Skip to main content
SpringerLink
Log in
Book cover

Satellite-based Applications on Climate Change pp 335–357Cite as

Remote Sensing of Leaf, Canopy, and Vegetation Water Contents for Satellite Environmental Data Records

  • Chapter
  • First Online:

Abstract

The absorption features of liquid water in plant leaves are readily detectable, and the amount of leaf water content may be determined by spectroscopy. Spectral reflectances at about 1240 and 1650 nm are the basis of numerous remote-sensing indices that could be used to estimate liquid water content of leaves and canopies. Two applications of remotely sensed water content are estimation of fuel moisture content for wildfire potential and estimation of vegetation water content for improving retrievals of soil moisture content from microwave sensors. The temporal record of MODIS, SPOT Vegetation, and AVHRR/3 sensors and the future record from VIIRS will create a global environmental data record of canopy water content for climate change studies.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Abrams MD, Kubiske ME (1990) Leaf structural characteristics of 31 hardwood and conifer tree species in central Wisconsin: influence of light regime and shade-tolerance rank. Forest Ecol Manag 31:245–253

    Article  Google Scholar 

  • Aldakheel YY, Danson FM (1997) Spectral reflectances of dehydrating leaves: measurements and modelling. Int J Remote Sens 18:3683–3690

    Article  Google Scholar 

  • Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321:1481–1484

    Article  Google Scholar 

  • Allen WA, Gausman HW, Richardson AJ (1969) Interaction of isotropic light with a compact plant leaf. J Opt Soc Am 59:1376–1379

    Article  Google Scholar 

  • Anderson MC, Kustas WP, Norman JM (2003) Upscaling and downscaling – a regional view of the soil-plant-air continuum. Agron J 95:1408–1423

    Article  Google Scholar 

  • Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens Environ 112:3958–3970

    Article  Google Scholar 

  • Asner GP, Nepstad D, Cardinot G, Ray D (2004) Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proc Natl Acad Sci 101:6039–6044

    Article  Google Scholar 

  • Bowman WD (1989) The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sens Environ 30:249–255

    Article  Google Scholar 

  • Brown JF, Wardlow BD, Tadesse T, Hayes MJ, Reed BC (2008) The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci Remote Sens 45:16–46

    Article  Google Scholar 

  • Burgan RE (1988) 1988 Revisions to the 1978 national fire danger rating system. Res Pap SE-273. USDA Forest Service, Southeastern Forest Experiment Station, Asheville

    Google Scholar 

  • Burgan RE, Hartford RA (1993) Monitoring vegetation greenness with satellite data. Gen Tech Rep INT-297. USDA Forest Service, Intermountain Research Station, Ogden

    Google Scholar 

  • Carter GA (1991) Primary and secondary effects of water content on the spectral reflectance of leaves. Am J Bot 78:916–924

    Article  Google Scholar 

  • Carter GA (1993) Responses of leaf spectral reflectances to plant stress. Am J Bot 80:239–243

    Article  Google Scholar 

  • Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002a) Designing a spectral index to estimate vegetation water content from remote sensing data: part 1 theoretical approach. Remote Sens Environ 82:188–197

    Article  Google Scholar 

  • Ceccato P, Flasse S, Grégoire JM (2002b) Designing a spectral index to estimate vegetation water content from remote sensing data: part 2 validation and applications. Remote Sens Environ 82:198–207

    Article  Google Scholar 

  • Champagne CM, Staenz K, Bannari A, McNairn H, Deguise JC (2003) Validation of a hyperspectral curve-fitting model for estimation of plant water content of agricultural canopies. Remote Sens Environ 87:148–160

    Article  Google Scholar 

  • Cheng T, Riaño D, Koltunov A, Whiting ML, Ustin SL (2011a) Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data. In: Gao W et al (eds) Remote sensing and modeling of ecosystems for sustainability VIII, Proc SPIE, vol 8156. SPIE, Bellingham

    Google Scholar 

  • Cheng T, Rivard B, Sánchez-Azofeifa A (2011b) Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ 115:659–670

    Article  Google Scholar 

  • Cheng YB, Ustin SL, Riaño D, Vanderbilt VC (2008) Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona. Remote Sens Environ 112:363–374

    Article  Google Scholar 

  • Cheng YB, Zarco-Tejada PJ, Riaño D, Rueda CA, Ustin SL (2006) Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes. Remote Sens Environ 105:354–366

