Contributions of imaging spectroscopy to improve estimates of evapotranspiration

Improved estimates of evapotranspiration (ET) are needed for water resource management and irrigation scheduling. We review the use of imaging spectroscopy to capture estimates of water vapour flux and biophysical components of ET. Remote sensing has long attempted to quantify and predict ET, with most applications relying only on green vegetation indexes from multispectral imagers combined with thermal radiance and weather data. In contrast, imaging spectrometry is an advanced remote sensing technology that measures hundreds of spectral bands in the solar spectrum. Plant pigments, water, and dry matter have unique spectral signatures that can advance estimates of ET and detection of drought stress. This allows analyses based on the physics of spectroscopy and avoids a requirement for continual empirical calibration. These spectral components provide unprecedented information about plant physiological processes, which improve understanding of the regulation of water fluxes and the energy budget. Laboratory, field, and airborne studies of spectral properties in the near- and shortwave-infrared region show strong relationships with plant water relations like water content, relative water content, and water potential. Because water absorption features are spectrally independent of pigment absorptions in the visible region, they provide a new source of information about environmental conditions. These new observations from imaging spectroscopy will lead to better understanding of ecological and hydrological processes. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  S. Dobrowski,et al.  Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects , 2003 .

[2]  Dudley A. Williams,et al.  Optical properties of water in the near infrared. , 1974 .

[3]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[4]  Robert O. Green,et al.  Atmospheric water vapor sensitivity and compensation requirement for Earth-looking imaging spectrometers in the solar-reflected spectrum , 2001 .

[5]  E. Rejmankova,et al.  Geostatistical scaling of canopy water content in a California salt marsh , 1998, Landscape Ecology.

[6]  T. Hsiao.  Plant Responses to Water Stress , 1973 .

[7]  M. Rietkerk,et al.  Ecohydrological advances and applications in plant-water relations research: a review , 2011 .

[8]  W. Calvin,et al.  SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping , 2003 .

[9]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

[10]  I. Noy-Meir,et al.  Desert Ecosystems: Environment and Producers , 1973 .

[11]  D. Roberts,et al.  The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral , 2003 .

[12]  Josep Peñuelas,et al.  Visible and near-infrared reflectance techniques for diagnosing plant physiological status , 1998 .

[13]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[14]  G. Asner.  Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[15]  S. Ustin,et al.  Predicting water content using Gaussian model on soil spectra , 2004 .

[16]  David W. Warren,et al.  LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing , 1996, Optics & Photonics.

[17]  Qing-Hua Huang,et al.  An optical coherence tomography (OCT)-based air jet indentation system for measuring the mechanical properties of soft tissues , 2009, Measurement science & technology.

[18]  T. Hsiao,et al.  Physiological Responses to Moderate Water Stress , 1982 .

[19]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[20]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[21]  Claudia M. Castaneda,et al.  Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .

[22]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[23]  A. Huete,et al.  Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing , 2010 .

[24]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[25]  John A. Gamon,et al.  Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index , 2006 .

[26]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

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

[28]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[29]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[30]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[31]  Julie C. Naumann,et al.  Linking Physiological Responses, Chlorophyll Fluorescence and Hyperspectral Imagery to Detect Salinity Stress Using the Physiological Reflectance Index in the Coastal Shrub, Myrica cerifera , 2008 .

[32]  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 .

[33]  Pablo J. Zarco-Tejada,et al.  Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Robert O. Green,et al.  Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS , 1997 .

[35]  S. Ustin,et al.  Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA , 2008 .

[36]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[37]  John A. Gamon,et al.  Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS , 2006 .

[38]  Susan L. Ustin,et al.  Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Ju , 2005 .

[39]  A. Rodger.  SODA: A new method of in-scene atmospheric water vapor estimation and post-flight spectral recalibration for hyperspectral sensors Application to the HyMap sensor at two locations , 2011 .

[40]  J. Conel,et al.  Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm - Sensitivity analysis and application to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data , 1993 .

[41]  Pablo J. Zarco-Tejada,et al.  Assessing structural effects on PRI for stress detection in conifer forests , 2011 .

[42]  Wallace M. Porter,et al.  The airborne visible/infrared imaging spectrometer (AVIRIS) , 1993 .

[43]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[44]  Martha C. Anderson,et al.  A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales , 2008 .

[45]  D. M. Gates,et al.  Spectral Properties of Plants , 1965 .

[46]  Pablo J. Zarco-Tejada,et al.  Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale , 2005 .

[47]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[48]  Martha C. Anderson,et al.  Thermal Remote Sensing of Drought and Evapotranspiration , 2008 .

[49]  Robert Frouin,et al.  Determination from Space of Atmospheric Total Water Vapor Amounts by Differential Absorption near 940 nm: Theory and Airborne Verification , 1990 .

[50]  T. Winkel,et al.  The Photochemical Reflectance Index (PRI) as a water-stress index , 2002 .

[51]  G. Carter.  PRIMARY AND SECONDARY EFFECTS OF WATER CONTENT ON THE SPECTRAL REFLECTANCE OF LEAVES , 1991 .

[52]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[53]  Ray D. Jackson,et al.  Estimation of Evapotranspiration at one Time-of-Day using Remotely Sensed Surface Temperatures , 1983 .

[54]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[55]  S. Schneider,et al.  Emissions pathways, climate change, and impacts on California. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[56]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[57]  Philip N. Slater,et al.  Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres , 1983 .

[58]  Jason F. Shogren,et al.  How probability weighting affects participation in water markets , 2006 .

