22.2.5 Chlorophyll Estimation, Chlorophyll Concentration, Chlorophyll Index

Chapter Contents (Back)
Chlorophyll.
See also Chlorophyll Estimation in Water.
See also Canopy Water Content.
See also Cyanobacteria, Analysis, Detection. Some related papers:
See also Plankton Analysis, Extraction, Features, Small Scale and Large Scale.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Main, R.[Russell], Cho, M.A.[Moses Azong], Mathieu, R.[Renaud], O'Kennedy, M.M.[Martha M.], Ramoelo, A.[Abel], Koch, S.[Susan],
An investigation into robust spectral indices for leaf chlorophyll estimation,
PandRS(66), No. 6, November 2011, pp. 751-761.
Elsevier DOI 1112
Leaf level reflectance; Leaf chlorophyll; Red-edge; Vegetation indices; Photosynthetic activity BibRef

Fournier, A., Daumard, F., Champagne, S., Ounis, A., Goulas, Y., Moya, I.,
Effect of canopy structure on sun-induced chlorophyll fluorescence,
PandRS(68), No. 1, March 2012, pp. 112-120.
Elsevier DOI 1204
Canopy structure; Chlorophyll fluorescence measurement; Simulation; F687/F760 fluorescence ratio; Oxygen absorption band; Band infilling BibRef

Vuolo, F., Dash, J., Curran, P., Lajas, D., Kwiatkowska, E.,
Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission,
RS(4), No. 5, May 2012, pp. 1112-1133.
DOI Link 1205
BibRef

Yu, K.[Kang], Leufen, G.[Georg], Hunsche, M.[Mauricio], Noga, G.[Georg], Chen, X.P.[Xin-Ping], Bareth, G.[Georg],
Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices,
RS(6), No. 1, 2013, pp. 64-86.
DOI Link 1402
BibRef

Yu, K., Lenz-Wiedemann, V.I.S., Leufen, G., Hunsche, M., Noga, G., Chen, X.P., Bareth, G.,
Assessing Hyperspectral Vegetation Indices for Estimating Leaf Chlorophyll Concentration of Summer Barley,
AnnalsPRS(I-7), No. 2012, pp. 89-94.
HTML Version. 1209
BibRef

Igamberdiev, R., Bill, R., Schubert, H., Lennartz, B.,
Analysis of Cross-Seasonal Spectral Response from Kettle Holes: Application of Remote Sensing Techniques for Chlorophyll Estimation,
RS(4), No. 11, November 2012, pp. 3481-3500.
DOI Link 1211
BibRef

Frampton, W.J.[William James], Dash, J.[Jadunandan], Watmough, G.[Gary], Milton, E.J.[Edward James],
Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation,
PandRS(82), No. 1, August 2013, pp. 83-92.
Elsevier DOI 1306
Vegetation; Sentinel-2; Chlorophyll; Red-Edge; LAI BibRef

Rivera, J.P.[Juan Pablo], Verrelst, J.[Jochem], Leonenko, G.[Ganna], Moreno, J.[José],
Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model,
RS(5), No. 7, 2013, pp. 3280-3304.
DOI Link 1308
BibRef

Verrelst, J.[Jochem], Rivera, J.P.[Juan Pablo], Moreno, J.[José], Camps-Valls, G.[Gustavo],
Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval,
PandRS(86), No. 1, 2013, pp. 157-167.
Elsevier DOI 1312
Uncertainty estimates BibRef

Sanches, I.D.[Ieda Del'Arco], de Souza Filho, C.R.[Carlos Roberto], Kokaly, R.F.[Raymond Floyd],
Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680nm absorption feature with continuum removal,
PandRS(97), No. 1, 2014, pp. 111-122.
Elsevier DOI 1410
Hyperspectral BibRef