    Article  Google Scholar 

  • Christensen JH, Hewitson B, Busuioc WA, Chen A, Gao X et al (2007) Regional climate projections. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Contribution of working group 1 to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  • Chuvieco E, Aguado I, Cocero D, Riaño D (2003) Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. Int J Remote Sens 24:1621–1637

    Google Scholar 

  • Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez J, Martín S, Ibarra P, Herrera MA, Zamora R (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221:46–58

    Article  Google Scholar 

  • Chuvieco E, Riaño D, Aguado I, Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: applications in fire danger assessment. Int J Remote Sens 23:2145–2162

    Article  Google Scholar 

  • Cohen WB (1991a) Temporal versus spatial variation in leaf reflectance under changing water stress conditions. Int J Remote Sens 12:1865–1876

    Article  Google Scholar 

  • Cohen WB (1991b) Chaparral vegetation reflectance and its potential utility for assessment of fire hazard. Photogramm Eng Remote Sens 57:203–207

    Google Scholar 

  • Collier P (1989) Radiometric monitoring of moisture stress in irrigated cotton. Int J Remote Sens 10:1445–1450

    Article  Google Scholar 

  • Curcio JA, Petty CC (1951) The near infrared absorption spectrum of liquid water. J Opt Soc Am 41:302–304

    Article  Google Scholar 

  • Dasgupta S, Qu JJ (2009) Soil adjusted water content retrievals in grasslands. Int J Remote Sens 30:1019–1043

    Article  Google Scholar 

  • Dasgupta S, Qu JJ, Hao X, Bhoi S (2007) Evaluating remotely sensed live fuel moisture estimation for fire behavior predictions in Georgia, USA. Remote Sens Environ 108:138–150

    Article  Google Scholar 

  • Daughtry CST, Hunt ER (2008) Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover. Remote Sens Environ 112:1647–1657

    Article  Google Scholar 

  • Davidson A, Wang S, Wilmhurst J (2006) Remote sensing of grassland-shrubland vegetation water content in the shortwave domain. Int J Appl Earth Observ GeoInform 8:225–236

    Article  Google Scholar 

  • Delbart N, Kergoat L, Le Toan T, Lhermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38

    Article  Google Scholar 

  • Delbart N, Le Toan T, Kergoat L, Fedotova V (2006) Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004). Remote Sens Environ 101:53–62

    Article  Google Scholar 

  • Dennison PE, Moritz MA, Taylor RS (2008) Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California. Int J Wildland Fire 17:18–27

    Article  Google Scholar 

  • Dennison PE, Roberts DA, Peterson SH, Rechel J (2005) Use of normalized difference water index for monitoring live fuel moisture. Int J Remote Sens 26:1035–1042

    Article  Google Scholar 

  • Downing HG, Carter GA, Holladay KW, Cibula WG (1993) The radiative-equivalent water thickness of leaves. Remote Sens Environ 46:103–107

    Article  Google Scholar 

  • Eitel JUH, Gessler PE, Smith AMS, Robberecht R (2006) Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecol Manag 229:170–182

    Article  Google Scholar 

  • Elsayed S, Mistele B, Schmidhalter U (2011) Can changes in leaf water potential be assessed spectrally? Funct Plant Biol 38:523–533

    Google Scholar 

  • Entekhabi D, Njoku EG, O’Neill PE, Kellogg KH, Crow WT et al (2010) The soil moisture active passive mission. Proc IEEE 98:704–716

    Article  Google Scholar 

  • Fang HL, Liang SL (2003) Retrieving leaf area index with a neural network method: simulation and validation. IEEE Trans Geosci Remote Sens 41:2052–2062

    Article  Google Scholar 

  • Fensholt R, Sandholt I (2003) Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sens Environ 87:111–121

    Article  Google Scholar 

  • Féret JB, François C, Asner GP, Gitelson AA, Martin RE, Bidel LPR, Ustin SL, le Maire G, Jacquemoud S (2008) PROSPECT-4 and -5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens Environ 112:3030–3042

    Article  Google Scholar 

  • Féret JB, François C, Gitelson A, Asner GP, Barry KM, Panigada C, Richardson AD, Jacquemoud S (2011) Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ 115:2742–2750

    Article  Google Scholar 

  • Fourty T, Baret F (1997) Vegetation water and dry matter contents estimated from top-of-the atmosphere reflectance data: a simulation study. Remote Sens Environ 61:34–45