[59]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[60]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[61]  Philip N. Slater,et al.  Mapping surface energy balance components by combining landsat thematic mapper and ground-based meteorological data , 1989 .

[62]  Raymond F. Kokaly,et al.  Investigating a Physical Basis for Spectroscopic Estimates of Leaf Nitrogen Concentration , 2001 .

[63]  J. R. Collins.  Change in the Infra-Red Absorption Spectrum of Water with Temperature , 1925 .

[64]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[65]  Pablo J. Zarco-Tejada,et al.  Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery , 2010 .

[66]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[67]  Thomas J. Jackson,et al.  Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery , 2008 .

[68]  N. J. Rosenberg,et al.  Thermal scanner measurement of canopy temperatures to estimate evapotranspiration , 1976 .

[69]  Thomas Hilker,et al.  Linking foliage spectral responses to canopy-level ecosystem photosynthetic light-use efficiency at a Douglas-fir forest in Canada , 2009 .

[70]  G. Heinson,et al.  Electrical evidence of continental accretion: Steeply‐dipping crustal‐scale conductivity contrast , 2006 .

[71]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2010 .

[72]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[73]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[74]  G. Asner,et al.  Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[75]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[76]  Shunlin Liang,et al.  Earth system science related imaging spectroscopy — an assessment , 2009 .

[77]  Christopher B. Field,et al.  Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies , 1990, Oecologia.

[78]  A. Goetz,et al.  Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data , 1990 .

[79]  Derek M. Cunnold,et al.  Observations of 1,1‐difluoroethane (HFC‐152a) at AGAGE and SOGE monitoring stations in 1994–2004 and derived global and regional emission estimates , 2007 .

[80]  Ping Yang,et al.  A new concept on remote sensing of cirrus optical depth and effective ice particle size using strong water vapor absorption channels near 1.38 and 1.88 /spl mu/m , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[81]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[82]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[83]  A. Goetz,et al.  Cirrus cloud detection from airborne imaging spectrometer data using the 1 , 1993 .

[84]  B. Rock,et al.  Measurement of leaf relative water content by infrared reflectance , 1987 .

[85]  J. Woolley.  Reflectance and transmittance of light by leaves. , 1971, Plant physiology.

[86]  Steven M. Driever,et al.  Photochemical reflectance index as a mean of monitoring early water stress , 2010 .

[87]  C. Tucker,et al.  Leaf optical system modeled as a stochastic process. , 1977, Applied optics.

[88]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[89]  B. Gao,et al.  Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data , 1995 .

[90]  C. Daughtry,et al.  Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes , 2003 .

[91]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[92]  Derek R. Peddle,et al.  Photosynthesis, chlorophyll fluorescence and spectral reflectance in Sphagnum moss at varying water contents , 2007, Oecologia.

[93]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[94]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[95]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[96]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[97]  John Shepanski,et al.  Hyperion, a space-based imaging spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[98]  Martha C. Anderson,et al.  Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States , 2011 .

[99]  Craig S. T. Daughtry,et al.  Discriminating Crop Residues from Soil by Shortwave Infrared Reflectance , 2001 .

[100]  David Riaño,et al.  Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona , 2008 .

[101]  F. M. Danson,et al.  Estimating live fuel moisture content from remotely sensed reflectance , 2004 .

[102]  P. Pinter,et al.  Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index , 2003, Irrigation Science.

[103]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[104]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[105]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[106]  A. Goetz,et al.  Software for the derivation of scaled surface reflectances from AVIRIS data , 1992 .

[107]  F. Meinzer.  Functional convergence in plant responses to the environment , 2002, Oecologia.

[108]  J. Curcio,et al.  Near infrared absorption spectrum of liquid water , 1951 .

[109]  Thomas H. Painter,et al.  Measuring the expressed abundance of the three phases of water with an imaging spectrometer over melting snow , 2006 .

[110]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[111]  J. C. Price,et al.  Estimation of Regional Scale Evapotranspiration Through Analysis of Satellite Thermal-infrared Data , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[112]  Piers J. Sellers,et al.  Remote sensing of the land biosphere and biogeochemistry in the EOS era: science priorities, methods and implementation—EOS land biosphere and biogeochemical cycles panels , 1993 .

[113]  A. Goetz,et al.  Terrestrial imaging spectroscopy , 1988 .

[114]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[115]  S. Ustin,et al.  Estimating Vegetation Water content with Hyperspectral data for different Canopy scenarios: Relationships between AVIRIS and MODIS Indexes , 2006 .

[116]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[117]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[118]  B. Lamb,et al.  Evaluating the relationship between AVIRIS water vapor and poplar plantation evapotranspiration , 2002 .

[119]  Elizabeth M. Middleton,et al.  Regional mapping of gross light-use efficiency using MODIS spectral indices , 2008 .

[120]  L. S. Galvão,et al.  Effects of Band Positioning and Bandwidth on NDVI Measurements of Tropical Savannas , 1999 .

[121]  John R. Miller,et al.  Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[122]  W. R. Windham,et al.  Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.

[123]  S. Brantley,et al.  Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies , 2011 .

[124]  Shuichi Rokugawa,et al.  A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images , 1998, IEEE Trans. Geosci. Remote. Sens..

[125]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[126]  Roger A. Pielke,et al.  Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate , 1998 .

[127]  Steve A. Chien,et al.  Onboard Science Processing Concepts for the HyspIRI Mission , 2009, IEEE Intelligent Systems.

[128]  Thomas Hilker,et al.  Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest , 2009 .

[129]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .