*

Remote sensing-supported vegetation parameters for regional climate models: a brief review

H Latifi (1)   , B Galos (2)

iForest - Biogeosciences and Forestry, Volume 3, Issue 4, Pages 98-101 (2010)
doi: https://doi.org/10.3832/ifor0543-003
Published: Jul 15, 2010 - Copyright © 2010 SISEF

Review Papers

Collection/Special Issue: NFZ Summer School 2009 - Birmensdorf (Switzerland)
Long-term ecosystem research: understanding the present to shape the future
Guest Editors: Marcus Schaub (WSL, Switzerland)


Land surface plays a key role in a climate system. Thus, the land surface description will become increasingly important for climate modelling by its feedbacks on the climate. Various forms of active/passive remotely sensed data are nowadays being used to provide continuous and up-to-date information on the earth’s surface on both global and regional scales. This information is useful to be included in climate models. This review summarizes how LAI and albedo, two of the most important land surface parameters, could be derived from remote sensing. Whereas the high acquisition frequency, accessibility, and spatial continuality are referred to potential advantages, the scaling is still a drawback which may cause further problems such as incompatibility of different remote sensing data sources for a specific climate model. Moreover, issues like shadow and atmospheric effects are often problematic, especially when optical remote sensing is applied. Here, suggestions for improvement are made and open questions are pointed out.

  Keywords


Regional climate models, Forest and vegetation parameters, Active and passive remote sensing

Authors’ address

(1)
H Latifi
Dept. of Remote Sensing and Landscape Information Systems, University of Freiburg, Tennenbacher straße 4, D-79106 Freiburg (Germany)
(2)
B Galos
Institute of Environment and Earth Sciences, Faculty of Forestry, University of West Hungary, 9400 Sopron (Hungary)

Corresponding author

Citation

Latifi H, Galos B (2010). Remote sensing-supported vegetation parameters for regional climate models: a brief review. iForest 3: 98-101. - doi: 10.3832/ifor0543-003

Academic Editor

Marcus Schaub

Paper history

Received: May 25, 2010
Accepted: May 31, 2010

First online: Jul 15, 2010
Publication Date: Jul 15, 2010
Publication Time: 1.50 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 14780
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 11111
Abstract Page Views: 699
PDF Downloads: 2576
Citation/Reference Downloads: 42
XML Downloads: 352

Web Metrics
Days since publication: 3415
Overall contacts: 14780
Avg. contacts per week: 30.30

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Aug 2019)