Amaral, C.H.[Cibele H.], Roberts, D.A.[Dar A.], Almeida, T.I.R.[Teodoro I.R.], de Souza Filho, C.R.[Carlos Roberto],
Mapping invasive species and spectral mixture relationships with neotropical woody formations in southeastern Brazil,
PandRS(108), No. 1, 2015, pp. 80-93.
Elsevier DOI 1511
Invasive species BibRef

Yu, K.[Kang], Lenz-Wiedemann, V.[Victoria], Chen, X.[Xinping], Bareth, G.[Georg],
Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects,
PandRS(97), No. 1, 2014, pp. 58-77.
Elsevier DOI 1410
Leaf chlorophyll BibRef

Liu, X.J.[Xin-Jie], Liu, L.Y.[Liang-Yun],
Assessing Band Sensitivity to Atmospheric Radiation Transfer for Space-Based Retrieval of Solar-Induced Chlorophyll Fluorescence,
RS(6), No. 11, 2014, pp. 10656-10675.
DOI Link 1412
BibRef

Croft, H., Chen, J.M., Zhang, Y., Simic, A., Noland, T.L., Nesbitt, N., Arabian, J.,
Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework,
PandRS(102), No. 1, 2015, pp. 85-95.
Elsevier DOI 1503
Leaf area index BibRef

Liu, X.J.[Xin-Jie], Liu, L.Y.[Liang-Yun], Zhang, S.[Su], Zhou, X.F.[Xian-Feng],
New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis,
RS(7), No. 8, 2015, pp. 10626.
DOI Link 1509
BibRef

Julitta, T.[Tommaso], Corp, L.A.[Lawrence A.], Rossini, M.[Micol], Burkart, A.[Andreas], Cogliati, S.[Sergio], Davies, N.[Neville], Hom, M.[Milton], Arthur, A.M.[Alasdair Mac], Middleton, E.M.[Elizabeth M.], Rascher, U.[Uwe], Schickling, A.[Anke], Colombo, R.[Roberto],
Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers,
RS(8), No. 2, 2016, pp. 122.
DOI Link 1603
BibRef

Sabater, N.[Neus], Vicent, J.[Jorge], Alonso, L.[Luis], Verrelst, J.[Jochem], Middleton, E.M.[Elizabeth M.], Porcar-Castell, A.[Albert], Moreno, J.[José],
Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy-Leaving Sun-Induced Chlorophyll Fluorescence,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Pacheco-Labrador, J.[Javier], Hueni, A.[Andreas], Mihai, L.[Laura], Sakowska, K.[Karolina], Julitta, T.[Tommaso], Kuusk, J.[Joel], Sporea, D.[Dan], Alonso, L.[Luis], Burkart, A.[Andreas], Cendrero-Mateo, M.P.[M. Pilar], Aasen, H.[Helge], Goulas, Y.[Yves], Arthur, A.M.[Alasdair Mac],
Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Aasen, H.[Helge], van Wittenberghe, S.[Shari], Medina, N.S.[Neus Sabater], Damm, A.[Alexander], Goulas, Y.[Yves], Wieneke, S.[Sebastian], Hueni, A.[Andreas], Malenovský, Z.[Zbynek], Alonso, L.[Luis], Pacheco-Labrador, J.[Javier], Cendrero-Mateo, M.P.[M. Pilar], Tomelleri, E.[Enrico], Burkart, A.[Andreas], Cogliati, S.[Sergio], Rascher, U.[Uwe], Arthur, A.M.[Alasdair Mac],
Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Cendrero-Mateo, M.P.[M. Pilar], Wieneke, S.[Sebastian], Damm, A.[Alexander], Alonso, L.[Luis], Pinto, F.[Francisco], Moreno, J.[Jose], Guanter, L.[Luis], Celesti, M.[Marco], Rossini, M.[Micol], Sabater, N.[Neus], Cogliati, S.[Sergio], Julitta, T.[Tommaso], Rascher, U.[Uwe], Goulas, Y.[Yves], Aasen, H.[Helge], Pacheco-Labrador, J.[Javier], Arthur, A.M.[Alasdair Mac],
Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Rossini, M.[Micol], Meroni, M.[Michele], Celesti, M.[Marco], Cogliati, S.[Sergio], Julitta, T.[Tommaso], Panigada, C.[Cinzia], Rascher, U.[Uwe], van der Tol, C.[Christiaan], Colombo, R.[Roberto],
Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data,
RS(8), No. 5, 2016, pp. 412.
DOI Link 1606
BibRef

