Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

Hydrological Processes

Volumn 26, Issue 8, 2012, Pages 1230-1241

  Wang, Quan ^a   Li, Pingheng ^b  

^a SHIZUOKA UNIVERSITY   (Japan)

^b XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY   (China)

Author keywords

EWT; FMC; Hyperspectral indices; PROSPECT; Reflectance derivatives

Indexed keywords

DATA SETS; EQUIVALENT WATER THICKNESS; EWT; FIELD REFLECTANCE; FIRE RISKS; FMC; FUEL MOISTURE CONTENT; HIGH PRECISION; HYPERSPECTRAL; LEAF ANATOMY; LEAF WATER CONTENT; PHENOLOGICAL STAGE; PROSPECT; REFLECTANCE MODEL; TEMPERATE DECIDUOUS FOREST; TERRESTRIAL ECOSYSTEMS; TWO WAVELENGTH; VEGETATION INDEX; WATER STATUS; WAVEBANDS; WAVELENGTH DOMAINS;

COMPUTER SIMULATION; FUELS; PHENOLS; REFLECTION; RISK PERCEPTION;

FORESTRY;

DATA SET; DECIDUOUS FOREST; ECOSYSTEM FUNCTION; LEAF AREA INDEX; MOISTURE CONTENT; NUMERICAL MODEL; PARAMETERIZATION; PHENOLOGY; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE; TEMPERATE FOREST; VEGETATION INDEX; WATER COLUMN;

DATA PROCESSING; FORESTRY; FUELS; PHENOLS; REFLECTANCE; RISK ASSESSMENT; SIMULATION; WAVE LENGTHS;

EID: 84859434833     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.8221     Document Type: Article

References (49)

1. Allen WA, Gausman HW, Richardson AJ, Cardenas R. 1971. Water and air changes in grapefruit, corn, and cotton leaves with maturation. Agronomy Journal 63: 392-394.
2. Bowyer P, Danson FM. 2004. Sensitivity of remotely sensed spectral reflectance to variation in live fuel moisture content. Remote Sensing of Environment 92: 297-308.
3. Carter CA. 1998. Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance of pine canopies. Remote Sensing of Environment 63: 61-72.
4. Ceccato P, Flasse S, Gregoire JM. 2002a. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sensing of Environment 82: 198-207.
5. Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire JM. 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 77: 22-33.
6. Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S. 2002b. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment 82: 188-197.
7. Cheng YB, Zarco-Tejada PJ, Riaño D, Rueda CA, Ustin S. 2006. Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment 105: 354-366.
8. Chuvieco E, Aguado I, Cocero D, Riaño D. 2003. Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR images in forest fire danger studies. International Journal of Remote Sensing 24: 1621-1637.
9. Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez J, Martín S, Ibarra P, de la Riva J, Baeza J, Rodríguez F, Molina JR, Herrera MA, Zamora R. 2010. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 221(1): 46-58. DOI:10.1016/j. ecolmodel.2008.11.017.
10. Chuvieco E, Riano 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. International Journal of Remote Sensing 23: 2145-2162.
11. Colombo R, Meroni M, Marchesi A, Busetto L, Rossini M, Giardino C, Panigada C. 2008. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment 112: 1820-1834. DOI:10.1016/j.rse.2007.09.005.
12. Danson FM, Bowyer P. 2004. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment 92: 309-321.
13. Danson FM, Steven MD, Malthus TJ, Clark JA. 1992. High spectral resolution data for determining leaf water content. International Journal of Remote Sensing 13(3): 461-470.
14. Datt B. 1999. Remote sensing of water content in Eucalyptus leaves. Australian Journal of Botany 47: 909-923.
15. Dawson TP, Curran PJ, North PRJ, Plummer SE. 1999. The propagation of foliar biochemical absorption features in forest canopy reflectance: A theoretical analysis. Remote Sensing of Environment 67: 147-159.
16. Dennison PE, Roberts DA, Peterson SH, Rechel J. 2005. Use of normalized difference water index for monitoring live fuel moisture. International Journal of Remote Sensing 26: 1035-1042.
17. Feret 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 Sensing of Environment 112: 3030-3043. DOI:10.1016/j.rse.2008.02.012.
18. Gamon, JA, Rahman, AF, Dungan, JL, Schildhauer, M, Huemmrich, KF. 2006. Spectral Network (SpecNet)-What is it and why do we need it? Remote Sensing of Environment 103: 227-235.
19. Gao BC. 1996. NDWI-A normalised difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: 257-266.
20. Gao BC, Goetz AFH. 1995. Retrieval of equivalent water thickness and information related to biochemical-components of vegetation canopies from AVIRIS data. Remote Sensing of Environment 52: 155-162.
21. Garcia M, Aguado I, Chuvieco E. 2008. Combining AVHRR and meteorological data for estimating live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 112: 3618-3627.
22. Hardisky MA, Klemas V, Smart RM. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 49: 77-83.
23. Horler DNH, Dockray M, Barbera J, Barringer AR. 1983. Red edge measurements for remotely sensing plant chlorophyll content. Advances in Space Research 3: 273-277.
24. Hunt Jr ER. 1991. Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. International Journal of Remote Sensing 12: 643-649.
25. Hunt Jr ER, Rock BN. 1989. Detection of changes in leaf water content using near-and middle-infrared reflectance. Remote Sensing of Environment 30: 43-54.
26. Hunt Jr ER, Rock BN, Nobel PS. 1987. Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment 22: 429-435.
27. Jacquemoud S, Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34: 74-91.
28. Jacquemoud S, Bacour C, Poilve' H, Frangi J-P. 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance-direct and inverse mode. Remote Sensing of Environment 74: 471-481.
29. Jacquemoud S, Ustin S L, Verdebout J, Schmuck G, Andreoli G, Hosgood B. 1996. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment 56: 194-202.
30. Jacquemoud S, Verdebout J, Schmück G, Andreoli G, Hosgood B. 1995. Investigation of leaf biochemistry by statistics, Remote Sensing of Environment 54: 180-188.
31. Le Maire G, François C, Dufrêne E. 2004. Towards universal deciduous broad leaf chlorophyll indices using PROSPECT simulated dataset and hyperspectral reflectance measurements. Remote Sensing of Environment 89: 1-28.
32. Le Maire G, François C, Soudani K, Berveiller D, Pontailler JY, Bréda N, Genet H, Davi H, Dufrêne E. 2008. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment 112: 3846-3864.
33. Peñuelas J, Filella I, Biel C, Serrano L, Save R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14: 1887-1905.
34. Peñuelas J, Piñol J. Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing 18: 2869-2875.
35. Peterson SH, Roberts DA, Dennison PE. 2008. Mapping live fuel moisture with MODIS data: A multiple regression approach. Remote Sensing of Environment 112: 4272-4284.
36. Piñol J, Filella I, Ogaya R, Peñuelas J. 1998. Ground based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agricultural and Forest Meteorology 90: 173-186.
37. Roberts DA, Peterson S, Dennison PE, Sweeney 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. Journal of Geophysical Research 111: G04S02, 16 pp. DOI:10.1029/2005JG000113.
38. Rock BN, Vogelmann JE, Williams DL, Vogelmann AF, Hoshizaki T. 1986. Remote detection of forest damage. Bioscience 36: 439-445.
39. Savitzky A, Golay MJE. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8): 1627-1639.
40. Seelig HD, Hoehn A, Stodieck LS, Klaus DM, Adams III WW, Emery WJ. 2008. Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sensing of Environment 112: 445-455.
41. Serrano L, Ustin SL, Roberts DA, Gamon JA, Peñelas J. 2000. Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment 74: 570-581.
42. Stow D, Niphadkar M, Kaiser J. 2005. MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. International Journal of Remote Sensing 26: 3867-3873.
43. Ustin SL, Roberts DA, Pinzon J, Jacquemoud S, Gardner M, Scheer G, Castaneda CM, Palacios-Orueta A. 1998. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sensing of Environment 65: 280-291.
44. Viegas DX, Viegas TP, Ferreira AD. 1992. Moisture content of fine forest fuels and fire occurrence in central Portugal. The International Journal of Wildland Fire 2: 69-85.
45. Wang Q, Iio A, Tenhunen J, Kakubari Y. 2008a. Annual and seasonal variations in photosynthetic capacity of Fagus crenata along elevation gradient in the Naeba Mountains, Japan. Tree Physiology 28: 277-285.
46. Wang Q, Iio A, Kakubari Y. 2008b. Broadband simple ratio closely traced seasonal trajectory of canopy photosynthetic capacity. Geophysical Research Letters 35: L07401, 5 pp. DOI:10.1029/2008GL033619.
47. Yebra M, Chuvieco E, Riaño D. 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology 148: 523-536.
48. Zarco-Tejada PJ, Rueda CA, Ustin SL. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment 85: 109-124.
49. Zarco-Tejada PJ, Ustin SL. 2001. Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium, IGARSS'01. Sydney, Australia. DOI:10.1109/IGARSS.2001.976152.