Total number of cites (since 2010): 7
Average cites per year: 0.70

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Berthelot B, Dedieu G, Cabot F, Adam S (1994)
Estimation of surface reflectance and vegetation index using NOAA/AVHRR: methods and results at global scale. Communication for the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, ISPRS. Val d’Isere, France.
Gscholar
(2)
Blümel B, Reimer E (2009)
Validation of boundary layer parameters of climate model REMO: estimation of LAI from NOAA-AVHRR data for the Baltimos region. Theoretical and Applied Climatology (Special issue).
CrossRef | Gscholar
(3)
Brakke TW, Kanemasu ET, Steiner JL, Ulaby FT, Wilson E (1981)
Microwave radar response to canopy moisture, LAI and dry weight of wheat, corn, and sorghum. Remote Sensing of Environment 11: 207-220.
CrossRef | Gscholar
(4)
Brovkin V, Claussen M, Driesschaert E, Fichefet T, Kicklighter D, Loutre MF, Matthews HD, Ramankutty N, Schaeffer M, Sokolov A (2006)
Biogeophysical effects of historical land cover changes simulated by six Earth system models of intermediate complexity. Climate Dynamics 26: 587-600.
CrossRef | Gscholar
(5)
Christensen JH, Christensen OB (2003)
Severe summertime flooding in Europe. Nature 421: 805-806.
CrossRef | Gscholar
(6)
Cohen WB, Maiersperger TK, Stith TG, Tumer DP (2003)
An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment 84: 561-571.
CrossRef | Gscholar
(7)
Déqué M, Jones RG, Wild M, Giorgi F, Christensen JH, Hassell DC, Vidale PL, Rockel B, Jacob D, Kjellstrom E, de Castro M, Kucharski F, van den Hurk B (2005)
Global high resolution versus limited area model climate change projections over Europe: quantifying confidence level from PRUDENCE results. Climate Dynamics 25: 653-670.
CrossRef | Gscholar
(8)
Fang H, Liang S (2005)
A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment 94 (3): 405-424.
CrossRef | Gscholar
(9)
Giorgi F, Bi X (2005)
Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophysical Research Letters 32: L21715.
Gscholar
(10)
Gonzalez-Sanpedro MC, Toan TL, Moreno J, Kergoat L, Rubio E (2008)
Seasonal variations of LAI of agricultural fields retrieved from Landsat data. Remote Sensing of Environment 112: 810-824.
CrossRef | Gscholar
(11)
Hagemann S, Botzet M, Dümenil L, Machenhauer B (1999)
Derivation of global GCM boundary conditions from 1km land use satellite data. MPI-M, Report 289, Hamburg, Germany.
Gscholar
(12)
Hagemann S (2002)
An improved land surface parameter dataset for global and regional climate models. MPI-M, Report 336, Hamburg, Germany.
Gscholar
(13)
Hagemann S, Jacob D (2007)
Gradient in the climate change signal of European discharge predicted by a multi-model ensemble. Climatic Change 81: 309-327.
CrossRef | Gscholar
(14)
Inoue Y, Olioso A (2004)
Estimating dynamics of CO2 flux in agro-ecosystems based on synergy of remote sensing and process modelling - a methodological study. In: “Global environmental change in the ocean and on land” (Shyomi et al. eds). Terrapub 2004, pp. 375-390.
Gscholar
(15)
Jacob D (2001)
A note to the simulation of the annual inter-annual variability of the water budget over the Baltic Sea drainage Basin. Me-teorology and Atmospheric Physics 77: 61-73.
CrossRef | Gscholar
(16)
Jacob D, Van den Hurk BJJM, Andræ U, Elgered G, Fortelius C, Graham LP, Jackson SD, Karstens U, Köpken CHR, Lindau R, Podzun R, Rockel B, Rubel F, Sass BH, Smith RNB, Yang X (2001)
A comprehensive model intercomparison study investigating the water budget during the BALTEX-PIDCAP Period. Meteorology and Atmospheric Physics 77: 19-43.
CrossRef | Gscholar
(17)
Jacob D, Goettel H, Jungclaus J, Muskulus M, Podzun R, Marotzke J (2005)
Slowdown of the thermohaline circulation causes enhanced maritime climate influence and snow cover over Europe. Geophysical Research Letters 32: L21711.
CrossRef | Gscholar
(18)
Kawata Y, Ueno S (1995)
The surface albedo retrieval of mountainous forest area from satellite MSS data. Applied Mathematics and Computations 69: 41-59.
CrossRef | Gscholar
(19)
Knorr W (1997)
Satellitengeschützte fernentkundung und modellierung des globalen CO2-austausch der landvegetation: eine synthese. Examensarbeit 49, MPI-M Hamburg, Germany.
Gscholar
(20)
Kotz B, Schaepman M, Morsdorf F, Bowyer P, Itten K, Allgöwer B (2004)
Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sensing of Environment 92: 332-344.
CrossRef | Gscholar
(21)
Kwak DA, Lee WK, Cho HK (2007)