Zhang, C.[Chao], Filella, I.[Iolanda], Garbulsky, M.F.[Martín F.], Peñuelas, J.[Josep],
Affecting Factors and Recent Improvements of the Photochemical Reflectance Index (PRI) for Remotely Sensing Foliar, Canopy and Ecosystemic Radiation-Use Efficiencies,
RS(8), No. 9, 2016, pp. 677.
DOI Link 1610
BibRef

Davila, J.C., Zaremba, M.B.,
An Iterative Learning Framework for Multimodal Chlorophyll-a Estimation,
GeoRS(54), No. 12, December 2016, pp. 7299-7308.
IEEE DOI 1612
hydrological techniques BibRef

de Sousa, C.H.R.[Celio Helder Resende], Hilker, T.[Thomas], Waring, R.[Richard], de Moura, Y.M.[Yhasmin Mendes], Lyapustin, A.[Alexei],
Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Tong, A.[Alexander], He, Y.H.[Yu-Hong],
Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years,
PandRS(126), No. 1, 2017, pp. 146-167.
Elsevier DOI 1704
Spectral indices BibRef

Sonobe, R.[Rei], Wang, Q.[Quan],
Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Li, D.[Dong], Cheng, T.[Tao], Zhou, K.[Kai], Zheng, H.[Hengbiao], Yao, X.[Xia], Tian, Y.[Yongchao], Zhu, Y.[Yan], Cao, W.[Weixing],
WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops,
PandRS(129), No. 1, 2017, pp. 103-117.
Elsevier DOI 1706
Chlorophyll content BibRef

Pinto, F.[Francisco], Müller-Linow, M.[Mark], Schickling, A.[Anke], Cendrero-Mateo, M.P.[M. Pilar], Ballvora, A.[Agim], Rascher, U.[Uwe],
Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Madani, N.[Nima], Kimball, J.S.[John S.], Jones, L.A.[Lucas A.], Parazoo, N.C.[Nicholas C.], Guan, K.[Kaiyu],
Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Rahimzadeh-Bajgiran, P.[Parinaz], Tubuxin, B.[Bayaer], Omasa, K.[Kenji],
Estimating Chlorophyll Fluorescence Parameters Using the Joint Fraunhofer Line Depth and Laser-Induced Saturation Pulse (FLD-LISP) Method in Different Plant Species,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Sabater, N.[Neus], Vicent, J.[Jorge], Alonso, L.[Luis], Cogliati, S.[Sergio], Verrelst, J.[Jochem], Moreno, J.[José],
Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Du, S.S.[Shan-Shan], Liu, L.Y.[Liang-Yun], Liu, X.J.[Xin-Jie], Hu, J.C.[Jiao-Chan],
Response of Canopy Solar-Induced Chlorophyll Fluorescence to the Absorbed Photosynthetically Active Radiation Absorbed by Chlorophyll,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Verrelst, J.[Jochem], Rivera Caicedo, J.P.[Juan Pablo], Muñoz-Marí, J.[Jordi], Camps-Valls, G.[Gustau], Moreno, J.[José],
SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
chlorophyll fluorescence BibRef

Sun, J.[Jia], Shi, S.[Shuo], Yang, J.[Jian], Du, L.[Lin], Gong, W.[Wei], Chen, B.[Biwu], Song, S.[Shalei],
Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval,
PandRS(135), No. Supplement C, 2018, pp. 74-83.
Elsevier DOI 1712
Leaf optical properties, PROSPECT, Hyperspectral data, Biochemistry BibRef