    Article  Google Scholar 

  • Gao BC (1996) NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Article  Google Scholar 

  • Gao BC, Goetz AFH (1990) Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res 95:3549–3564

    Article  Google Scholar 

  • Gao BC, Goetz AFH (1994) Extraction of dry leaf spectral features from reflectance spectra of green vegetation. Remote Sens Environ 47:369–374

    Article  Google Scholar 

  • Gao BC, Goetz AFH (1995) Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ 52:155–162

    Article  Google Scholar 

  • Gao BC, Montes MJ, Davis CO, Goetz AFH (2009) Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113:S17–S24

    Article  Google Scholar 

  • Garnier E, Laurent G, Bellmann A, Debain S, Berthelier P, Ducout B, Roumet C, Navas ML (2001) Consistency of species ranking based on functional leaf traits. New Phytol 152:69–83

    Article  Google Scholar 

  • Gates DM, Keegan HJ, Schleter JC, Weidner VR (1965) Spectral properties of plants. Appl Optic 4:11–20

    Article  Google Scholar 

  • Gausman HW, Allen WA, Cardenas R, Richardson AJ (1970) Relation of light reflectance to histological and physical evaluations of cotton leaf maturity. Appl Opt 9:545–552

    Article  Google Scholar 

  • Govender M, Dye PJ, Weiersbye IM, Witkowski ETF, Ahmed F (2009) Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 35:741–752

    Article  Google Scholar 

  • Green RO, Painter TH, Roberts DA, Dozier J (2006) Measuring the expressed abundance of the three phases of water with an imaging spectrometer over snow. Water Resour Res. doi:10.1029/2005WR004509

  • Ghulam A, Li ZL, Qin Q, Yimit H, Wang J (2008) Estimating crop water stress with ETM+ NIR and SWIR data. Agr Forest Meteorol 148:1679–1695

    Article  Google Scholar 

  • Hao X, Qu JJ (2007) Retrieval of real-time live fuel moisture content using MODIS measurements. Remote Sens Environ 108:130–137

    Article  Google Scholar 

  • Hardisky MA, Klemas V, Smart RM (1983) The influence of soil-salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm Eng Remote Sens 49:77–83

    Google Scholar 

  • Hardy CC, Burgan RE (1999) Evaluation of NDVI for monitoring live moisture in three vegetation types of the Western U.S. Photogramm Eng Remote Sens 65:603–610

    Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Article  Google Scholar 

  • Hunt ER, Daughtry CST, Qu JJ, Wang L, Hao X (2011a) Comparison of hyperspectral retrievals with vegetation water indices for leaf and canopy water content. In: Gao W et al (eds) Remote sensing and modeling of ecosystems for sustainability VIII, Proc SPIE, vol 8156. SPIE, Bellingham

    Google Scholar 

  • Hunt ER, Li L, Yilmaz MT, Jackson TJ (2011b) Comparison of vegetation water contents derived from shortwave-infrared and passive microwave sensors over central Iowa. Remote Sens Environ 115:2376–2383

    Article  Google Scholar 

  • Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens Environ 30:43–54

    Article  Google Scholar 

  • Hunt ER, Rock BN, Nobel PS (1987) Measurement of leaf relative water content by infrared reflectance. Remote Sens Environ 22:429–435

    Article  Google Scholar 

  • Jacquemoud S, Bacour C, Poilve H, Frangi JP (2000) Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sens Environ 74:471–481

    Article  Google Scholar 

  • Jacquemoud S, Baret F (1990) Prospect – a model of leaf optical-properties spectra. Remote Sens Environ 34:75–91

    Article  Google Scholar 

  • Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, Francois C, Ustin SL (2009) PROSPECT plus SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:S56–S66

    Article  Google Scholar 

  • Jackson RD, Slater PN, Pinter PJ (1983) Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sens Environ 13:187–208

    Article  Google Scholar 

  • Jackson RD, Pinter PJ, Reginato RJ, Idso SB (1986) Detection and evaluation of plant stresses for crop management decisions. IEEE Trans Geosci Remote Sens 24:99–106

    Article  Google Scholar 

  • Jackson TJ, Chen D, Cosh M, Li F, Anderson M, Walthall C, Doraiswamy PC, Hunt ER (2004) Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans. Remote Sens Environ 92:475–482

    Article  Google Scholar 

  • Ji L, Zhang L, Wylie BK, Rover J (2011) On the terminology of the spectral vegetation index (NIR-SWIR)/(NIR+SWIR). Int J Remote Sens 32:6901–6909

