数字农科院2.0

The normalized difference yellow vegetation index (NDYVI): A new index for crop identification by using GaoFen-6 WFV data

文献类型： 外文期刊

作者： Yanbing Wei;Miao Lu;Qiangyi Yu;Wenjuan Li;Cong Wang;Huajun Tang;Wenbin Wu

作者机构：

关键词： Crop identification;GaoFen-6 WFV;NDYVI;Vegetation index;Yellowing morphology

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2024 年 226 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： The yellowing morphologies of crops provide typical spectral characteristics for crop identification. However, this feature was generally neglected by most existing vegetation indices (VIs) focused on greening feature. Chinese GaoFen-6 satellite (GF-6) equipped with a wide field-of-view (WFV) camera has a refined spectral system within 0.40–0.89 μm, including the spectral bands that are sensitive to yellowing feature. This study proposes a new normalized difference yellow vegetation index (NDYVI) based on GF-6 image, by capitalizing on spectral reflectance feature of crops with yellowing morphologies, such as flowers and tassels. We used yellow and red-edge1 band to discriminate between crops within similar growing periods and incorporated NIR band to distinguish non-crop types. The performance of NDYVI was evaluated in two distinct classification scenarios involving different cropping systems: rapeseed with winter wheat in southern China, and maize with soybean in northeastern China. By calculating NDYVI and using Classification and Regression Tree (CART) algorithm, we generated classification maps in two scenarios. Additionally, the effectiveness of NDYVI was tested and compared with other six VIs, such as Normalized Difference Vegetation Index, Red-Edge Normalized Difference Vegetation Index and Normalized Difference Yellowness Index. The results demonstrated that NDYVI outperformed the other vegetation indices in both scenarios, achieving overall accuracies over 85 % (Kappa coefficient greater than 0.80) and each crop accuracy exceeding 80 %. Due to the higher reflectance in yellow band and red-edge1 band, NDYVI is more sensitive to canopies yellowness, which offers significant advantages in distinguishing crops during similar growing periods. Moreover, NDYVI is constructed from original spectral bands in GF-6 images, offering potential for significant flexibility in diverse classification scenarios. Consequently, NDYVI holds significant potential as a new vegetation index suitable for different remote sensing applications, including crop identification, growth monitoring and land cover classification.

分类号：

  • 相关文献

[1]A review of crop identification and area monitoring based on SAR image. Ding Yaping,Chen Zhongxin,Ding Yaping,Chen Zhongxin. 2012

[2]Discriminant models for crop identification of complex agricultural landscapes. You, Jiong,Pei, Zhiyuan,Wang, Dongliang. 2013

[3]Advances in research on crop identification using SAR. Di, Wang,Zhou Qingbo,Chen Zhongxin,Yan, Su. 2015

[4]Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images. Hu, Qiong,Wu, Wenbin,Yu, Qiangyi,Lu, Miao,Yang, Peng,Tang, Huajun,Long, Yuqiao,Song, Qian,Song, Qian.

[5]How Do Temporal And Spectral F.eatures Matter In Crop C lassification In Heilongjiang Province, China?. Hu, Q, Wu, WB, Song, Q, Lu, M, Chen, D, Yu, QY, Tang, HJ. 2017

[6]Comparative Analysis of Machine Learning Algorithms for Object-Based Crop Classification Using Multispectral Imagery. Madjebi Collela Be,Antsa Sarobidy Randrianantenaina,James E. Kanneh,Yingchun Han,Yaping Lei,Xiaoyu Zhi,Shiwu Xiong,Yahui Jiao,Shilong Shang,Yunzhen Ma,Beifang Yang,Lin Tao,Yabing Li. 2025

[7]Effects of vegetation indices to the spatial changes of desert environment using EOS/MODIS data: A case study to Sangong inland arid ecosystem. Lu, Liping,Qin, Zhihao,Qin, Zhihao,Gao, Maofang,Zhao, Chengyi,Li, Wenjuan. 2006

[8]Comparison of two methods for monitoring leaf total chlorophyll content (LTCC) of wheat using field spectrometer data. Jin, X.,Wang, K.,Li, S.,Jin, X.,Diao, W.,Xiao, C.,Wang, K.,Li, S.,Wang, F.,Chen, B..

[9]Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Liang, Liang,Zhang, Lianpeng,Lin, Hui,Liang, Liang,Zhao, Shuhe,Liang, Liang,Di, Liping,Deng, Meixia,Qin, Zhihao.

[10]Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Tao, FL,Yokozawa, M,Zhang, Z,Xu, YL,Hayashi, Y.

[11]New fast detection method of forest fire monitoring and application based on FY-1D/MVISR data. Feng, Jianzhong,Tang, Huajun,Zhou, Qingbo,Chen, Zhongxin,Bai, Linyan,Feng, Jianzhong. 2008

[12]Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring. Peng, D.,Hu, Y.,Li, Z..

[13]Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. Liu, Zhan-Yu,Huang, Jing-Feng,Wu, Xin-Hong,Dong, Yong-Ping. 2007

[14]Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method. Ju, Chengxin,Chen, Chen,Li, Rui,Zhao, Yuanyuan,Zhong, Xiaochun,Sun, Ruilin,Liu, Tao,Sun, Chengming. 2023

[15]Replacing The Red Band With T.he Red-Swir Band (0.74(Red)+0.26(Swir)) C an Reduce The Sensitivity Of Vegetation Indices To Soil Background. Chen, XH, Guo, ZF, Chen, J, Yang, W, Yao, YM, Zhang, CS, Cui, XH, Cao, X. 2019

[16]Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning. Bin Ma,Guangqiao Cao,Chaozhong Hu,Cong Chen. 2023

[17]Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data. Da zhong LIU,Fei fei YANG,Sheng ping LIU. 2021

[18]The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Dawei Xu,Cong Wang,Jin Chen,Miaogen Shen,Beibei Shen,Ruirui Yan,Zhenwang Li,Arnon Karnieli,Jiquan Chen,Yuchun Yan,Xu Wang,Baorui Chen,Dameng Yin,Xiaoping Xin. 2021

[19]The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Dawei Xu,Cong Wang,Jin Chen,Miaogen Shen,Beibei Shen,Ruirui Yan,Zhenwang Li,Arnon Karnieli,Jiquan Chen,Yuchun Yan,Xu Wang,Baorui Chen,Dameng Yin,Xiaoping Xin. 2021

[20]Quantifying effect of tassels on near-ground maize canopy RGB images using deep learning segmentation algorithm. Shao, Mingchao,Nie, Chenwei,Cheng, Minghan,Yu, Xun,Bai, Yi,Ming, Bo,Song, Hongli,Jin, Xiuliang. 2021

作者其他论文 更多>>