Remote sensing of plant-water relations: An overview and future perspectives.
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F Morsdorf | F D Schneider | M. Migliavacca | F. Morsdorf | C. Simmer | F. Schneider | U. Rascher | C. Tol | A. Damm | E. Paul-Limoges | E. Haghighi | C. van der Tol | M Migliavacca | U Rascher | A Damm | E Paul-Limoges | E Haghighi | C Simmer | C van der Tol | Alexander Damm | Erfan Haghighi | Fabian D. Schneider
[1] Felix Morsdorf,et al. Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling , 2009 .
[2] O. Sonnentag,et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .
[3] Jaime Hueso Gonzalez,et al. TanDEM-X: A satellite formation for high-resolution SAR interferometry , 2007 .
[4] Patrick E. Van Laake,et al. Estimation of absorbed PAR across Scandinavia from satellite measurements : Part I: Incident PAR , 2007 .
[5] Thomas Udelhoven,et al. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance , 2016, Int. J. Appl. Earth Obs. Geoinformation.
[6] W. Oechel,et al. FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .
[7] Dara Entekhabi,et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. , 2017, Nature geoscience.
[8] Yann Kerr,et al. The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.
[9] Manabu Watanabe,et al. ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[10] D. Baldocchi. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future , 2003 .
[11] Moon S. Kim,et al. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .
[12] Prashant K. Srivastava,et al. Satellite Soil Moisture: Review of Theory and Applications in Water Resources , 2017, Water Resources Management.
[13] M. Rossini,et al. Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity , 2016 .
[14] M. Farooq,et al. Plant drought stress: effects, mechanisms and management , 2011, Agronomy for Sustainable Development.
[15] Jarrett E. K. Byrnes,et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change , 2012, Nature.
[16] K. Itten,et al. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .
[17] Susan L Ustin,et al. Remote sensing of plant functional types. , 2010, The New phytologist.
[18] L. Isaksen,et al. THE ATMOSPHERIC DYNAMICS MISSION FOR GLOBAL WIND FIELD MEASUREMENT , 2005 .
[19] Pablo J. Zarco-Tejada,et al. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .
[20] Clayton T. Morrison,et al. Factoring in canopy cover heterogeneity on evapotranspiration partitioning: Beyond big-leaf surface homogeneity assumptions , 2014, Journal of Soil and Water Conservation.
[21] Jiancheng Shi,et al. The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.
[22] Pierre Gentine,et al. Global variations in ecosystem‐scale isohydricity , 2017, Global change biology.
[23] J. Randerson,et al. Global net primary production: Combining ecology and remote sensing , 1995 .
[24] M. Rietkerk,et al. Ecohydrological advances and applications in plant-water relations research: a review , 2011 .
[25] M. Schildhauer,et al. Monitoring plant functional diversity from space , 2016, Nature Plants.
[26] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[27] George Vosselman,et al. Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.
[28] Michael J. Aspinwall,et al. Stomatal and non-stomatal limitations of photosynthesis for four tree species under drought: A comparison of model formulations , 2017 .
[29] R. B. Jackson,et al. Hydraulic limits on maximum plant transpiration and the emergence of the safety-efficiency trade-off. , 2013, The New phytologist.
[30] Weiwei Zhu,et al. An improved satellite‐based approach for estimating vapor pressure deficit from MODIS data , 2014 .
[31] S. J. Birks,et al. Terrestrial water fluxes dominated by transpiration , 2013, Nature.
[32] Keith A. Mott,et al. Modelling stomatal conductance in response to environmental factors. , 2013, Plant, cell & environment.
[33] Geng-Ming Jiang,et al. Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI , 2006 .
[34] F. Rocca,et al. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle , 2011 .
[35] E. Næsset,et al. Forestry Applications of Airborne Laser Scanning , 2014, Managing Forest Ecosystems.
[36] O. Reitebuch,et al. The Airborne Demonstrator for the Direct-Detection Doppler Wind Lidar ALADIN on ADM-Aeolus. Part I: Instrument Design and Comparison to Satellite Instrument , 2009 .
[37] K. Itten,et al. Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization , 2007 .
[38] Michael E. Schaepman,et al. Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data , 2015, Remote. Sens..
[39] V. L. Mulder,et al. The use of remote sensing in soil and terrain mapping — A review , 2011 .
[40] Jessica A. Faust,et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .
[41] Felix Morsdorf,et al. Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images , 2014 .
[42] F. Chapin,et al. EFFECTS OF BIODIVERSITY ON ECOSYSTEM FUNCTIONING: A CONSENSUS OF CURRENT KNOWLEDGE , 2005 .
[43] K. Itten,et al. Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) , 2015 .
[44] Michele Meroni,et al. Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies , 2011, Sensors.
[45] D. Beerling,et al. Evolution of leaf-form in land plants linked to atmospheric CO2 decline in the Late Palaeozoic era , 2001, Nature.
[46] W. Verhoef,et al. Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .
[47] S. Running,et al. Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data , 2008 .
[48] Uwe Stilla,et al. Single tree identification using airborne multibaseline SAR interferometry data. , 2016 .
[49] Erik Næsset,et al. Determination of Mean Tree Height of Forest Stands by Digital Photogrammetry , 2002 .
[50] Alan H. Strahler,et al. A conceptual model for effective directional emissivity from nonisothermal surfaces , 1999, IEEE Trans. Geosci. Remote. Sens..
[51] H. Jones,et al. Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. , 2004, Journal of experimental botany.
[52] Christopher B. Field,et al. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies , 1990, Oecologia.
[53] Rebecca N. Handcock,et al. Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies , 2011, Sensors.
[54] 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 .
[55] G. Rondeaux,et al. Optimization of soil-adjusted vegetation indices , 1996 .
[56] R. Colombo,et al. Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis , 2015 .
[57] J. Moreno,et al. Remote sensing of sun‐induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) , 2010 .
[58] Pierre Gentine,et al. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation , 2017 .
[59] 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 .
[60] L. Hoffmann,et al. Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy , 2010 .
[61] Julie K. Lundquist,et al. Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics , 2015 .
[62] K. Itten,et al. LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management , 2004 .
[63] Stefan Heise,et al. Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures , 2008 .
[64] Stanislaus J. Schymanski,et al. Leaf-scale experiments reveal an important omission in the Penman-Monteith equation , 2017 .
[65] Roselyne Lacaze,et al. Retrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument , 2002 .
[66] F. Villalobos,et al. A soil-plant-atmosphere continuum (SPAC) model for simulating tree transpiration with a soil multi-compartment solution , 2017, Plant and Soil.
[67] W. Verhoef,et al. Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data , 2015 .
[68] J. M. Krijger,et al. Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence , 2014 .
[69] I. E. Woodrow,et al. A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .
[70] A. Gitelson,et al. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .
[71] Jeff Dozier,et al. A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..
[72] I. Prentice,et al. Biophysical homoeostasis of leaf temperature: A neglected process for vegetation and land-surface modelling , 2017 .
[73] Patrick Hostert,et al. The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..
[74] P. North,et al. Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .
[75] 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.
[76] C. Rodgers,et al. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation , 1976 .
[77] José A. Sobrino,et al. A Split-Window Algorithm for Estimating LST From Meteosat 9 Data: Test and Comparison With In Situ Data and MODIS LSTs , 2009, IEEE Geoscience and Remote Sensing Letters.
[78] Raymond F. Kokaly,et al. Characterizing regional soil mineral composition using spectroscopy and geostatistics , 2013 .
[79] C. Frankenberg,et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. , 2014, Journal of experimental botany.
[80] Amilcare Porporato,et al. Biological constraints on water transport in the soil–plant–atmosphere system , 2013 .
[81] P. Zarco-Tejada,et al. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .
[82] F. Baret,et al. Relating soil surface moisture to reflectance , 2002 .
[83] José A. Sobrino,et al. Satellite-derived land surface temperature: Current status and perspectives , 2013 .
[84] R. Waring,et al. Generalizing plant-water relations to landscapes , 2011 .
[85] M. Rossini,et al. Remote Sensing of Sun-induced Fluorescence to Measure the Functional Regulation of Photosynthesis , 2014 .
[86] J. Passioura. Plant–Water Relations , 2010 .
[87] G. Collatz,et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .
[88] C. Simmer,et al. Remote Sensing of Angular Characteristics of Canopy Reflectances , 1985, IEEE Transactions on Geoscience and Remote Sensing.
[89] J. Moreno,et al. Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? , 2015 .
[90] S. Paloscia,et al. Microwave Emission and Plant Water Content: A Comparison between Field Measurements and Theory , 1986, IEEE Transactions on Geoscience and Remote Sensing.