    Article  Google Scholar 

  • Jones HG (2007) Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. J Exp Bot 58:119–130

    Article  Google Scholar 

  • Keane RE, Burgan R, van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. Int J Wildland Fire 10:301–319

    Article  Google Scholar 

  • Keane RE, Drury SA, Karau EC, Hessburg PF, Reynolds KM (2010) A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecol Model 221:2–18

    Article  Google Scholar 

  • Khanna S, Palacios-Orueta A, Whiting ML, Ustin SL, Riaño D, Litago J (2007) Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination. Remote Sens Environ 109:154–165

    Article  Google Scholar 

  • Kimes DS, Markham BL, Tucker CJ, McMurtrey JE (1981) Temporal relationships between spectral response and agronomic variables of a corn canopy. Remote Sens Environ 11:401–411

    Article  Google Scholar 

  • Kogan F, Gitelson A, Zakarin E, Spivak L, Lebed L (2003) AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: calibration and validation. Photogramm Eng Remote Sens 69:899–906

    Google Scholar 

  • Kustas WP, Anderson MC (2009) Advances in thermal infrared remote sensing for land surface modeling. Agr Forest Meteorol 149:2071–2081

    Article  Google Scholar 

  • Lenz TI, Wright IJ, Westoby M (2006) Interrelations among pressure-volume curve traits across species and water availability gradients. Physiol Plant 127:423–433

    Article  Google Scholar 

  • Li L, Cheng YB, Ustin SL, Hua XT, Riaño D (2008) Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression. Adv Space Res 41:1755–1763

    Article  Google Scholar 

  • Li L, Gaiser PW, Gao BC, Bevilacqua RM, Jackson TJ, Njoku EG, Rëdiger C, Calvet JC, Bindlish R (2010) WindSat global soil moisture retrieval and validation. IEEE Trans Geosci Remote Sens 48:2224–2241

    Article  Google Scholar 

  • Li L, Njoku EG, Im E, Chang PS, St. Germain K (2004) A preliminary survey of radio-frequency interference over the U.S. In AQUA AMSR-E data. IEEE Trans Geosci Remote Sens 42:380–390

    Article  Google Scholar 

  • Lichtenthaler HK, Gitelson A, Lang M (1996) Non-destructive determination of chlorophyll content of a green and an aurea mutant of tobacco by reflectance measurements. J Plant Physiol 148:483–493

    Article  Google Scholar 

  • Liu Y, Stanturf J, Goodrick S (2010) Trends in global wildfire potential in a changing climate. Forest Ecol Manag 259:685–697

    Article  Google Scholar 

  • Maki M, Ishiahra M, Tamura M (2004) Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sens Environ 90:441–450

    Article  Google Scholar 

  • Miller JR, Steven MD, Demetriades-Shah TH (1992) Reflection of layered bean leaves over different soil backgrounds: measured and simulated spectra. Int J Remote Sens 13:3273–3286

    Article  Google Scholar 

  • Neigh CSR, Tucker CJ, Townshend JRG (2008) North American vegetation dynamics observed with multi-resolution satellite data. Remote Sens Environ 112:1749–1772

    Article  Google Scholar 

  • Nobel PS (2009) Physicochemical and environmental plant physiology, 4th edn. Academic, San Diego

    Google Scholar 

  • NRC (2004) Climate data records from environmental satellites: interim report. National research council committee on climate data records from NOAA operational satellites. National Academies Press, Washington

    Google Scholar 

  • Olsen CE (1967) Optical sensing the moisture content in fine forest fuels: final report. Willow Run Laboratories, University of Michigan, Ann Arbor

    Google Scholar 

  • Palmer KF, Williams D (1974) Optical properties of water in the near infrared. J Opt Soc Am 64:107–111

    Article  Google Scholar 

  • Passioura J (2007) The drought environment: physical, biological and agricultural perspectives. J Exp Bot 58:113–117

    Article  Google Scholar 

  • Peñuelas J, Filella I, Biel C, Serrano L, Savé R (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens 14:1887–1905

    Article  Google Scholar 

  • Peñuelas J, Piñol J, Ogaya R, Filella I (1997) Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int J Remote Sens 18:2869–2875

