Remote sensing of plant-water relations: An overview and future perspectives.

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration.

[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 .