nach oben

Erschienen in:

2013 | OriginalPaper | Buchkapitel

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

verfasst von : E. Raymond Hunt Jr., Susan L. Ustin, David Riaño

Erschienen in: Satellite-based Applications on Climate Change

Verlag: Springer Netherlands

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Land Surface Temperature (LST) Retrieval from GOES Satellite Observations

Nächstes Kapitel Recent Advances and Challenges of Monitoring Climate Change from Space

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 31:245–253CrossRef

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

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

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

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

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

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 101:6039–6044CrossRef

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

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 45:16–46CrossRef

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

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

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

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

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 82:188–197CrossRef

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 82:198–207CrossRef

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 87:148–160CrossRef

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

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

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

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 105:354–366CrossRef

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

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

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 221:46–58CrossRef

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 23:2145–2162CrossRef

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

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

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

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

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

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 108:138–150CrossRef

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 112:1647–1657CrossRef

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 8:225–236CrossRef

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 97:26–38CrossRef

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 101:53–62CrossRef

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 17:18–27CrossRef

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

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

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 229:170–182CrossRef

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

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

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

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 87:111–121CrossRef

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 112:3030–3042CrossRef

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 115:2742–2750CrossRef

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 61:34–45CrossRef

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

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

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

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 52:155–162CrossRef

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–S24CrossRef

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 152:69–83CrossRef

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

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

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 35:741–752CrossRef

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 148:1679–1695CrossRef

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

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

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

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

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 83:195–213CrossRef

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

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 115:2376–2383CrossRef

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

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

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 74:471–481CrossRef

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

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–S66CrossRef

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 13:187–208CrossRef

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

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 92:475–482CrossRef

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 32:6901–6909CrossRef

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 58:119–130CrossRef

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 10:301–319CrossRef

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 221:2–18CrossRef

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 109:154–165CrossRef

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

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

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

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

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 41:1755–1763CrossRef

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 48:2224–2241CrossRef

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 42:380–390CrossRef

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 148:483–493CrossRef

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

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 90:441–450CrossRef

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 13:3273–3286CrossRef

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

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

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

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

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

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

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 14:1887–1905CrossRef

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 18:2869–2875CrossRef

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

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 182:565–588CrossRef

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 66:408–417CrossRef

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 43:819–826CrossRef

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

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

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 7:545–562CrossRef

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

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

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

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 33:396–414CrossRef

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

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 12:17–26CrossRef

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

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

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

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 84:526–537CrossRef

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 96:108–118CrossRef

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 112:203–215CrossRef

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

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

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 26:4485–4498CrossRef

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

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

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

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 65:280–291CrossRef

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 108:357–368CrossRef

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 2:137–145CrossRef

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

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 32:7097–7109CrossRef

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 4:101–113CrossRef

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 23:3009–3022CrossRef

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 82:335–348CrossRef

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 2:21–26CrossRef

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 113:2403–2411CrossRef

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

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 112:350–362CrossRef

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

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

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

Titel: Remote Sensing of Leaf, Canopy, and Vegetation Water Contents for Satellite Environmental Data Records
verfasst von: E. Raymond Hunt Jr.
Susan L. Ustin
David Riaño
Verlag: Springer Netherlands
Buch: Satellite-based Applications on Climate Change
Print ISBN: 978-94-007-5871-1

Electronic ISBN: 978-94-007-5872-8

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-94-007-5872-8_20