    Article  Google Scholar 

  • Pierce LL, Running SW, Riggs GA (1990) Remote detection of canopy water stress in coniferous forests using the NS001 thematic mapper simulator and the thermal infrared multispectral scanner. Photogramm Eng Remote Sens 56:579–586

    Google Scholar 

  • Poorter H, Niimenets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol 182:565–588

    Article  Google Scholar 

  • Ramoelo A, Skidmore AK, Schlerf M, Mathieu R, Heitkönig IMA (2011) Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations. ISPRS J Photogramm Remote Sens 66:408–417

    Article  Google Scholar 

  • Riaño D, Vaughan P, Chuvieco E, Zarco-Tejada PJ, Ustin SL (2005) 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. IEEE Trans Geosci Remote Sens 43:819–826

    Article  Google Scholar 

  • Riggs GA, Running SW (1991) Detection of canopy water stress in conifers using the Airborne Imaging Spectrometer. Remote Sens Environ 35:51–68

    Article  Google Scholar 

  • Roberts DA, Brown K, Green R, Ustin SL, Hinckley T (1998) Investigating the relationship between liquid water and leaf area in clonal populus. In: Summaries of the 7th annual JPL earth science workshop. Jet Propulsion Laboratory, Pasadena

    Google Scholar 

  • Roberts DA, Dennison PE, Peterson S, Sweeny S, Rechel J (2006) Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. J Geophys Res. doi:10.1029/2005JG000113

  • Roberts DA, Ustin SL, Ogenjemiyo S, Greenberg J, Dobrowski SZ, Chen J, Hinckley TM (2004) Spectral and structural measures of northwest forest vegetation at leaf to landscape scales. Ecosystems 7:545–562

    Article  Google Scholar 

  • Rodríguez-Pérez JR, Riaño D, Carlisle E, Ustin S, Smart DR (2007) Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. Am J Enol Viticult 58:302–317

    Google Scholar 

  • Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18:235–249

    Article  Google Scholar 

  • Rollins MG, Keane RE, Parsons RA (2004) Mapping fuels and fire regimes using remote sensing, ecosystem simulation, and gradient modeling. Ecol Appl 14:75–95

    Article  Google Scholar 

  • Romero A, Aguado I, Yebra M (2012) Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversions. Int J Remote Sens 33:396–414

    Article  Google Scholar 

  • Rouse JW, Haas RW, Schell JA, Deering DH, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA GSFC, Greenbelt

    Google Scholar 

  • Schlerf M, Atzberger C, Hill J, Buddenbaum H, Werner W, Schüler G (2010) Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. Int J Appl Earth Observ Geoinform 12:17–26

    Article  Google Scholar 

  • Schwarz MD, Reed BC, White MA (2002) Assessing satellite-derived start-of-season measures in the conterminous USA. Int J Clim 22:1793–1805

    Article  Google Scholar 

  • Serrano L, Ustin SL, Roberts DA, Gamon JA, Peñuelas J (2000) Deriving water content of chaparral vegetation from AVIRIS data. Remote Sens Environ 74:570–581

    Article  Google Scholar 

  • Shipley B, Vu TT (2002) Dry matter content as a measure of dry matter concentration in plants and their parts. New Phytol 153:359–364

    Article  Google Scholar 

  • Sims DA, Gamon JA (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens Environ 84:526–537

    Article  Google Scholar 

  • Stimson HC, Breshears DD, Ustin SL, Kefauver SC (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: pinus edulis and Juniperus monosperma. Remote Sens Environ 96:108–118

    Article  Google Scholar 

  • Trombetti M, Riaño D, Rubio MA, Cheng YB, Ustin SL (2008) Multitemporal vegetation canopy water content retrieval using artificial neural networks for the USA. Remote Sens Environ 112:203–215

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32

    Article  Google Scholar 

  • Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, Saleous NE (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–4498

    Article  Google Scholar 

  • Tucker CJ, Townshend JRG, Goff TE (1985) African land-cover classification using satellite data. Science 227:369–375

    Article  Google Scholar 

  • Ustin SL, Riaño D, Hunt ER (2012) Estimating canopy water content from spectroscopy. Israel J Plant Sci 60:9–23

    Google Scholar 

  • Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54:523–534

    Article  Google Scholar 

  • Ustin SL, Roberts DA, Pinzón J, Jacquemoud S, Gardner M, Scheer B, Castañeda CM, Palacios-Orueta A (1998) Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens Environ 65:280–291

