23.2.6 Chlorophyll Estimation, Chlorophyll Concentration, Chlorophyll Fluorescence, Chlorophyll Index

Chapter Contents (Back)
Chlorophyll. Chlorophyll Fluorescence.
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

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

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.B.[Heng-Biao], Yao, X.[Xia], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing],
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.Y.[Kai-Yu],
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

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.Q.[Zhun-Qiao], Zhou, Y.Y.[Yu-Yu], Liu, Y.L.[Ya-Ling], An, S.Q.[Shu-Qing], 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.G.[Xu-Guang], Gu, Q.[Qing], Wang, M.[Min], Ma, M.G.[Ming-Guo], Han, X.J.[Xu-Jun],
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.Y.[Qiao-Yun], Xu, X.P.[Xin-Peng], 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.S.[Sha-Sha], Teng, Y.G.[Yan-Guo], Yang, J.H.[Jian-Hua], Han, X.Y.[Xin-Yi],
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.F.[Yun-Fei], Zhang, Y.G.[Yong-Guang],
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.L.[Jia-Li], Chen, J.M.[Jing M.], Liu, J.G.[Jian-Gui], Qian, B.[Budong], Ma, B.[Baoluo], Morrison, M.J.[Malcolm J.], Zhang, C.[Chao], Liu, Y.P.[Yu-Peng], Shi, Y.C.[Yi-Chao], Pan, H.[Hui], Zhou, G.S.[Gui-Sheng],
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
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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.H.[Song-Han], Guan, K.Y.[Kai-Yu], 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
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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
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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
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Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning,
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Pineda, M.[Mónica], Barón, M.[Matilde], Pérez-Bueno, M.L.[María-Luisa],
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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],
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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.W.[Zi-Wei], Mao, K.[Kebiao], He, X.L.[Xin-Lei], Zhu, Z.L.[Zhong-Li], Chai, L.[Linna],
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Tong, C.M.[Chi-Ming], Bao, Y.F.[Yun-Fei], Zhao, F.[Feng], Fan, C.R.[Chong-Rui], Li, Z.J.[Zhen-Jiang], Huang, Q.L.[Qiao-Lin],
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
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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
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de Grave, C.[Charlotte], Pipia, L.[Luca], Siegmann, B.[Bastian], Morcillo-Pallarés, P.[Pablo], Rivera-Caicedo, J.P.[Juan Pablo], Moreno, J.[José], Verrelst, J.[Jochem],
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RS(13), No. 8, 2021, pp. xx-yy.
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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
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Li, J.[Jun], Li, T.J.[Tong-Ji], Song, Q.J.[Qing-Jun], Ma, C.F.[Chao-Fei],
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.T.[Feng-Ting], Wang, X.B.[Xiao-Bo], Liu, Y.Y.[Yuan-Yuan], Wang, P.Y.[Peng-Yuan], Wang, J.[Junbang], Huang, M.[Mei], Wang, Z.S.[Zhao-Sheng],
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
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Lin, W.P.[Wen-Peng], Yu, X.[Xumiao], Xu, D.[Di], Sun, T.T.[Teng-Teng], Sun, Y.[Yue],
Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes,
RS(13), No. 18, 2021, pp. xx-yy.
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Lu, B.[Bing], He, Y.H.[Yu-Hong],
Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems,
RS(13), No. 22, 2021, pp. xx-yy.
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Ferrera-Cobos, F.[Francisco], Vindel, J.M.[Jose M.], Wane, O.[Ousmane], Navarro, A.A.[Ana A.], Zarzalejo, L.F.[Luis F.], Valenzuela, R.X.[Rita X.],
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RS(13), No. 23, 2021, pp. xx-yy.
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Photosynthetically Active Radiation. BibRef

