Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

[1]  C. Field,et al.  Canopy near-infrared reflectance and terrestrial photosynthesis , 2017, Science Advances.

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

[3]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Stefan Noel,et al.  Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: Feasibility study and first results , 2015 .

[5]  Michele Meroni,et al.  Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data , 2016, Remote. Sens..

[6]  Ismael Moya,et al.  Chlorophyll fluorescence emission spectrum inside a leaf , 2008, Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology.

[7]  Victor Kuperman,et al.  The Random Forests statistical technique: An examination of its value for the study of reading , 2016, Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading.

[8]  Jingfeng Xiao,et al.  A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data , 2019, Remote. Sens..

[9]  E. Middleton,et al.  Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT , 2012 .

[10]  Ekaterina Sukhova,et al.  Analysis of Light-Induced Changes in the Photochemical Reflectance Index (PRI) in Leaves of Pea, Wheat, and Pumpkin Using Pulses of Green-Yellow Measuring Light , 2019, Remote. Sens..

[11]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[12]  S. Vincenzi,et al.  Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy , 2011 .

[13]  L. Gu,et al.  Sun-induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions. , 2019, The New phytologist.

[14]  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.

[15]  C. Panigada,et al.  Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. , 2016, Plant, cell & environment.

[16]  C. Frankenberg,et al.  Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. , 2014, Journal of experimental botany.

[17]  P. Gentine,et al.  A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks , 2018, Biogeosciences.

[18]  W. Verhoef,et al.  Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence , 2019, Remote Sensing of Environment.

[19]  Gregory Duveiller,et al.  Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity , 2016 .

[20]  Peijun Du,et al.  Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features , 2015 .

[21]  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.

[22]  Onisimo Mutanga,et al.  Modeling the Potential Distribution of Pine Forests Susceptible to Sirex Noctilio Infestations in Mpumalanga, South Africa , 2010, Trans. GIS.

[23]  R. Colombo,et al.  Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis , 2015 .

[24]  C. Frankenberg,et al.  Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2 , 2013 .

[25]  A. Huete,et al.  Spatial pattern and seasonal dynamics of the photosynthesis activity across Australian rainfed croplands , 2020 .

[26]  A. Porcar-Castell,et al.  Dynamic response of plant chlorophyll fluorescence to light, water and nutrient availability. , 2015, Functional plant biology : FPB.

[27]  J. Moreno,et al.  Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies: A SCOPE modeling study , 2016 .

[28]  C. Frankenberg,et al.  Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2 , 2014 .

[29]  Liangxu Wang,et al.  The Heihe Integrated Observatory Network: A Basin‐Scale Land Surface Processes Observatory in China , 2018 .

[30]  D. Greenland.  The Climate of Niwot Ridge, Front Range, Colorado, U.S.A. , 1989, Arctic and Alpine Research.

[31]  Y. Ryu,et al.  A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence , 2019, Remote Sensing of Environment.

[32]  G. Fleming,et al.  Carotenoid to chlorophyll energy transfer in light harvesting complex II from Arabidopsis thaliana probed by femtosecond fluorescence upconversion , 2003 .

[33]  Yuan Gao,et al.  Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach , 2019, Remote. Sens..

[34]  Jan U.H. Eitel,et al.  Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat , 2016 .

[35]  Galit Shmueli,et al.  To Explain or To Predict? , 2010, 1101.0891.

[36]  G. Toci,et al.  A retrieval algorithm to evaluate the Photosystem I and Photosystem II spectral contributions to leaf chlorophyll fluorescence at physiological temperatures , 2011, Photosynthesis Research.

[37]  J. Gómez-Ramírez,et al.  Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods , 2019, Scientific Reports.

[38]  Liangyun Liu,et al.  Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level , 2020 .

[39]  Ismael Moya,et al.  Effect of canopy structure on sun-induced chlorophyll fluorescence , 2012 .

[40]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[41]  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.

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

[43]  Ying Sun,et al.  High‐Resolution Global Contiguous SIF of OCO‐2 , 2019, Geophysical Research Letters.

[44]  Xinjie Liu,et al.  Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations , 2020, Sensors.

[45]  J. Berry,et al.  Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops , 2020 .

[46]  L. Guanter,et al.  New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations andapplication to GOME-2 and SCIAMACHY , 2016 .

[47]  A. Isoda,et al.  Effects of Water Stress on Leaf Temperature and Chlorophyll Fluorescence Parameters in Cotton and Peanut , 2010 .

[48]  C. Frankenberg,et al.  Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP , 2018 .

[49]  Pedro M. Domingos.  A few useful things to know about machine learning , 2012, Commun. ACM.

[50]  W. Ju,et al.  Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures , 2019, Remote Sensing of Environment.

[51]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[52]  C. Tol,et al.  Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance. , 2018 .

[53]  Henk Eskes,et al.  TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications , 2012 .

[54]  J. Flexas,et al.  Steady-state chlorophyll fluorescence (Fs) measurements as a tool to follow variations of net CO2 assimilation and stomatal conductance during water-stress in C3 plants. , 2002, Physiologia plantarum.

[55]  Luis Alonso,et al.  Diurnal Cycle Relationships between Passive Fluorescence, PRI and NPQ of Vegetation in a Controlled Stress Experiment , 2017, Remote. Sens..

[56]  Shi Hu,et al.  Relationship between fluorescence yield and photochemical yield under water stress and intermediate light conditions , 2018, Journal of experimental botany.

[57]  Xiangming Xiao,et al.  Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize. , 2016, Ecological applications : a publication of the Ecological Society of America.

[58]  J. Joiner,et al.  Retrieval of sun-induced chlorophyll fluorescence from space , 2014 .

[59]  M. Hill,et al.  tri-PRI: A three band reflectance index tracking dynamic photoprotective mechanisms in a mature eucalypt forest , 2019, Agricultural and Forest Meteorology.

[60]  C. Tucker,et al.  The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: Insights from modeling and comparisons with parameters derived from satellite reflectances , 2015 .

[61]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[62]  P. Blanken,et al.  Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence , 2019, Proceedings of the National Academy of Sciences.

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

[64]  Jian Guo,et al.  Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity , 2018, Remote. Sens..

[65]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.

[66]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[67]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[68]  Zheng Niu,et al.  Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System , 2014, PloS one.

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

[70]  Xingying Zhang,et al.  Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. , 2018, Science bulletin.

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

[72]  L. Guanter,et al.  Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications , 2016 .

[73]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[74]  Dawn Emil Sebastian,et al.  Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses , 2016, Scientific Reports.

[75]  J. Landgraf,et al.  Global Retrievals of Solar‐Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO‐2 , 2018, Geophysical research letters.

[76]  W. Verhoef,et al.  Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics , 2018, Remote Sensing of Environment.

[77]  Marek Zivcak,et al.  Photosynthetic responses of sun- and shade-grown barley leaves to high light: is the lower PSII connectivity in shade leaves associated with protection against excess of light? , 2014, Photosynthesis Research.

[78]  M. Schaepman,et al.  Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: An assessment based on observational and modeling approaches , 2015 .

[79]  Luis Guanter,et al.  A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity , 2020 .

[80]  Mathias Disney,et al.  Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? , 2007 .

[81]  F. Gao,et al.  Detecting vegetation structure using a kernel-based BRDF model , 2003 .

[82]  Aniruddha Ghosh,et al.  A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales , 2014, Int. J. Appl. Earth Obs. Geoinformation.

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

[84]  B. He,et al.  Chlorophyll fluorescence observed by OCO-2 is strongly related to gross primary productivity estimated from flux towers in temperate forests , 2018 .

[85]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[86]  P. Gentine,et al.  Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence , 2018, Geophysical research letters.

[87]  Jian Guo,et al.  SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis , 2019, Sensors.