Monitoring the impacts of waterlogging on winter wheat using high spatial resolution satellite data

被引:0
|
作者
Liu, Weiwei [1 ]
Huang, Jingfeng [1 ]
Song, Xiaodong [1 ]
Wei, Chuanwen [2 ]
Zhang, Dongdong [2 ]
Wang, Xiuzhen [3 ]
Zhang, Lijie [3 ]
Zhou, Zhen [4 ]
Han, Jiahui [4 ]
Chen, Yaoliang [5 ]
机构
[1] Zhejiang Univ, Inst Remote Sensing & Informat Technol Applicat, Hangzhou, Zhejiang, Peoples R China
[2] Key Lab Agr Remote Sensing & Informat Syst, Hangzhou, Zhejiang, Peoples R China
[3] Hangzhou Normal Univ Hangzhou, Inst Remote Sensing & Earth Sci, Hangzhou, Zhejiang, Peoples R China
[4] Coll Environm & Resource Sci, Minist Educ, Key Lab Environm Remediat & Ecol Hlth, Hangzhou, Zhejiang, Peoples R China
[5] Zhejiang Univ, Sch Publ Affairs, Dept Land Management, Hangzhou, Zhejiang, Peoples R China
来源
2016 FIFTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS) | 2016年
关键词
waterlogging; remote sensing; winter wheat; EMPIRICAL LINE METHOD; REMOTE-SENSING DATA; METEOROLOGICAL DROUGHT; REFLECTANCE; MULTISENSOR; RETRIEVAL; INDEXES; BARLEY; YIELD;
D O I
暂无
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Waterlogging, including flooding, is one of the serious agricultural disasters for winter wheat in the middle and lower reaches of the Yangtze River region, especially during the spring. It's of great practical value to discriminating the impact of waterlogging and flooding on winter wheat efficiently in the field. In this study, we combined the field campaign and satellite remote sensing data to discriminate the impact of waterlogging and flooding on winter wheat during the growing stages in the spring. The field experiment was carried out with three treatments, i.e., waterlogging (WL), flooding (FL)(waterlogging with 3 cm water layer above the ground) and contrast (CK), and two varieties, i.e., YM14 and YM18. The phenological stages includes seeding and tillering (ST) from 7 to 27 January and jointing and booting (JB) from 12 March to 1 April, respectively. For each treatment, three samples with an area of 0.25 m(2) were randomly determined and collected. High resolution satellite data, i.e., WorldView2, Pleiades and SPOT were also obtained. We proposed a method to calibrate the collection of high resolution remote sensing data. We used a measurement, namely the M, to differentiate the different waterlogging treatments. The results showed that the yield of winter wheat under different treatments for both varieties in ST and JB had significant differences. It showed that the flooding treatment could be discriminated from its CK and waterlogging treatment for YM14, and the waterlogging treatment of YM18 could be discriminated from its CK 16 days after ST treatment. The treatment in ST showed more lasting impacts on both YM14 and YM18. Compared with ST, the treatments in JB had more severe impacts on winter wheat growth. The flooding treatment of YM14 was differentiable from its CK and waterlogging treatment before the end of JB treatment, earlier than YM18. We concluded that both of the treatments in ST and JB periods had adverse impacts on YM14 and YM18, especially during ST period.
引用
收藏
页码:197 / 201
页数:5
相关论文
共 50 条
  • [21] CAPTURING CORN AND SOYBEAN YIELD VARIABILITY AT FIELD SCALE USING VERY HIGH SPATIAL RESOLUTION SATELLITE DATA
    Skakun, S.
    Brown, M. G. L.
    Roger, J-C
    Vermote, E.
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 3723 - 3726
  • [22] Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method
    Wei, Chuanwen
    Huang, Jingfeng
    Mansaray, Lamin R.
    Li, Zhenhai
    Liu, Weiwei
    Han, Jiahui
    REMOTE SENSING, 2017, 9 (05)
  • [23] Mapping urban and peri-urban agriculture using high spatial resolution satellite data
    Forster, Dionys
    Buehler, Yves
    Kellenberger, Tobias W.
    JOURNAL OF APPLIED REMOTE SENSING, 2009, 3
  • [24] Winter wheat growth and grain protein uniformity monitoring through remotely sensed data
    Song Xiaoyu
    Wang Jihua
    Huang Wenjiang
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XII, 2010, 7824
  • [25] Potentiality of optical and radar satellite data at high spatio-temporal resolutions for the monitoring of irrigated wheat crops in Morocco
    Hadria, R.
    Duchemin, B.
    Jarlan, L.
    Dedieu, G.
    Baup, F.
    Khabba, S.
    Olioso, A.
    Le Toan, T.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2010, 12 : S32 - S37
  • [26] High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier
    Mirik, Mustafa
    Ansley, R. James
    Steddom, Karl
    Rush, Charles M.
    Michels, Gerald J.
    Workneh, Fekede
    Cui, Song
    Elliott, Norman C.
    JOURNAL OF APPLIED REMOTE SENSING, 2014, 8
  • [27] Object-based feature extraction using high spatial resolution satellite data of urban areas
    Taubenboeck, H.
    Esch, T.
    Wurm, M.
    Roth, A.
    Dech, S.
    JOURNAL OF SPATIAL SCIENCE, 2010, 55 (01) : 117 - 132
  • [28] Estimating leaf area index of a degraded mangrove forest using high spatial resolution satellite data
    Kovacs, JM
    Flores-Verdugo, F
    Wang, JF
    Aspden, LP
    AQUATIC BOTANY, 2004, 80 (01) : 13 - 22
  • [29] Quantifying mortality of tropical rain forest trees using high-spatial-resolution satellite data
    Clark, DB
    Castro, CS
    Alvarado, LDA
    Read, JM
    ECOLOGY LETTERS, 2004, 7 (01) : 52 - 59
  • [30] Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters
    YANG Fei-fei
    LIU Tao
    WANG Qi-yuan
    DU Ming-zhu
    YANG Tian-le
    LIU Da-zhong
    LI Shi-juan
    LIU Sheng-ping
    JournalofIntegrativeAgriculture, 2021, 20 (10) : 2613 - 2626