A novel hybrid machine learning phasor-based approach to retrieve a full set of solar-induced fluorescence metrics and biophysical parameters

[1]  R. Colombo,et al.  Towards consistent assessments of in situ radiometric measurements for the validation of fluorescence satellite missions , 2022, Remote Sensing of Environment.

[2]  J. Gamon,et al.  Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant , 2021, Remote sensing of environment.

[3]  D. Morton,et al.  Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies , 2021 .

[4]  C. Panigada,et al.  Mapping landscape canopy nitrogen content from space using PRISMA data , 2021, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[5]  Charlotte De Grave,et al.  Prototyping Vegetation Traits Models in the Context of the Hyperspectral Chime Mission Preparation , 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

[6]  C. Sheppard,et al.  Phasor approach of Mueller matrix optical scanning microscopy for biological tissue imaging , 2021, Biophysical journal.

[7]  Roberta E. Martin,et al.  NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms , 2021 .

[8]  Wolfram Mauser,et al.  Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops , 2021 .

[9]  Jacob A. Nelson,et al.  A model for estimating transpiration from remotely sensed solar-induced chlorophyll fluorescence , 2021 .

[10]  Xiaoliang Lu,et al.  Comparison of total emitted solar-induced chlorophyll fluorescence (SIF) and top-of-canopy (TOC) SIF in estimating photosynthesis , 2020 .

[11]  Charlotte De Grave,et al.  Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources. , 2020, Remote sensing of environment.

[12]  Hideki Kobayashi,et al.  FLiES-SIF version 1.0: three-dimensional radiative transfer model for estimating solar induced fluorescence , 2020 .

[13]  M. Lagorio,et al.  Re-absorption and scattering of chlorophyll fluorescence in canopies: A revised approach , 2020 .

[14]  C. Frankenberg,et al.  Systematic Assessment of Retrieval Methods for Canopy Far‐Red Solar‐Induced Chlorophyll Fluorescence Using High‐Frequency Automated Field Spectroscopy , 2020, Journal of Geophysical Research: Biogeosciences.

[15]  Matthew Maimaitiyiming,et al.  Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning , 2020, Remote. Sens..

[16]  Maitiniyazi Maimaitijiang,et al.  Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning , 2020, Remote. Sens..

[17]  Elizabeth M. Middleton,et al.  Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence , 2020 .

[18]  M. Rossini,et al.  Dynamics of sun-induced chlorophyll fluorescence and reflectance to detect stress-induced variations in canopy photosynthesis. , 2020, Plant, cell & environment.

[19]  Luis Alonso,et al.  The High-Performance Airborne Imaging Spectrometer HyPlant - From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain , 2019, Remote. Sens..

[20]  C. Tol,et al.  The scattering and re-absorption of red and near-infrared chlorophyll fluorescence in the models Fluspect and SCOPE , 2019, Remote Sensing of Environment.

[21]  W. Verhoef,et al.  Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. , 2019, Remote sensing of environment.

[22]  M. Rossini,et al.  Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem. , 2019, Remote sensing of environment.

[23]  L. Guanter,et al.  Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model , 2019, Remote Sensing of Environment.

[24]  Tommaso Julitta,et al.  A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance , 2019, Remote. Sens..

[25]  Giuseppe Chirico,et al.  Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging , 2019, Front. Oncol..

[26]  Luis Alonso,et al.  Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing , 2019, Remote. Sens..

[27]  Neus Sabater,et al.  Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level , 2019, Remote. Sens..

[28]  Tommaso Julitta,et al.  Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales , 2019, Remote. Sens..

[29]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2018, Neural Networks.

[30]  C. Frankenberg,et al.  The Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red fluorescence from aircraft , 2018, Remote Sensing of Environment.

[31]  C. Frankenberg,et al.  PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence , 2018, Remote Sensing of Environment.

[32]  Luis Alonso,et al.  Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun-Induced Chlorophyll Fluorescence , 2018, Remote. Sens..

[33]  M. Rossini,et al.  Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion , 2018, Remote Sensing of Environment.

