Information Content of Spectral Vegetation Indices for Assessing the Weed Infestation of Crops Using Ground-Based and Satellite Data

被引:2
|
作者
Pisman, T., I [1 ]
Erunova, M. G. [2 ]
Botvich, I. Yu [1 ]
Emelyanov, D., V [1 ]
Kononova, N. A. [1 ]
Bobrovsky, A., V [3 ]
Kryuchkov, A. A. [3 ]
Shpedt, A. A. [3 ]
Shevyrnogov, A. P. [1 ]
机构
[1] Russian Acad Sci, Inst Biophys, Siberian Branch, Krasnoyarsk, Russia
[2] Russian Acad Sci, Fed Res Ctr, Siberian Branch, Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia
[3] Russian Acad Sci, Fed Res Ctr, Krasnoyarsk Sci Res Inst Agr, Siberian Branch,Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia
关键词
vegetation indices; PlanetScope; ground-based spectrometry; geobotanical studies; wheat crops; Krasnoyarsk krai; DIFFERENTIATION; REFLECTANCE;
D O I
10.1134/S0001433821090577
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
This paper presents the results of a study assessing the degree of weed infestation of wheat crops. They are obtained using optical ground-based and satellite spectral data with a 3-m spatial resolution from PlanetScope Dove satellites for 2019. The vegetation indices, including the normalized difference vegetation index (NDVI), the relative chlorophyll index (Chlorophyll Index Green-ClGreen or GCI), the modified soil-adjusted vegetation index (MSAVI2), and the visible atmospherically resistant index (VARI) are used in the interpretation of ground-based spectrometric and space images. This paper indicates the possibility of assessing the degree of weed infestation of agricultural fields. The higher the weed infestation, the lower the index values. The dynamics of VARI is found to be different from the dynamics of NDVI, ClGreen, and MSAVI2 during the growing season. The strong correlation between NDVI, ClGreen, and MSAVI2 and the weak correlation between VARI and other indices are observed. The possibility of identifying weedy sites in the agricultural fields is shown using the spatial distribution map of ClGreen dated August 2, 2019.
引用
收藏
页码:1188 / 1197
页数:10
相关论文
共 50 条
  • [31] STUDY OF SOIL RESPONS UNDER A VEGETATION LAYER USING TOMSAR DATA AND GROUND-BASED TOMSAR DATA
    Lahlou, Nabil
    Ferro-Famil, Laurent
    Allain-Bailhache, Sophie
    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 1324 - 1327
  • [32] EVALUATING EVAPORATION FROM FIELD CROPS USING AIRBORNE RADIOMETRY AND GROUND-BASED METEOROLOGICAL DATA
    JACKSON, RD
    MORAN, MS
    GAY, LW
    RAYMOND, LH
    IRRIGATION SCIENCE, 1987, 8 (02) : 81 - 90
  • [33] Assessing Nitrogen Status in Potted Geranium through Discriminant Analysis of Ground-based Spectral Reflectance Data
    Wang, Yun-wen
    Dunn, Bruce L.
    Arnall, Daryl B.
    HORTSCIENCE, 2012, 47 (03) : 343 - 348
  • [34] Spectral indices based object oriented classification for change detection using satellite data
    Bhatt A.
    Ghosh S.K.
    Kumar A.
    International Journal of System Assurance Engineering and Management, 2018, 9 (1) : 33 - 42
  • [35] An Intercomparison of the Spatiotemporal Variability of Satellite- and Ground-Based Cloud Datasets Using Spectral Analysis Techniques
    Li, Jing
    Carlson, Barbara E.
    Rossow, William B.
    Lacis, Andrew A.
    Zhang, Yuanchong
    JOURNAL OF CLIMATE, 2015, 28 (14) : 5716 - 5736
  • [36] COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA
    Wang, Yanjie
    Liao, Qinhong
    Yang, Guijun
    Feng, Haikuan
    Yang, Xiaodong
    Yue, Jibo
    XXIII ISPRS CONGRESS, COMMISSION VII, 2016, 41 (B7): : 137 - 143
  • [37] QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize
    Bausch, W. C.
    Khosla, R.
    PRECISION AGRICULTURE, 2010, 11 (03) : 274 - 290
  • [38] QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize
    W. C. Bausch
    R. Khosla
    Precision Agriculture, 2010, 11 : 274 - 290
  • [39] An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data
    Bertini, Claudia
    Ridolfi, Elena
    Resende de Padua, Luiz Henrique
    Russo, Fabio
    Napolitano, Francesco
    Alfonso, Leonardo
    HYDROLOGY RESEARCH, 2021, 52 (03): : 620 - 635
  • [40] PRELIMINARY RADIOMETRIC PERFORMANCE EVALUATION OF ISS HISUI USING SATELLITE-BASED AND GROUND-BASED DATA
    Yamamoto, Hirokazu
    Tsuchida, Satoshi
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 2996 - 2999