Xu, X.J.[Xiao-Jun], Tang, Y.[Yan], Qu, Y.L.[Yi-Ling], Zhou, Z.S.[Zhong-Sheng], Hu, J.G.[Jun-Guo],
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RS(13), No. 24, 2021, pp. xx-yy.
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Ilteralp, M.[Melike], Ariman, S.[Sema], Aptoula, E.[Erchan],
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RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Jiao, Q.J.[Quan-Jun], Sun, Q.[Qi], Zhang, B.[Bing], Huang, W.J.[Wen-Jiang], Ye, H.C.[Hui-Chun], Zhang, Z.M.[Zhao-Ming], Zhang, X.[Xiao], Qian, B.X.[Bin-Xiang],
A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle,
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Ferreira, A.[Afonso], Brito, A.C.[Ana C.], Mendes, C.R.B.[Carlos R. B.], Brotas, V.[Vanda], Costa, R.R.[Raul R.], Guerreiro, C.V.[Catarina V.], Sá, C.[Carolina], Jackson, T.[Thomas],
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RS(14), No. 5, 2022, pp. xx-yy.
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Song, G.[Guangman], Wang, Q.[Quan],
Developing Hyperspectral Indices for Assessing Seasonal Variations in the Ratio of Chlorophyll to Carotenoid in Deciduous Forests,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Liu, G.H.[Gui-Hua], Wang, Y.S.[Yi-Song], Chen, Y.[Yanan], Tong, X.Q.[Xing-Qing], Wang, Y.D.[Yuan-Dong], Xie, J.[Jing], Tang, X.G.[Xu-Guang],
Remotely Monitoring Vegetation Productivity in Two Contrasting Subtropical Forest Ecosystems Using Solar-Induced Chlorophyll Fluorescence,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Guo, M.[Meng], Li, J.[Jing], Li, J.[Jianuo], Zhong, C.[Chao], Zhou, F.F.[Fen-Fen],
Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Hu, J.C.[Jiao-Chan], Jia, J.[Jia], Ma, Y.[Yan], Liu, L.Y.[Liang-Yun], Yu, H.Y.[Hao-Yang],
A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data,
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Tagliabue, G.[Giulia], Boschetti, M.[Mirco], Bramati, G.[Gabriele], Candiani, G.[Gabriele], Colombo, R.[Roberto], Nutini, F.[Francesco], Pompilio, L.[Loredana], Rivera-Caicedo, J.P.[Juan Pablo], Rossi, M.[Marta], Rossini, M.[Micol], Verrelst, J.[Jochem], Panigada, C.[Cinzia],
Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery,
PandRS(187), 2022, pp. 362-377.
Elsevier DOI 2205
Remote sensing, Earth Observation, Machine learning regression, Nitrogen content, Chlorophyll content, Water content BibRef

Nofrizal, A.Y.[Adenan Yandra], Sonobe, R.[Rei], Yamashita, H.[Hiroto], Seki, H.[Haruyuki], Mihara, H.[Harumi], Morita, A.[Akio], Ikka, T.[Takashi],
Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Han, S.[Shuai], Liu, Z.G.[Zhi-Gang], Chen, Z.[Zhuang], Jiang, H.[Hao], Xu, S.[Shan], Zhao, H.R.[Hua-Rong], Ren, S.[Sanxue],
Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
Photosynthetically active radiation. Solar-Induced Chlorophyll Fluorescence. BibRef

Li, M.[Meng], Chu, R.H.[Rong-Hao], Sha, X.Z.[Xiu-Zhu], Xie, P.F.[Peng-Fei], Ni, F.[Feng], Wang, C.[Chao], Jiang, Y.[Yuelin], Shen, S.[Shuanghe], Islam, A.R.M.T.[Abu Reza Md. Towfiqul],
Monitoring 2019 Drought and Assessing Its Effects on Vegetation Using Solar-Induced Chlorophyll Fluorescence and Vegetation Indexes in the Middle and Lower Reaches of Yangtze River, China,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
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de Cannière, S.[Simon], Vereecken, H.[Harry], Defourny, P.[Pierre], Jonard, F.[François],
Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
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Wang, C.[Cong], Wu, Y.J.[Yi-Jin], Hu, Q.[Qiong], Hu, J.[Jie], Chen, Y.P.[Yun-Ping], Lin, S.R.[Shang-Rong], Xie, Q.Y.[Qiao-Yun],
Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Verma, B.[Bhagyashree], Prasad, R.[Rajendra], Srivastava, P.K.[Prashant K.], Singh, P.[Prachi], Badola, A.[Anushree], Sharma, J.[Jyoti],
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Chen, J.[Jidai], Liu, X.J.[Xin-Jie], Ma, Y.[Yan], Liu, L.Y.[Liang-Yun],
Effects of Low Temperature on the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity across Different Plant Function Types,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Gao, S.[Sicong], Huete, A.[Alfredo], Kobayashi, H.[Hideki], Doody, T.M.[Tanya M.], Liu, W.W.[Wei-Wei], Wang, Y.[Yakai], Zhang, Y.G.[Yong-Guang], Lu, X.L.[Xiao-Liang],
Simulation of solar-induced chlorophyll fluorescence in a heterogeneous forest using 3-D radiative transfer modelling and airborne LiDAR,
PandRS(191), 2022, pp. 1-17.
Elsevier DOI 2208
Fluorescence escape ratio, Heterogeneous forest canopies, Monte Carlo model, SIF field measurements, Vegetation BibRef