Estimation of LAI using LiDAR remote sensing in forest. ISPRS Workshop on Laser Scanning and SilviLaser 2007, Espoo-Finland.
Gscholar
(22)
Lee KS, Park YI, Kim SH, Park, JH, Woo CS, Jang, KC (2004)
Remote sensing estimation of forest LAI in close canopy situation. Proceedings of ISPRS conference, Istanbul, Turkey.
Online | Gscholar
(23)
Lewis P, Disney MI, Barnsley MJ, Muller JP (1999)
Deriving albedo maps for HAPEX-Sahel from ASAS data using kernel-driven BRDF models. Hydrology and Earth Sytem Sciences 3 (1): 1-13.
CrossRef | Gscholar
(24)
Liu W, Hu B, Wang S (2008)
Improving land surface pixel level albedo characterization using sub-pixel information retrieved from remote sensing. Proceedings of IGARSS2008, Boston, Massachusetts, USA.
Online | Gscholar
(25)
Liu CH, Chen AJ, Liu GR (1994)
Variability of the bare soil albedo due to different solar zenith angles and atmospheric haziness. International Journal of Remote Sensing 15 (13): 2531-2542.
CrossRef | Gscholar
(26)
Lucht W, Schaaf C, Strahler AH, d’Entremont R (2000)
Remote sensing of albedo using the BRDF in relation to land surface properties. In: “Observing land from space: science, customers and technology” (Verstraete MM, et al. eds). Kluwer Academic Publishers, pp. 175-186.
Gscholar
(27)
Mátyás C (2008)
Ecological challenges of climate change in Europe’s continental, drought-threatened Southeast. In: “Regional aspects of climate-terrestrial-hydrologic interactions in non-boreal Eastern Europe” (Groisman PY, Sergiy VI eds). NATO Science Series, Springer Verlag, pp. 35-46.
Gscholar
(28)
McAllister DM, Valeo C (2007)
A robust new method for the remote estimation of LAI in montane and boreal forests. International Journal of Remote Sensing 28 (8): 1891-1905.
CrossRef | Gscholar
(29)
Pang Y, Tan B, Solberg S, Li Z (2009)
Forest LAI estimation comparison using LiDAR and hyperspectral data in boreal and temperate forests. In: “Remote Sensing and Modeling of Ecosystems for Sustainability VI” (Gao W, Jackson TJ). Proceedings of the SPIE, vol. 7454, pp. 74540-74548.
Gscholar
(30)
Pitman AJ (2003)
Review the evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology. 23: 479-510.
Gscholar
(31)
Post DF, Fimbres A, Matthias AD, Sano EE, Accioly L, Batchily AK, Ferreira LG (2000)
Predicting soil albedo from soil color and spectral reflectance data. Soil Science Society American Journal 64: 1027-1034.
CrossRef | Gscholar
(32)
Raschke E, Karstens U, Nolte-Holube R, Brandt R, Isemer HJ, Hoffmann D, Lobmeyer M, Rockel B, Stuhlmann R (1998)
The Baltic sea experiment BALTEX: a brief overview and some selected results of the authors. Surveys in Geophysics 19: 1-22.
CrossRef | Gscholar
(33)
Rechid D, Raddatz TJ, Jacob D (2007)
Parameterization of snow-free land surface albedo as a function of vegetation phenology based on MODIS data and applied in climate modeling. Theoretical and Applied Climatology 95: 245-255.
CrossRef | Gscholar
(34)
Richter K, Vuolo F, D’Urso G (2008)
LAI and surface albedo estimation: comparative analysis from vegetation indexes to radiative transfer models. Proceedings of IGARSS2008, Boston, Massachusetts, USA. -
Online | Gscholar
(35)
Schär C, Vidale P L, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004)
The role of increasing temperature variability in European summer heatwaves. Nature 427: 332-336.
CrossRef | Gscholar
(36)
Seneviratne, SI, Lüthi D, Litschi M, Schär C (2006)
Land-atmosphere coupling and climate change in Europe. Nature 443: 205-209.
CrossRef | Gscholar
(37)
Soudani K, François C, Maire GL, Dantec VL, Dufrêne E (2006)
Comparative analysis of IKONOS, SPOT, and ETM+ data for LAI estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment 102: 161-175.
CrossRef | Gscholar
(38)
Wardley NW, Curran PJ (1984)
The estimation of green LAI from remotely sensed airborne multispectral scanner data. International Journal of Remote Sensing 5: 671-679.
CrossRef | Gscholar
(39)
Wilfong RT, Brown RH, et al. (1967)
Relationships between leaf area index and apparent photosynthesis in Alfalfa (Medicago Sativa L.). and Ladino clover (Trifolium Repens L.). Crop Science 7 (1): 27-30.
CrossRef | Gscholar
(40)
Wulder MA, Franklin SE (2003)
Remote sensing of forest environments: concepts and case studies. Kluwer Academic Publishers, Boston, USA.
Gscholar
(41)
Zheng M, Moskal MM (2009)
Retrieving LAI using remote sensing: theories, methods and sensors. Sensors 9: 2719-2745.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website