[34]  Luis Alonso,et al.  Red and Far-Red Fluorescence Emission Retrieval from Airborne High-Resolution Spectra Collected by the Hyplant-Fluo Sensor , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[35]  M. Rossini,et al.  Variability of sun‐induced chlorophyll fluorescence according to stand age‐related processes in a managed loblolly pine forest , 2018, Global change biology.

[36]  Gustau Camps-Valls,et al.  Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods , 2018, Surveys in Geophysics.

[37]  L. Guanter,et al.  Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data , 2018, Remote Sensing of Environment.

[38]  W. Verhoef,et al.  Hyperspectral radiative transfer modeling to explore the combined retrieval of biophysical parameters and canopy fluorescence from FLEX – Sentinel-3 tandem mission multi-sensor data , 2018 .

[39]  L. Sironi,et al.  μMAPPS: a novel phasor approach to second harmonic analysis for in vitro-in vivo investigation of collagen microstructure , 2017, Scientific Reports.

[40]  Luis Alonso,et al.  Oxygen transmittance correction for solar-induced chlorophyll fluorescence measured on proximal sensing: Application to the NASA-GSFC fusion tower , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[41]  Xiaoliang Lu,et al.  Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest , 2017, Global change biology.

[42]  Liangyun Liu,et al.  Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence , 2017 .

[43]  Francesco Cutrale,et al.  Hyperspectral phasor analysis enables multiplexed 5D in vivo imaging , 2017, Nature Methods.

[44]  L. Guanter,et al.  Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests , 2016, Global change biology.

[45]  R. Colombo,et al.  Sun‐induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant , 2015, Global change biology.

[46]  Uwe Rascher,et al.  Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress , 2015 .

[47]  J. Moreno,et al.  Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? , 2015 .

[48]  M. Rossini,et al.  Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems , 2015 .

[49]  M. Rossini,et al.  Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest , 2015 .

[50]  W. Verhoef,et al.  Retrieval of sun-induced fluorescence using advanced spectral fitting methods , 2015 .

[51]  L. Guanter,et al.  A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data , 2014 .

[52]  J. Berry,et al.  Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence , 2014, Journal of geophysical research. Biogeosciences.

[53]  Liangyun Liu,et al.  A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements , 2014, Remote. Sens..

[54]  M. Schaepman,et al.  FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data , 2014 .

[55]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[56]  Hans C Gerritsen,et al.  Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images. , 2012, Optics express.

[57]  Philip Lewis,et al.  Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements , 2012 .

[58]  C. Frankenberg,et al.  New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity , 2011, Geophysical Research Letters.

[59]  E. Middleton,et al.  First observations of global and seasonal terrestrial chlorophyll fluorescence from space , 2010 .

[60]  Marina Mazzoni,et al.  High-resolution methods for fluorescence retrieval from space. , 2010, Optics express.

[61]  W. Verhoef,et al.  Performance of spectral fitting methods for vegetation fluorescence quantification , 2010 .

[62]  J. Moreno,et al.  Remote sensing of sun‐induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) , 2010 .

[63]  W. Verhoef,et al.  An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance , 2009 .

[64]  Luis Alonso,et al.  Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications , 2009 .

[65]  N. Baker Chlorophyll fluorescence: a probe of photosynthesis in vivo. , 2008, Annual review of plant biology.

[66]  E. Gratton,et al.  The phasor approach to fluorescence lifetime imaging analysis. , 2008, Biophysical journal.

[67]  Barry D Ganapol,et al.  LCM2: A coupled leaf/canopy radiative transfer model , 1999 .

[68]  G. Krause,et al.  Chlorophyll Fluorescence and Photosynthesis: The Basics , 1991 .

[69]  E. Gratton,et al.  The Measurement and Analysis of Heterogeneous Emissions by Multifrequency Phase and Modulation Fluorometry , 1984 .

[70]  G. Weber,et al.  Resolution of the fluorescence lifetimes in a heterogeneous system by phase and modulation measurements , 1981 .