    Article  Google Scholar 

  • Verbesselt J, Somers B, Lhermitte S, Jonckheere I, van Aardt J, Coppin P (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sens Environ 108:357–368

    Article  Google Scholar 

  • Wang L, Hunt ER, Qu JJ, Hao X, Daughtry CST (2011a) Estimating the dry matter content of leaves from the residuals between leaf and water reflectance. Remote Sens Lett 2:137–145

    Article  Google Scholar 

  • Wang L, Hunt ER, Qu JJ, Hao X, Daughtry CST (2011b) Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sens Environ 115:836–840

    Article  Google Scholar 

  • Wang L, Qu JJ, Hao X, Hunt ER (2011c) Estimating dry matter content from spectral reflectances for green leaves of different species. Int J Remote Sens 32:7097–7109

    Article  Google Scholar 

  • Wang L, Qu JJ (2007) NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys Res Lett. doi:10.1029/2007GL031021

  • Waring RH, Landsberg JJ (2011) Generalizing plant-water relations to landscapes. J Plant Ecol 4:101–113

    Article  Google Scholar 

  • Xiao X, Boles S, Frolking S, Salas W, Moore B, Li C (2002a) Observation of flooding and rice transplanting of paddy rice fields at site to landscape scales in China using VEGETATION sensor data. Int J Remote Sens 23:3009–3022

    Article  Google Scholar 

  • Xiao X, Boles S, Liu J, Zhaung D, Liu M (2002b) Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens Environ 82:335–348

    Article  Google Scholar 

  • Yebra M, Chuvieco E (2009a) Generalization of a species-specific look-up table for fuel moisture content assessment. IEEE J Select Topic Appl Earth Observ Remote Sens 2:21–26

    Article  Google Scholar 

  • Yebra M, Chuvieco E (2009b) Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: solving the ill-posed inverse problem. Remote Sens Environ 113:2403–2411

    Article  Google Scholar 

  • Yebra M, Chuvieco E, Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agr Forest Meteorol 148:523–536

    Article  Google Scholar 

  • Yilmaz MT, Hunt ER, Goins LD, Ustin SL, Vanderbilt VC, Jackson TJ (2008a) Vegetation water content during SMEX04 from ground data and landsat 5 thematic mapper imagery. Remote Sens Environ 112:350–362

    Article  Google Scholar 

  • Yilmaz MT, Hunt ER, Jackson TJ (2008b) Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sens Environ 112:2514–2522

    Article  Google Scholar 

  • Zarco-Tejada PJ, Rueda CA, Ustin SL (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens Environ 85:109–124

    Article  Google Scholar 

  • Zhang QX, Li QB, Zhang GJ (2011) Scattering impact analysis and correction for leaf biochemical parameter estimation using VIS-NIR spectroscopy. Spectroscopy 26:28–39

    Google Scholar 

Download references

Acknowledgements

Funding for this work was provided by the NASA Terrestrial Hydrology Program (Grants NAG5-11260 and NNX09AN51G) and MODIS Science Team (Grant NNX11AF93G).

Author information

Authors and Affiliations

  1. USDA-ARS Hydrology and Remote Sensing Laboratory, Bldg 007 Room 104 BARC-West, 10300 Baltimore Avenue, Beltsville, MD, 20705, USA

    E. Raymond Hunt Jr.

  2. Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA

    Susan L. Ustin & David Riaño

  3. Centro de Ciencias Humanas y Sociales, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, 28037, Spain

    David Riaño

Authors
  1. E. Raymond Hunt Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susan L. Ustin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Riaño
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Raymond Hunt Jr. .

Editor information

Editors and Affiliations

  1. , Environmental Science and Technology, George Mason University, Fairfax, 22030, USA

    John Qu

  2. Administration, NOAA/NESDIS, Center for Satellite Application and Res, National Oceanic & Atmospheric, Auth Road 5200, Camp Springs, 20746, Maryland, USA

    Alfred Powell

  3. , Climate and Water Programme, World Meteorological Organization, 7bis, Avenue de la Paix, Geneva, 1211, Switzerland

    M.V.K. Sivakumar

Additional information

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Hunt, E.R., Ustin, S.L., Riaño, D. (2013). Remote Sensing of Leaf, Canopy, and Vegetation Water Contents for Satellite Environmental Data Records. In: Qu, J., Powell, A., Sivakumar, M. (eds) Satellite-based Applications on Climate Change. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5872-8_20

Download citation

Publish with us

Policies and ethics

Search

Navigation