Xu, M.Z.[Ming-Zhu], Liu, R.G.[Rong-Gao], Chen, J.M.[Jing M.], Shang, R.[Rong], Liu, Y.[Yang], Qi, L.[Lin], Croft, H.[Holly], Ju, W.M.[Wei-Min], Zhang, Y.G.[Yong-Guang], He, Y.H.[Yu-Hong], Qiu, F.[Feng], Li, J.[Jing], Lin, Q.[Qinan],
Retrieving global leaf chlorophyll content from MERIS data using a neural network method,
PandRS(192), 2022, pp. 66-82.
Elsevier DOI 2209
Global mapping, Machine learning, Leaf pigment, Leaf biochemical parameter, Phenology BibRef

Zhang, R.F.[Run-Fei], Yang, P.Q.[Pei-Qi], Liu, S.Y.[Shou-Yang], Wang, C.H.[Cai-Hong], Liu, J.[Jing],
Evaluation of the Methods for Estimating Leaf Chlorophyll Content with SPAD Chlorophyll Meters,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Rossini, M.[Micol], Celesti, M.[Marco], Bramati, G.[Gabriele], Migliavacca, M.[Mirco], Cogliati, S.[Sergio], Rascher, U.[Uwe], Colombo, R.[Roberto],
Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
SIF: Sun-Induced Fluorescence. BibRef

Ruszczak, B.[Bogdan], Wijata, A.M.[Agata M.], Nalepa, J.[Jakub],
Unbiasing the Estimation of Chlorophyll from Hyperspectral Images: A Benchmark Dataset, Validation Procedure and Baseline Results,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Impollonia, G.[Giorgio], Croci, M.[Michele], Blandinières, H.[Henri], Marcone, A.[Andrea], Amaducci, S.[Stefano],
Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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He, Y.T.[Yu-Ting], Wu, P.[Penghai], Ma, X.S.[Xiao-Shuang], Wang, J.[Jie], Wu, Y.[Yanlan],
Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Sasagawa, T.[Taiga], Akitsu, T.K.[Tomoko Kawaguchi], Ide, R.[Reiko], Takagi, K.[Kentaro], Takanashi, S.[Satoru], Nakaji, T.[Tatsuro], Nasahara, K.N.[Kenlo Nishida],
Accuracy Assessment of Photochemical Reflectance Index (PRI) and Chlorophyll Carotenoid Index (CCI) Derived from GCOM-C/SGLI with In Situ Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Zhao, W.H.[Wen-Hui], Wu, J.J.[Jian-Jun], Shen, Q.[Qiu], Yang, J.H.[Jian-Hua], Han, X.[Xinyi],
Exploring the Ability of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring Based on an Intelligent Irrigation Control System,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Ghaziri, A.E.[Angelina El], Bouhlel, N.[Nizar], Sapoukhina, N.[Natalia], Rousseau, D.[David],
On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Qian, X.J.[Xiao-Jin], Liu, L.Y.[Liang-Yun], Chen, X.[Xidong], Zhang, X.[Xiao], Chen, S.Y.[Si-Yuan], Sun, Q.[Qi],
Global Leaf Chlorophyll Content Dataset (GLCC) from 2003-2012 to 2018-2020 Derived from MERIS and OLCI Satellite Data: Algorithm and Validation,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Xu, S.[Shan], Liu, Z.G.[Zhi-Gang], Han, S.[Shuai], Chen, Z.[Zhuang], He, X.[Xue], Zhao, H.[Huarong], Ren, S.[Sanxue],
Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Pang, S.Y.[Shu-Yu], Zhu, L.P.[Li-Ping], Liu, C.[Chong], Ju, J.T.[Jian-Ting],
Causes and Impacts of Decreasing Chlorophyll-a in Tibet Plateau Lakes during 1986-2021 Based on Landsat Image Inversion,
RS(15), No. 6, 2023, pp. 1503.
DOI Link 2304
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Dang, X.Y.[Xiao-Yan], Du, J.[Jun], Wang, C.[Chao], Zhang, F.F.[Fang-Fang], Wu, L.[Lin], Liu, J.P.[Ji-Ping], Wang, Z.[Zheng], Yang, X.[Xu], Wang, J.X.[Jing-Xu],
A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification,
RS(15), No. 8, 2023, pp. 2209.
DOI Link 2305
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Zhu, K.[Kai], Chen, J.H.[Jing-Hua], Wang, S.Q.[Shao-Qiang], Fang, H.L.[Hong-Liang], Chen, B.[Bin], Zhang, L.[Leiming], Li, Y.[Yuelin], Zheng, C.[Chen], Amir, M.[Muhammad],
Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest,
PandRS(201), 2023, pp. 78-91.
Elsevier DOI 2307
Solar-induced chlorophyll fluorescence, Subtropical evergreen forest, Radiative transfer, Understory BibRef

Wang, Y.[Yanan], He, J.C.[Jing-Chi], Shao, T.[Ting], Tu, Y.J.[You-Jun], Gao, Y.X.[Yu-Xin], Li, J.L.[Jun-Li],
Exploring the Potential of Solar-Induced Chlorophyll Fluorescence Monitoring Drought-Induced Net Primary Productivity Dynamics in the Huang-Huai-Hai Plain Based on the SIF/NPP Ratio,
RS(15), No. 13, 2023, pp. 3276.
DOI Link 2307
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Hu, M.[Meijun], Cheng, X.[Xiangfen], Zhang, J.S.[Jin-Song], Huang, H.[Hui], Zhou, Y.[Yu], Wang, X.[Xin], Pan, Q.M.[Qing-Mei], Guan, C.F.[Chong-Fan],
Temporal Variation in Tower-Based Solar-Induced Chlorophyll Fluorescence and Its Environmental Response in a Chinese Cork Oak Plantation,
RS(15), No. 14, 2023, pp. 3568.
DOI Link 2307
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Jacobson, J.[Josh], Cressie, N.[Noel], Zammit-Mangion, A.[Andrew],
Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data,
RS(15), No. 16, 2023, pp. 4038.
DOI Link 2309
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Zhuang, J.[Jie], Wang, Q.[Quan], Song, G.[Guangman], Jin, J.[Jia],
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions,
RS(15), No. 19, 2023, pp. 4890.
DOI Link 2310
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Zhang, J.[Jingru], Gonsamo, A.[Alemu], Tong, X.J.[Xiao-Juan], Xiao, J.F.[Jing-Feng], Rogers, C.A.[Cheryl A.], Qin, S.H.[Shu-Hong], Liu, P.R.[Pei-Rong], Yu, P.Y.[Pei-Yang], Ma, P.[Pu],
Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices,
PandRS(203), 2023, pp. 183-198.
Elsevier DOI 2310
Phenology, Gross primary productivity, PhenoCam, Solar-induced chlorophyll fluorescence, TROPOMI, Vegetation indices BibRef

Ahmed, K.R.[Kazi Rifat], Paul-Limoges, E.[Eugenie], Rascher, U.[Uwe], Hanus, J.[Jan], Miglietta, F.[Franco], Colombo, R.[Roberto], Peressotti, A.[Alessandro], Genangeli, A.[Andrea], Damm, A.[Alexander],
Empirical insights on the use of sun-induced chlorophyll fluorescence to estimate short-term changes in crop transpiration under controlled water limitation,
PandRS(203), 2023, pp. 71-85.
Elsevier DOI 2310
Transpiration, Sun-induced chlorophyll fluorescence, Penman-Monteith, Ball-Berry-Leuning, Airborne optical data, Airborne thermal data BibRef