[91] Ray Leuning,et al. A coupled model of stomatal conductance, photosynthesis and transpiration , 2003 .
[92] J. Hatfield,et al. Encyclopedia of Soils in The Environment , 2004 .
[93] J. Berry,et al. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.
[94] R. Jenssen,et al. 1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .
[95] Park S. Nobel,et al. Physicochemical and Environmental Plant Physiology , 1991 .
[96] M. S. Moran,et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.
[97] J. Flexas,et al. UAVs challenge to assess water stress for sustainable agriculture , 2015 .
[98] Michael A. Forster,et al. A vegetation‐focused soil‐plant‐atmospheric continuum model to study hydrodynamic soil‐plant water relations , 2017 .
[99] H. Zwally,et al. Overview of ICESat's Laser Measurements of Polar Ice, Atmosphere, Ocean, and Land , 2002 .
[100] M. Govender,et al. Review of commonly used remote sensing and ground-based technologies to measure plant water stress , 2009 .
[101] Susanne Lehner,et al. Simultaneous Measurements by Advanced SAR and Radar Altimeter on Potential Improvement of Ocean Wave Model Assimilation , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[102] D. Or,et al. Wind increases leaf water use efficiency. , 2016, Plant, cell & environment.
[103] Clemens Simmer,et al. Effects of the Near-Surface Soil Moisture Profile on the Assimilation of L-band Microwave Brightness Temperature , 2006 .
[104] Hongliang Fang,et al. Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data , 2006 .
[105] Pablo J. Zarco-Tejada,et al. Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery , 2009 .
[106] J. Oertli. The Soil-Plant-Atmosphere Continuum , 1976 .
[107] Joe Landsberg,et al. Water relations in tree physiology: where to from here? , 2016, Tree physiology.
[108] J. Peñuelas,et al. Changes in leaf osmotic and elastic properties and canopy structure of strawberries under mild water stress , 1993 .
[109] Hervé Cochard,et al. An overview of models of stomatal conductance at the leaf level. , 2010, Plant, cell & environment.
[110] M. Schaepman,et al. FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data , 2014 .
[111] W. Verhoef,et al. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance , 2009 .
[112] A. Verhoef,et al. Towards an improved and more flexible representation of water stress in coupled photosynthesis-stomatal conductance models. , 2011 .
[113] Josep Peñuelas,et al. Cell wall elasticity and Water Index (R970 nm/R900 nm) in wheat under different nitrogen availabilities , 1996 .
[114] B. Hapke,et al. The cause of the hot spot in vegetation canopies and soils: Shadow-hiding versus coherent backscatter , 1996 .
[115] Nate G. McDowell,et al. Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth , 2017 .
[116] S. Kollet,et al. Evaluating the Influence of Plant-Specific Physiological Parameterizations on the Partitioning of Land Surface Energy Fluxes , 2015 .
[117] I. Sandholt,et al. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .
[118] José F. Moreno,et al. Replacing radiative transfer models by surrogate approximations through machine learning , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[119] S. Dekker,et al. Global CO2 rise leads to reduced maximum stomatal conductance in Florida vegetation , 2011, Proceedings of the National Academy of Sciences.
[120] S. Seneviratne,et al. Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .
[121] Philippe Lagueux,et al. A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications , 2012, Remote. Sens..
[122] W. Cohen,et al. Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .
[123] R. Jeu,et al. Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .
[124] A. Huete,et al. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .
[125] G. Asner,et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation , 2017, Science.
[126] James H. Matis,et al. Overview of Models , 2000 .
[127] Lawrence A. Corp,et al. Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers , 2016, Remote. Sens..
[128] L. Guanter,et al. Assessing the potential of sun-induced fluorescence and the canopy scattering coefficient to track large-scale vegetation dynamics in Amazon forests , 2016 .
[129] H. Walz. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges , 2014 .
[130] M. Schaepman,et al. How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response , 2017 .
[131] M. Schaepman,et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits , 2017, Nature Communications.
[132] J. Hill,et al. Using Imaging Spectroscopy to study soil properties , 2009 .
[133] Nick van de Giesen,et al. Dielectric Response of Corn Leaves to Water Stress , 2017, IEEE Geoscience and Remote Sensing Letters.
[134] Thomas Hilker,et al. Tracking plant physiological properties from multi-angular tower-based remote sensing , 2011, Oecologia.
[135] Wolfgang Wagner,et al. Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: Basic physical concepts , 2010 .
[136] Gerhard Krieger,et al. TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[137] Y. Cohen,et al. Estimation of leaf water potential by thermal imagery and spatial analysis. , 2005, Journal of experimental botany.
[138] Michael E. Schaepman,et al. Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm , 2017 .
[139] D. Lobell,et al. Moisture effects on soil reflectance , 2002 .
[140] I Leinonen,et al. Estimating stomatal conductance with thermal imagery. , 2006, Plant, cell & environment.
[141] S. Wofsy,et al. Modelling the soil-plant-atmosphere continuum in a Quercus-Acer stand at Harvard Forest : the regulation of stomatal conductance by light, nitrogen and soil/plant hydraulic properties , 1996 .
[142] John A. Gamon,et al. Facultative and constitutive pigment effects on the Photochemical Reflectance Index (PRI) in sun and shade conifer needles , 2012 .
[143] C. Giardino,et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling , 2008 .
[144] Joe T. Ritchie,et al. Soil water balance and plant water stress , 1998 .
[145] E. N. Stavros,et al. ISS observations offer insights into plant function , 2017, Nature Ecology &Evolution.
[146] Markus Reichstein,et al. The significance of land-atmosphere interactions in the Earth system — iLEAPS achievements and perspectives , 2015 .
[147] N. Mahowald,et al. Global review and synthesis of trends in observed terrestrial near-surface wind speeds; implications for evaporation , 2012 .
[148] C. Foyer. Interactions between Electron Transport and Carbon Assimilation in Leaves: Coordination of Activities and Control , 1993 .
[149] Susanne Crewell,et al. Towards a high‐resolution regional reanalysis for the European CORDEX domain , 2015 .
[150] Pablo J. Zarco-Tejada,et al. Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .
[151] David Riaño,et al. Contributions of imaging spectroscopy to improve estimates of evapotranspiration , 2011 .
[152] J. Peñuelas,et al. The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .
[153] R. Colombo,et al. Sun‐induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant , 2015, Global change biology.
[154] Thomas Hilker,et al. An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance , 2009 .
[155] W. Verhoef,et al. A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data , 2013 .
[156] Hans Lambers,et al. Plant Physiological Ecology , 1998, Springer New York.
[157] J. Kirchner,et al. Near‐surface turbulence as a missing link in modeling evapotranspiration‐soil moisture relationships , 2017 .
[158] Maurizio Mencuccini,et al. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. , 2017, Plant, cell & environment.
[159] S. Seneviratne,et al. Climate extremes and the carbon cycle , 2013, Nature.
[160] B. Choudhury. Estimation of vapor pressure deficit over land surfaces from satellite observations , 1998 .
[161] W. L. Smith,et al. Note on the Relationship Between Total Precipitable Water and Surface Dew Point , 1966 .
[162] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[163] S. Jacquemoud. Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study , 1993 .
[164] Gaylon S. Campbell,et al. Soil physics with BASIC :transport models for soil-plant systems , 1985 .
[165] Wout Verhoef,et al. The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8 , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[166] F Morsdorf,et al. Close-range laser scanning in forests: towards physically based semantics across scales , 2018, Interface Focus.
[167] Henry H. Dixon. ON THE ASCENT OF SAP , 1894 .
[168] Zhao-Liang Li,et al. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data , 2017 .
[169] Erich Meier,et al. 3-D Time-Domain SAR Imaging of a Forest Using Airborne Multibaseline Data at L- and P-Bands , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[170] C. Frankenberg,et al. Remote sensing of near-infrared chlorophyll fluorescence from space in scattering atmospheres: implications for its retrieval and interferences with atmospheric CO 2 retrievals , 2012 .
[171] Thomas H. Painter,et al. Measuring the expressed abundance of the three phases of water with an imaging spectrometer over melting snow , 2006 .
[173] S. Ustin,et al. Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .
[174] Shunlin Liang,et al. A Method for Consistent Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[175] Yoshio Inoue,et al. Remote estimation of leaf transpiration rate and stomatal resistance based on infrared thermometry , 1990 .
[176] Shaun Quegan,et al. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems , 2017 .
[177] R. Davies,et al. Feasibility and Error Analysis of Cloud Motion Wind Extraction from Near-Simultaneous Multiangle MISR Measurements , 2001 .