Bertani, G.[Gabriel], Wagner, F.H.[Fabien H.], Anderson, L.O.[Liana O.], Aragão, L.E.O.C.[Luiz E. O. C.],
Chlorophyll Fluorescence Data Reveals Climate-Related Photosynthesis Seasonality in Amazonian Forests,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Ma, X.D.[Xiao-Dan], Feng, J.R.[Jia-Rui], Guan, H.[Haiou], Liu, G.[Gang],
Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Hu, J.C.[Jiao-Chan], Liu, X.J.[Xin-Jie], Liu, L.Y.[Liang-Yun], Guan, L.L.[Lin-Lin],
Evaluating the Performance of the SCOPE Model in Simulating Canopy Solar-Induced Chlorophyll Fluorescence,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Liu, X.J.[Xin-Jie], Guo, J.[Jian], Hu, J.C.[Jiao-Chan], Liu, L.Y.[Liang-Yun],
Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Sala, I.[Iria], Navarro, G.[Gabriel], Bolado-Penagos, M.[Marina], Echevarría, F.[Fidel], García, C.M.[Carlos M.],
High-Chlorophyll-Area Assessment Based on Remote Sensing Observations: The Case Study of Cape Trafalgar,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Blix, K.[Katalin], Eltoft, T.[Torbjørn],
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Lu, X.L.[Xiao-Liang], Liu, Z.[Zhunqiao], Zhou, Y.[Yuyu], Liu, Y.L.[Ya-Ling], An, S.[Shuqing], Tang, J.[Jianwu],
Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Pyo, J.C.[Jong Cheol], Ligaray, M.[Mayzonee], Kwon, Y.S.[Yong Sung], Ahn, M.H.[Myoung-Hwan], Kim, K.[Kyunghyun], Lee, H.[Hyuk], Kang, T.[Taegu], Cho, S.B.[Seong Been], Park, Y.[Yongeun], Cho, K.H.[Kyung Hwa],
High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Hilborn, A.[Andrea], Costa, M.[Maycira],
Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Nichol, C.J.[Caroline J.], Drolet, G.[Guillaume], Porcar-Castell, A.[Albert], Wade, T.[Tom], Sabater, N.[Neus], Middleton, E.M.[Elizabeth M.], MacLellan, C.[Chris], Levula, J.[Janne], Mammarella, I.[Ivan], Vesala, T.[Timo], Atherton, J.[Jon],
Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Campbell, P.K.E.[Petya K. E.], Huemmrich, K.F.[Karl F.], Middleton, E.M.[Elizabeth M.], Ward, L.A.[Lauren A.], Julitta, T.[Tommaso], Daughtry, C.S.T.[Craig S. T.], Burkart, A.[Andreas], Russ, A.L.[Andrew L.], Kustas, W.P.[William P.],
Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Ahlman, L.[Linnéa], Bånkestad, D.[Daniel], Wik, T.[Torsten],
Relation between Changes in Photosynthetic Rate and Changes in Canopy Level Chlorophyll Fluorescence Generated by Light Excitation of Different Led Colours in Various Background Light,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Li, X.[Xing], Xiao, J.F.[Jing-Feng],
A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Jin, J., Wang, Q.,
Selection of Informative Spectral Bands for PLS Models to Estimate Foliar Chlorophyll Content Using Hyperspectral Reflectance,
GeoRS(57), No. 5, May 2019, pp. 3064-3072.
IEEE DOI 1905
vegetation mapping, leaf chemical components, informative spectral bands, informative bands, partial least squares (PLS) BibRef

