Development of the FI-R model, a novel remote sensing method for fine-scale extraction of vegetation, using rapeseed as an example

doi:10.1016/j.jia.2025.05.006

Journal of Integrative Agriculture

2026, Vol. 25

Issue (3): 1223-1242 DOI: 10.1016/j.jia.2025.05.006

Agro-ecosystem & Environment

Advanced Online Publication | Current Issue | Archive | Adv Search

Development of the FI-R model, a novel remote sensing method for fine-scale extraction of vegetation, using rapeseed as an example

Sixian Yin¹, Taixia Wu^1#, Shudong Wang², Ran Chen¹, Yingying Yang¹, Hongzhao Tang^3#

¹ School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

² Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

³ Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China

Highlights
● A novel flowering spectral index for the fine-scale extraction of angiosperms over large areas in complex multi-regional backgrounds was developed using rapeseed as an example.
● Ground hyperspectral characteristics of rapeseed and other typical crops in rapeseed planting areas were analyzed and extended to Landsat-8 imagery to develop the flowering index of rapeseed (FI-R).
● FI-R shows low sensitivity to background complexity and rapeseed varieties, and it has good applicability to multiple multi-spectral sensor images.

Abstract
References

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摘要

植被精细地表信息是研究资源分布状况和环境响应动态变化规律的基础，精确提取不同种属作物的分布对于提升农业生产效率和保障粮食安全具有重要意义。传统的植被精细提取往往因为存在大量具有光谱相似性的地物，模型受背景影响严重，难以在大区域应用。而花期作为植物关键生理周期，在开花时间、花朵形态以及冠层光谱表现等方面都具有独特性，是遥感植被精细提取的有效途径。本研究以油菜为例，选择黄度指标(Blue、Green)和高峰指标(Red、Nir、Swir1)，提出了一种基于Landsat OLI影像的开花植被花期精细提取光谱指数模型(FI-R)，利用NDVI简化同谱异物现象背景影响，成功实现在复杂背景条件下，快速、准确地绘制大范围开花植被分布地图。选择全球五个不同背景的油菜种植区为研究区，验证数据集通过GF影像以及美国CDL农田数据集生成，FI-R表现出更好的区分花期油菜和其它植被的能力，所有研究区总体精度大于94%。FI-R也可以应用于其它相似波段设置的多光谱传感器的油菜提取。该方法在其它开花被子植被精细提取上也具有良好的应用潜力。

Abstract

The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses. Accurately extracting the distributions of different crop species is of critical importance for improving agricultural production efficiency and ensuring food security. Traditional fine-scale vegetation extraction methods often face significant challenges due to the presence of spectrally similar features and the substantial influence of background interference, which limit their applicability across large areas. As a key phenological stage of angiosperms, flowering is characterized by distinctive flowering times, floral morphology, and canopy spectral signatures, so it is an effective pathway for fine-scale vegetation extraction using remote sensing. Using rapeseed as an example, this study developed a spectral index model for precise flowering vegetation extraction (FI-R) based on Landsat OLI imagery. The model integrates a yellowness index (Blue, Green) and a peak index (Red, Nir and SWIR1) while leveraging the NDVI to mitigate background interference from spectrally similar objects. This approach successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions. The proposed method was tested in five rapeseed cultivation regions worldwide with diverse backgrounds. Validation datasets were generated using GF imagery and the U.S. CDL dataset. The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation, and achieved overall accuracies exceeding 94% in all study areas. Furthermore, FI-R is compatible with other multispectral sensors that have similar band configurations, so it is applicable to rapeseed extraction in broader contexts. The method also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation.

Keywords: flowering rapeseed multispectral imagery precise classification spectral indices FI-R

Received: 16 January 2025 Accepted: 01 April 2025 Online: 14 May 2025

Fund:

This research was supported by the National Natural Science Foundation of China (42201339), and the “Science for a Better Development of Inner Mongolia” Program of the Bureau of Science and Technology of the Inner Mongolia Autonomous Region, China (2022EEDSKJXM003).

About author: Sixian Yin, E-mail: yinsx@hhu.edu.cn; #Correspondence Taixia Wu, E-mail: wutx@hhu.edu.cn; Hongzhao Tang, E-mail: tanghz@pku.edu.cn

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Sixian Yin, Taixia Wu, Shudong Wang, Ran Chen, Yingying Yang, Hongzhao Tang. 2026. Development of the FI-R model, a novel remote sensing method for fine-scale extraction of vegetation, using rapeseed as an example. Journal of Integrative Agriculture, 25(3): 1223-1242.

Asen S. 1975. Factors affecting flower colour. Acta Horticulturae, 41, 57–68.

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 Y, Li G L, Luo Y K, Pan Q, Zhang S Y. 2020. Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images. Computers and Electronics in Agriculture, 171, 105331.

Chen B, Jin Y, Brown P. 2019. An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 108–120.

Chen J, Shen M, Zhu X, Tang Y. 2009. Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan plateau. Ecological Indicators, 9, 818–823.

Chen M, Zhang R, Jia M, Cheng L, Zhao C, Li H, Wang Z. 2024. Accurate and rapid extraction of aquatic vegetation in the china side of the Amur River Basin based on Landsat imagery. Remote Sensing, 16, 654.

Cleland E, Chuine I, Menzel A, Mooney H, Schwartz M. 2007. Shifting plant phenology in response to global change. Trends in Ecology & Evolution, 22, 357–365.

Freden S C, Mercanti E P, Becker M A. 1974. Third Earth Resources Technology Satellite-1 Symposium: Section A–B. Technical Presentations. Scientific and Technical Information Office, National Aeronautics and Space Administration, USA.

Fu B, Deng L, Sun W, He H, Li H, Wang Y, Wang Y Q. 2024a. Quantifying vegetation species functional traits along hydrologic gradients in karst wetland based on 3D mapping with UAV hyperspectral point cloud. Remote Sensing of Environment, 307, 114160.

Fu B, Wu Y, Zhang S, Sun W, Jia M, Deng T, He H, Yuan B, Fan D, Wang Y. 2024b. Synergistic retrieval of mangrove vital functional traits using field hyperspectral and satellite data. International Journal of Applied Earth Observation and Geoinformation, 131, 103963.

Gao L, Brian A J, Tian Q, Wang Y, Verrelst J, Mu X, Gu X, 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 364–377.

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.

Grotewold E. 2006. The genetics and biochemistry of floral pigments. Annual Review of Plant Biology, 57, 761–780.

Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.

Ienco D, Interdonato R, Gaetano R, Ho Tong Minh D . 2019. Combining sentinel-1 and sentinel-2 satellite image time series for land cover mapping via a multi-source deep learning architecture. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 11–22.

Kennedy R E, Andréfouët S, Cohen W B, Gómez C, Griffiths P, Hais M, Healey S P, Helmer E H, Hostert P, Lyons M B, Meigs G W, Pflugmacher D, Phinn S R, Powell S L, Scarth P, Sen S, Schroeder T A, Schneider A, Sonnenschein R, Vogelmann J E, et al. 2014. Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment, 12, 339–346.

Liu L, Xiao X, Qin Y, Wang J, Xu X, Hu Y, Qiao Z. 2020. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 239, 111624.

Lyu X, Li X, Dang D, Dou H, Xuan X, Liu S, Li M, Gong J. 2020. A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing. Ecological Indicators, 114, 106310.

Meng S, Zhong Y, Luo C, Hu X, Wang X, Huang S. 2020. Optimal temporal window selection for winter wheat and rapeseed mapping with Sentinel-2 images: A case study of Zhongxiang in China. Remote Sensing, 12, 226.

Meng Z, Song F, Huo J, Zhang M, Yang F, Zheng W, Liu C. 2023. Comparative analysis on light-temperature resource use efficiency of spring rapeseed (Brassica napus) differing in maturity in China Tibet under plateau climate. Chinese Journal of Oil Crop Sciences, 45, 63–71. (in Chinese)

Narbona E, Del Valle J C, Arista M, Buide M L, Ortiz P L. 2021. Major flower pigments originate different colour signals to pollinators. Frontiers in Ecology and Evolution, 9, 743850.

Nuijten R J G, Coops N C, Theberge D, Prescott C E. 2024. Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring. Science of Remote Sensing, 9, 100114.

Pandey A, Jain K, 2022. An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Computers and Electronics in Agriculture. 192, 106543.

Parmesan C, Yohe G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42.

Ribeiro A L A, Maciel G M, Siquieroli A C S, Luz J M Q, Gallis R B D A, Assis P H D S, Catão H C R M, Yada R Y. 2023. Vegetation indices for predicting the growth and harvest rate of lettuce. Agriculture, 13, 1091.

Shen M, Chen J, Zhu X, Tang Y. 2009. Yellow flowers can decrease NDVI and EVI values: Evidence from a field experiment in an alpine meadow. Canadian Journal of Remote Sensing, 35, 99–106.

