Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data

被引:3
|
作者
Yang Xu [1 ]
Lu Xue-he [1 ]
Shi Jing-ming [1 ]
Li Jing [1 ]
Ju Wei-min [1 ]
机构
[1] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China
关键词
Leaf chlorophyll content; Remote sensing inversion; Sentinel-2; PROSAIL; VEGETATION INDEXES; REFLECTANCE; RETRIEVAL;
D O I
10.3964/j.issn.1000-0593(2022)03-0866-07
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Chlorophyll content is an important indicator of crop health, plant productivity, and environmental stress. Real-time, fast and accurate acquisition of leaf chlorophyll content of crops is of significant for monitoring crop growth. Remote sensing is an effective way to retrieve leaf chlorophyll content of crops at regional and global scales. However, previous studies retrieving leaf chlorophyll content of crops does not fully consider the impact of underlying surface background, limiting retrieval accuracy. To this end, this paper aims at the inversion of rice leaf chlorophyll content from Sentinel-2 remote sensing satellite data using a look-up table based approach. The look-up table was simulated using the PRAOSAIL radiation transfer model. The applicability of chlorophyll indices (CI) calculated from the reflectance of the green band and different red-edge bands and the spectral index (Zarco and Miller, ZM) constructed by two different red edge bands in inverting leaf chlorophyll content was evaluated using field measurements. The greenness index (G) was integrated with CI and ZM to constrain the impact of background on the inversion of leaf chlorophyll content. The main findings of this study are: (1) The accuracy of leaf element content inversion based on the spectral index constructed in different bands is different, and CI740 performed the best (R-2 = 0. 79, RMSE= 9. 02 mu g.cm(-2)), followed by ZM (R-2 =0. 71, RMSE=10. 53 mu g.cm(-2)) , CI705 (R-2 =0. 69, RMSE=9. 17 mu g.cm(-2)), and CI783 (R-2 =0. 67, RMSE= 10. 84 mu g.cm(-2)); (2) The inverted leaf chlorophyll content is significantly affected by the background, especially at the early stage of rice growth. The inverted leaf chlorophyll content was systematically lower than observations (mean relative error (MRE) in the range from 18. 87% to 31. 94%) owing to strong background interference; (3) CI/G and ZM/G can effectively eliminate the influence of background and improve the accuracy of rice leaf chlorophyll inversion. At the early stage of rice growth, inversion based on CI/G and ZM/G significantly improves agreement between inverted and observed leaf chlorophyll content (MRE in the range from 8. 11% to 18.11%). These findings are of great significance for improving the inversion of leaf chlorophyll content under different leaf area index levels of rice from remote sensing data.
引用
收藏
页码:866 / 872
页数:7
相关论文
共 18 条
  • [1] Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches
    Blackburn, GA
    [J]. REMOTE SENSING OF ENVIRONMENT, 1998, 66 (03) : 273 - 285
  • [2] Retrieval of canopy biophysical variables from bidirectional reflectance -: Using prior information to solve the ill-posed inverse problem
    Combal, B
    Baret, F
    Weiss, M
    Trubuil, A
    Macé, D
    Pragnère, A
    Myneni, R
    Knyazikhin, Y
    Wang, L
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 84 (01) : 1 - 15
  • [3] The global distribution of leaf chlorophyll content
    Croft, H.
    Chen, J. M.
    Wang, R.
    Mo, G.
    Luo, S.
    Luo, X.
    He, L.
    Gonsamo, A.
    Arabian, J.
    Zhang, Y.
    Simic-Milas, A.
    Noland, T. L.
    He, Y.
    Homolova, L.
    Malenovsky, Z.
    Yi, Q.
    Beringer, J.
    Amiri, R.
    Hutley, L.
    Arellano, P.
    Stahl, C.
    Bonal, D.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2020, 236
  • [4] The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures
    Croft, H.
    Chen, J. M.
    Zhang, Y.
    [J]. ECOLOGICAL COMPLEXITY, 2014, 17 : 119 - 130
  • [5] Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach
    Darvishzadeh, Roshanak
    Matkan, Ali A.
    Ahangar, Abdolhamid Dashti
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (04) : 1222 - 1230
  • [6] PROSPECT-4 and 5:: Advances in the leaf optical properties model separating photosynthetic pigments
    Feret, Jean-Baptiste
    Francois, Christophe
    Asner, Gregory P.
    Gitelson, Anatoly A.
    Martin, Roberta E.
    Bidel, Luc P. R.
    Ustin, Susan L.
    le Maire, Guerric
    Jacquemoud, Stephane
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (06) : 3030 - 3043
  • [7] Remote estimation of chlorophyll content in higher plant leaves
    Gitelson, AA
    Merzlyak, MN
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1997, 18 (12) : 2691 - 2697
  • [8] Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model
    Hilker, Thomas
    Galvao, Lenio Soares
    Aragao, Luiz E. O. C.
    de Moura, Yhasmin M.
    do Amaral, Cibele H.
    Lyapustin, Alexei I.
    Wu, Jin
    Albert, Loren P.
    Ferreira, Marciel Jose
    Anderson, Liana O.
    dos Santos, Victor A. H. F.
    Prohaska, Neill
    Tribuzy, Edgard
    Barbosa Ceron, Joao Vitor
    Saleska, Scott R.
    Wang, Yujie
    de Carvalho Goncalves, Jose Francisco
    de Oliveira Junior, Raimundo Cosme
    Figueiredo Cardoso Rodrigues, Joao Victor
    Garcia, Maquelle Neves
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2017, 58 : 278 - 287
  • [9] Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data
    Houborg, Rasmus
    Soegaard, Henrik
    Boegh, Eva
    [J]. REMOTE SENSING OF ENVIRONMENT, 2007, 106 (01) : 39 - 58
  • [10] Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP
    Houborg, Rasmus
    Cescatti, Alessandro
    Migliavacca, Mirco
    Kustas, W. P.
    [J]. AGRICULTURAL AND FOREST METEOROLOGY, 2013, 177 : 10 - 23