Román, J.R.[José Raúl], Rodríguez-Caballero, E.[Emilio], Rodríguez-Lozano, B.[Borja], Roncero-Ramos, B.[Beatriz], Chamizo, S.[Sonia], Águila-Carricondo, P.[Pilar], Cantón, Y.[Yolanda],
Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Vanbrabant, Y.[Yasmin], Tits, L.[Laurent], Delalieux, S.[Stephanie], Pauly, K.[Klaas], Verjans, W.[Wim], Somers, B.[Ben],
Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Qiu, F.[Feng], Chen, J.M.[Jing M.], Croft, H.[Holly], Li, J.[Jing], Zhang, Q.[Qian], Zhang, Y.Q.[Yong-Qin], Ju, W.M.[Wei-Min],
Retrieving Leaf Chlorophyll Content by Incorporating Variable Leaf Surface Reflectance in the PROSPECT Model,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Wei, J.[Jin], Tang, X.[Xuguang], Gu, Q.[Qing], Wang, M.[Min], Ma, M.[Mingguo], Han, X.[Xujun],
Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Brown, L.A.[Luke A.], Ogutu, B.O.[Booker O.], Dash, J.[Jadunandan],
Estimating Forest Leaf Area Index and Canopy Chlorophyll Content with Sentinel-2: An Evaluation of Two Hybrid Retrieval Algorithms,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Merrick, T.[Trina], Pau, S.[Stephanie], Jorge, M.L.S.P.[Maria Luisa S.P.], Silva, T.S.F.[Thiago S. F.], Bennartz, R.[Ralf],
Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Hosoi, F.[Fumiki], Umeyama, S.[Sho], Kuo, K.[Kuangting],
Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Xie, M.M.[Meng-Meng], Wang, Z.Q.[Zhong-Qiang], Huete, A.[Alfredo], Brown, L.A.[Luke A.], Wang, H.[Heyu], Xie, Q.[Qiaoyun], Xu, X.[Xinpeng], Ding, Y.L.[Yan-Ling],
Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Lu, B.[Bing], He, Y.H.[Yu-Hong],
Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Liu, L.Z.[Lei-Zhen], Zhao, W.H.[Wen-Hui], Wu, J.J.[Jian-Jun], Liu, S.[Shasha], Teng, Y.[Yanguo], Yang, J.H.[Jian-Hua], Han, X.[Xinyi],
The Impacts of Growth and Environmental Parameters on Solar-Induced Chlorophyll Fluorescence at Seasonal and Diurnal Scales,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Zeng, C.Q.[Chui-Qing], Binding, C.[Caren],
The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Zhang, Q.[Qian], Zhang, X.K.[Xiao-Kang], Li, Z.H.[Zhao-Hui], Wu, Y.[Yunfei], Zhang, Y.[Yongguang],
Comparison of Bi-Hemispherical and Hemispherical-Conical Configurations for In Situ Measurements of Solar-Induced Chlorophyll Fluorescence,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Dong, T.F.[Tai-Feng], Shang, J.[Jiali], Chen, J.M.[Jing M.], Liu, J.[Jiangui], Qian, B.[Budong], Ma, B.[Baoluo], Morrison, M.J.[Malcolm J.], Zhang, C.[Chao], Liu, Y.P.[Yu-Peng], Shi, Y.[Yichao], Pan, H.[Hui], Zhou, G.[Guisheng],
Assessment of Portable Chlorophyll Meters for Measuring Crop Leaf Chlorophyll Concentration,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Miraglio, T.[Thomas], Adeline, K.[Karine], Huesca, M.[Margarita], Ustin, S.[Susan], Briottet, X.[Xavier],
Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef
And: Correction: RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Annala, L.[Leevi], Honkavaara, E.[Eija], Tuominen, S.[Sakari], Pölönen, I.[Ilkka],
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Kira, O.[Oz], Sun, Y.[Ying],
Extraction of sub-pixel C3/C4 emissions of solar-induced chlorophyll fluorescence (SIF) using artificial neural network,
PandRS(161), 2020, pp. 135-146.
Elsevier DOI 2002
solar-induced chlorophyll fluorescence (SIF), Sub-pixel SIF extraction, Artificial neural network (ANN) BibRef

Guo, M.[Meng], Li, J.[Jing], Huang, S.[Shubo], Wen, L.X.[Li-Xiang],
Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Gao, Y.[Yun], Wang, S.[Songhan], Guan, K.[Kaiyu], Wolanin, A.[Aleksandra], You, L.Z.[Liang-Zhi], Ju, W.M.[Wei-Min], Zhang, Y.G.[Yong-Guang],
The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Jin, J.[Jia], Pratama, B.A.[Bayu Arief], Wang, Q.[Quan],
Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Bendig, J., Malenovský, Z., Gautam, D., Lucieer, A.,
Solar-Induced Chlorophyll Fluorescence Measured From an Unmanned Aircraft System: Sensor Etaloning and Platform Motion Correction,
GeoRS(58), No. 5, May 2020, pp. 3437-3444.
IEEE DOI 2005
Airborne spectroscopy, solar-induced chlorophyll fluorescence (SIF), unmanned aerial vehicle (UAV) BibRef

Mohebzadeh, H.[Hamid], Yeom, J.[Junho], Lee, T.[Taesam],
Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Liu, L.Z.[Lei-Zhen], Zhao, W.H.[Wen-Hui], Shen, Q.[Qiu], Wu, J.J.[Jian-Jun], Teng, Y.G.[Yan-Guo], Yang, J.H.[Jian-Hua], Han, X.Y.[Xin-Yi], Tian, F.[Feng],
Nonlinear Relationship Between the Yield of Solar-Induced Chlorophyll Fluorescence and Photosynthetic Efficiency in Senescent Crops,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Vargas, J.Q.[Juan Quirós], Bendig, J.[Juliane], Arthur, A.M.[Alasdair Mac], Burkart, A.[Andreas], Julitta, T.[Tommaso], Maseyk, K.[Kadmiel], Thomas, R.[Rick], Siegmann, B.[Bastian], Rossini, M.[Micol], Celesti, M.[Marco], Schüttemeyer, D.[Dirk], Kraska, T.[Thorsten], Muller, O.[Onno], Rascher, U.[Uwe],
Unmanned Aerial Systems (UAS)-Based Methods for Solar Induced Chlorophyll Fluorescence (SIF) Retrieval with Non-Imaging Spectrometers: State of the Art,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ali, A.M.[Abebe Mohammed], Darvishzadeh, R.[Roshanak], Skidmore, A.[Andrew], Heurich, M.[Marco], Paganini, M.[Marc], Heiden, U.[Uta], Mücher, S.[Sander],
Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Syariz, M.A.[Muhammad Aldila], Lin, C.H.[Chao-Hung], Nguyen, M.V.[Manh Van], Jaelani, L.M.[Lalu Muhamad], Blanco, A.C.[Ariel C.],
WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ma, Y.[Yan], Liu, L.Y.[Liang-Yun], Chen, R.N.[Ruo-Nan], Du, S.S.[Shan-Shan], Liu, X.J.[Xin-Jie],
Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Qian, X.J.[Xiao-Jin], Liu, L.Y.[Liang-Yun],
Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Bhadra, S.[Sourav], Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi], Maimaitiyiming, M.[Matthew], Newcomb, M.[Maria], Shakoor, N.[Nadia], Mockler, T.C.[Todd C.],
Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Pastor-Guzman, J., Brown, L., Morris, H., Bourg, L., Goryl, P., Dransfeld, S., Dash, J.,
The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Sonobe, R.[Rei], Yamashita, H.[Hiroto], Mihara, H.[Harumi], Morita, A.[Akio], Ikka, T.[Takashi],
Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Hoeppner, J.M.[J. Malin], Skidmore, A.K.[Andrew K.], Darvishzadeh, R.[Roshanak], Heurich, M.[Marco], Chang, H.C.[Hsing-Chung], Gara, T.W.[Tawanda W.],
Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zou, T.Y.[Tian-Yuan], Zhang, J.[Jing],
A New Fluorescence Quantum Yield Efficiency Retrieval Method to Simulate Chlorophyll Fluorescence under Natural Conditions,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Pineda, M.[Mónica], Barón, M.[Matilde], Pérez-Bueno, M.L.[María-Luisa],
Thermal Imaging for Plant Stress Detection and Phenotyping,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Sun, Q.[Qi], Jiao, Q.J.[Quan-Jun], Qian, X.J.[Xiao-Jin], Liu, L.Y.[Liang-Yun], Liu, X.J.[Xin-Jie], Dai, H.Y.[Hua-Yang],
Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Feng, H.Z.[Huai-Ze], Xu, T.R.[Tong-Ren], Liu, L.Y.[Liang-Yun], Zhou, S.[Sha], Zhao, J.X.[Jing-Xue], Liu, S.M.[Shao-Min], Xu, Z.[Ziwei], Mao, K.[Kebiao], He, X.L.[Xin-Lei], Zhu, Z.L.[Zhong-Li], Chai, L.[Linna],
Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Park, J.E.[Ji-Eun], Park, K.A.[Kyung-Ae],
Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012-2017),
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Tong, C.[Chiming], Bao, Y.[Yunfei], Zhao, F.[Feng], Fan, C.[Chongrui], Li, Z.J.[Zhen-Jiang], Huang, Q.[Qiaolin],
Evaluation of the FluorWPS Model and Study of the Parameter Sensitivity for Simulating Solar-Induced Chlorophyll Fluorescence,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ogashawara, I.[Igor], Kiel, C.[Christine], Jechow, A.[Andreas], Kohnert, K.[Katrin], Ruhtz, T.[Thomas], Grossart, H.P.[Hans-Peter], Hölker, F.[Franz], Nejstgaard, J.C.[Jens C.], Berger, S.A.[Stella A.], Wollrab, S.[Sabine],
The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Grave, C.D.[Charlotte De], Pipia, L.[Luca], Siegmann, B.[Bastian], Morcillo-Pallarés, P.[Pablo], Rivera-Caicedo, J.P.[Juan Pablo], Moreno, J.[José], Verrelst, J.[Jochem],
Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Bandopadhyay, S.[Subhajit], Rastogi, A.[Anshu], Cogliati, S.[Sergio], Rascher, U.[Uwe], Gabka, M.[Maciej], Juszczak, R.[Radoslaw],
Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Li, J.[Jun], Li, T.[Tongji], Song, Q.J.[Qing-Jun], Ma, C.[Chaofei],
Performance Evaluation of Four Ocean Reflectance Model,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
The ocean reflectance model (ORM), which takes into account the chlorophyll a concentration data. BibRef