Song Z, Zhang Z, Yang S, Ding D, Ning J. 2020. Identifying sunflower lodging based on image fusion and deep semantic segmentation with UAV remote sensing imaging. Computers and Electronics in Agriculture, 179, 105812.

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

Sulik J J, Long D S. 2015. Spectral indices for yellow canola flowers. International Journal of Remote Sensing, 36, 2751–2765.

Sun G, Jiao Z, Zhang A, Li F, Fu H, Li Z. 2021. Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research. International Journal of Applied Earth Observation and Geoinformation, 103, 102529.

Szantoi Z, Escobedo F, Abd-Elrahman A, Smith S, Pearlstine L. 2013. Analyzing fine-scale wetland composition using high resolution imagery and texture features. International Journal of Applied Earth Observation and Geoinformation, 23, 204–212.

Tian H, Chen T, Li Q, Mei Q, Wang S, Yang M, Wang Y, Qin Y. 2022. A novel spectral index for automatic canola mapping by using Sentinel-2 imagery. Remote Sensing, 14, 1113.

Timm B C, McGarigal K. 2012. Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at cape cod national seashore using random forests. Remote Sensing of Environment, 127, 106–117.

Vásquez R A R, Heenkenda M K, Nelson R, Segura Serrano L. 2023. Developing a new vegetation index using cyan, orange, and near infrared bands to analyze soybean growth dynamics. Remote Sensing, 15, 2888.

Weis s M, Jacob F, Duveiller G. 2020. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402.

Wong C Y S. 2023. Plant optics: Underlying mechanisms in remotely sensed signals for phenotyping applications. AoB Plants, 15, plad039.

Wouters N, De Ketelaere B, De Baerdemaeker J, Saeys W. 2013. Hyperspectral waveband selection for automatic detection of floral pear buds. Precision Agriculture, 14, 86–98.

Wu T, Li G, Yang Z, Zhang H, Lei Y, Wang N, Zhang L. 2017. Shortwave infrared imaging spectroscopy for analysis of ancient paintings. Applied Spectroscopy, 71, 977–987.

Yan S, Yao X, Zhu D, Liu D, Zhang L, Yu G, Gao B, Yang J, Yun W. 2021. Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids. International Journal of Applied Earth Observation and Geoinformation, 103, 102485.

Yan Y, Deng L, Liu X, Zhu L. 2019. Application of UAV-based multi-angle hyperspectral remote sensing in fine vegetation classification. Remote Sensing, 11, 2753.

Yang G, Huang K, Sun W, Meng X, Mao D, Ge Y. 2022. Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 236–254.

Yao X, Yi Q, Wang F, Xu T, Zheng J, Shi Z. 2023. Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 122, 103415.

Yu Q, Hu Q, van Vliet J, Verburg P H, Wu W. 2018. GlobeLand30 shows little cropland area loss but greater fragmentation in China. International Journal of Applied Earth Observation and Geoinformation, 66, 37–45.

Zamani-Noor N, Feistkorn D. 2022. Monitoring growth status of winter oilseed rape by NDVI and NDYI derived from UAV-Based Red–Green–Blue imagery. Agronomy, 12, 2212.

Zhang B, Liu C, Wang Y, Yao X, Wang F, Wu J, King G J, Liu K. 2015. Disruption of a CAROTENOID CLEAVAGE DIOXYGENASE 4 gene converts flower colour from white to yellow in Brassica species. New Phytologist, 206, 1513–1526.

Zhang H, Flottmann S. 2018. Source-sink manipulations indicate seed yield in canola is limited by source availability. European Journal of Agronomy, 96, 70–76.

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 Y, Wang D, Zhou Q. 2019. Advances in crop fine classification based on hyperspectral remote sensing. In: 2019 8th International Conference on Agro-Geoinformatics. Institute of Electrical and Electronics Engineers, USA. pp. 1–6.

Zhang Z, Jin W, Dou R, Cai Z, Wei H, Wu T, Yang S, Tan M, Li Z, Wang C, Yin G, Xu B. 2023. Improved estimation of leaf area index by reducing leaf chlorophyll content and saturation effects based on red-edge bands. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14.

Zheng Q, Huang W, Cui X, Shi Y, Liu L. 2018. New spectral index for detecting wheat yellow rust using Sentinel-2 multispectral imagery. Sensors (Basel, Switzerland), 18, 868.

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