zhu X L, Tang Y H, Chen X H. 2010. Do flowers affect biomass estimate accuracy from NDVI and EVI? International Journal of Remote Sensing, 31, 2139-2149.

Somers B, Asner G P. 2012. Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sensing, 4, 2510-2529.

Srinivas C, Prasad M, Sirisha M. 2019. Remote sensing image segmentation using otsu algorithm. International Journal of Computer Applications, 975, 8887.

Sulik J J, Long D S. 2016. Spectral considerations for modeling yield of canola. Remote Sensing of Environment, 184, 161-174.

Tao J B, Liu W B, Tan W X, Kong X B, Xu M. 2019. Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China. Journal of Integrative Agriculture, 18, 2393-2407.

Tao J B, Wu W B, Liu W B, Xu M. 2020. Exploring the spatio-temporal dynamics of winter rape on the middle reaches of yangtze river valley using time-series MODIS data. Sustainability, 12, 466.

Teklemariam D, Azadi H, Nyssen J, Haile M, Witlox F. 2016. How sustainable is transnational farmland acquisition in Ethiopia? Lessons learned from the Benishangul-Gumuz Region. Sustainability, 8, 213.

Tian H F, Huang N, Niu Z, Qin Y C, Pei J, Wang J. 2019a. Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sensing, 11, 820.

Tian H F, Meng M, Wu M Q, Niu Z. 2019b. Mapping spring canola and spring wheat using Radarsat-2 and Landsat-8 images with Google Earth Engine. Current Science, 116, 291-298.

Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, Dejoux J F, Ceschia E. 2017. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415-426.

Wan L, Li Y J, Cen H Y, Zhu J P, Yin W X, Wu W K, Zhu H Y, Sun D W, Zhou W J, He Y. 2018. Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sensing, 10, 1484.

Wang D, Fang S H, Yang Z Z, Wang L, Tang W C, Li Y C, Tong C Y. 2018. A regional mapping method for oilseed rape based on HSV transformation and spectral features. International Journal of Geo-Information, 7, 224.

Wang K, Zhang J. 2015. Extraction of rape seed cropping distribution information in Hubei Province based on MODIS images. Remote Sensing for Land & Resources, 27, 65-70.

Yang N, Liu D, Feng Q, Xiong Q, Zhang L, Ren T, Zhao Y, Zhu D, Huang J. 2019. Large-scale crop mapping based on machine learning and parallel computation with grids. Remote Sensing, 11, 1500.

Yin L, You N, Zhang G, Huang J, Dong J. 2020. Optimizing feature selection of individual crop types for improved crop mapping. Remote Sensing, 12, 162.

Zang Y, Chen X, Chen J, Tian Y, Shi Y, Cao X, Cui X. 2020. Remote sensing index for mapping canola flowers using MODIS data. Remote Sensing, 12, 3912.

Zhang H, Liu W, Zhang L. 2022. Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 45-62.

Zhang W, Chen E, Li Z, Zhao L, Ji Y, Zhang Y, Liu Z. 2018. Rape (Brassica napus L.) growth monitoring and mapping based on radarsat-2 time-series data. Remote Sensing, 10, 206.

Zhang Z, Cong R, Tao R, Hui L, Yun Z, Lu J. 2020. Optimizing agronomic practices for closing rapeseed yield gaps under intensive cropping systems in China. Journal of Integrative Agriculture, 19, 1241-1249.

Zhu Z, Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94.

 

No related articles found!
No Suggested Reading articles found!