Chen, J.H.[Jing-Hua], Wang, S.Q.[Shao-Qiang], Chen, B.[Bin], Li, Y.[Yue], Amir, M.[Muhammad], Ma, L.[Li], Zhu, K.[Kai], Yang, F.[Fengting], Wang, X.B.[Xiao-Bo], Liu, Y.Y.[Yuan-Yuan], Wang, P.Y.[Peng-Yuan], Wang, J.[Junbang], Huang, M.[Mei], Wang, Z.[Zhaosheng],
Comparative Analysis on the Estimation of Diurnal Solar-Induced Chlorophyll Fluorescence Dynamics for a Subtropical Evergreen Coniferous Forest,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef


Chang, Y., Moan, S.L., Bailey, D.,
RGB Imaging Based Estimation of Leaf Chlorophyll Content,
IVCNZ19(1-6)
IEEE DOI 2004
cameras, image colour analysis, image sensors, neural nets, reflectivity, regression analysis, remote sensing, vegetation, RGB sensor BibRef

Nguyen, M.V., Chu, H.J., Lin, C.H., Lalu, M.J.,
Feature Selection of Optical Satellite Images for Chlorophyll-a Concentration Estimation,
ISSDQ19(1249-1253).
DOI Link 1912
BibRef

Irteza, S.M., Nichol, J.E.,
Measurement Of Sun Induced Chlorophyll Fluorescence Using Hyperspectral Satellite Imagery,
ISPRS16(B8: 911-913).
DOI Link 1610
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Chlorophyll Estimation in Water .


Last update:Sep 12, 2021 at 22:38:33