Li, D.[Dasui], Hu, Q.Q.[Qing-Qing], Ruan, S.Q.[Si-Qi], Liu, J.[Jun], Zhang, J.Z.[Jin-Zhi], Hu, C.G.[Chun-Gen], Liu, Y.Z.[Yong-Zhong], Dian, Y.Y.[Yuan-Yong], Zhou, J.J.[Jing-Jing],
Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves,
RS(15), No. 20, 2023, pp. 4934.
DOI Link 2310
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Falcioni, R.[Renan], Antunes, W.C.[Werner Camargos], de Oliveira, R.B.[Roney Berti], Chicati, M.L.[Marcelo Luiz], Demattê, J.A.M.[José Alexandre M.], Nanni, M.R.[Marcos Rafael],
Assessment of Combined Reflectance, Transmittance, and Absorbance Hyperspectral Sensors for Prediction of Chlorophyll a Fluorescence Parameters,
RS(15), No. 20, 2023, pp. 5067.
DOI Link 2310
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Wang, C.[Cong], Chen, Y.[Yunping], Tong, W.T.[Wan-Ting], Zhou, W.[Wei], Li, J.[Jing], Xu, B.D.[Bao-Dong], Hu, Q.[Qiong],
Mapping crop phenophases in reproductive growth period by satellite solar-induced chlorophyll fluorescence: A case study in mid-temperate zone in China,
PandRS(205), 2023, pp. 191-205.
Elsevier DOI 2311
Solar-induced chlorophyll fluorescence, Enhanced vegetation index, Maturity BibRef

Zhang, A.[Aiwu], Yin, S.N.[Sheng-Nan], Wang, J.[Juan], He, N.P.[Nian-Peng], Chai, S.[Shatuo], Pang, H.Y.[Hai-Yang],
Grassland Chlorophyll Content Estimation from Drone Hyperspectral Images Combined with Fractional-Order Derivative,
RS(15), No. 23, 2023, pp. 5623.
DOI Link 2312
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Hou, Y.Q.[Yu-Qing], Wu, Y.F.[Yun-Fei], Wu, L.S.[Lin-Sheng], Pei, L.[Lei], Zhang, Z.Y.[Zhao-Ying], Ding, D.W.[Da-Wei], Wang, G.S.[Guang-Shuai], Li, Z.Y.[Zhong-Yang], Zhang, Y.G.[Yong-Guang],
Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements,
RS(15), No. 24, 2023, pp. 5689.
DOI Link 2401
BibRef

Gao, S.[Sicong], Woodgate, W.[William], Ma, X.L.[Xuan-Long], Doody, T.M.[Tanya M.],
Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Chu, T.J.[Tian-Jia], Li, J.[Jing], Zhao, J.[Jing], Gu, C.P.[Chen-Peng], Mumtaz, F.[Faisal], Dong, Y.D.[Ya-Dong], Zhang, H.[Hu], Liu, Q.H.[Qin-Huo],
Regional Analysis of Dominant Factors Influencing Leaf Chlorophyll Content in Complex Terrain Regions Using a Geographic Statistical Model,
RS(16), No. 3, 2024, pp. 479.
DOI Link 2402
BibRef

Baranoski, G.V.G.[Gladimir V. G.], Varsa, P.M.[Petri M.],
Environmentally Induced Snow Transmittance Variations in the Photosynthetic Spectral Domain: Photobiological Implications for Subnivean Vegetation under Climate Warming Conditions,
RS(16), No. 5, 2024, pp. 927.
DOI Link 2403
BibRef


Wang, S.H.[Song-Han], Zhang, Y.G.[Yong-Guang], Ju, W.M.[Wei-Min], Wu, M.S.[Mou-Song], Liu, L.[Lei], He, W.[Wei], Peñuelas, J.[Josep],
Temporally corrected long-term satellite solar-induced fluorescence leads to improved estimation of global trends in vegetation photosynthesis during 1995-2018,
PandRS(194), 2022, pp. 222-234.
Elsevier DOI 2212
Solar-induced chlorophyll fluorescence, Temporal trend, Gross primary production Long-term global SIF data, Spatial downscaling approach 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:Mar 16, 2024 at 20:36:19