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

Abstract: 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.

[1]  Rolf Stuhlmann,et al.  The Baltic Sea Experiment BALTEX: A brief overview and some selected results of the authors , 1998 .

[2]  Mathias Disney,et al.  Deriving albedo maps for HAPEX-Sahel from ASAS data using kernel-driven BRDF models , 1999 .

[3]  Gin-Rong Liu,et al.  Improved atmospheric correction process in monitoring SST around the outfall of a nuclear power plant , 1994 .

[4]  Daniela Jacob,et al.  A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin , 2001 .

[5]  J. H. Park,et al.  REMOTE SENSING ESTIMATION OF FOREST LAI IN CLOSE CANOPY SITUATION , 2004 .

[6]  Thomas Raddatz,et al.  Parameterization of snow-free land surface albedo as a function of vegetation phenology based on MODIS data and applied in climate modelling , 2009 .

[7]  Andrei P. Sokolov,et al.  Climate Dynamics (2006) DOI 10.1007/s00382-005-0092-6 , 2005 .

[8]  J. Christensen,et al.  Climate modelling: Severe summertime flooding in Europe , 2003, Nature.

[9]  Stefan Hagemann,et al.  An improved land surface parameter dataset for global and regional climate models , 2002 .

[10]  P. J. Curran,et al.  The estimation of green-leaf-area index from remotely sensed airborne multispectral scanner data , 1984 .

[11]  S. Liang,et al.  A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies , 2005 .

[12]  Guang Zheng,et al.  Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors , 2009, Sensors.

[13]  D. Lüthi,et al.  The role of increasing temperature variability in European summer heatwaves , 2004, Nature.

[14]  retrieved from Landsat data , 2009 .

[15]  S. Ueno,et al.  The surface albedo retrieval of mountainous forest area from satellite MSS data , 1995 .

[16]  S. Seneviratne,et al.  Land–atmosphere coupling and climate change in Europe , 2006, Nature.

[17]  R. Brown,et al.  Relationships Between Leaf Area Index and Apparent Photosynthesis in Alfalfa (Medicago sativa L.) and Ladino Clover (Trifolium repens L.)1 , 1967 .

[18]  C. H. Liu,et al.  Variability of the bare soil albedo due to different solar zenith angles and atmospheric haziness , 1994 .

[19]  Stefan Hagemann,et al.  Gradient in the climate change signal of European discharge predicted by a multi-model ensemble , 2007 .

[20]  W. Cohen,et al.  An improved strategy for regression of biophysical variables and Landsat ETM+ data. , 2003 .

[21]  J. Moreno,et al.  Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data , 2008 .

[22]  S. Franklin,et al.  Remote sensing of forest environments : concepts and case studies , 2003 .

[23]  Yong Pang,et al.  Forest LAI estimation comparison using LiDAR and hyperspectral data in boreal and temperate forests , 2009, Optical Engineering + Applications.

[24]  Guerric Le Maire,et al.  Erratum to ``Comparative analysis of IKONOS, SPOT, and ETM+ data leaf area index estimation in temperature coniferous and deciduous forest stands'' [Remote Sensing of Environment 102 (2006) 161-175] , 2006 .

[25]  K. Itten,et al.  Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties , 2004 .

[26]  Katja Richter,et al.  Leaf Area Index and Surface Albedo Estimation: Comparative Analysis from Vegetation Indexes to Radiative Transfer Models , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[27]  C. Mátyás.  Ecological Challenges of Climate Change in Europe's Continental, Drought-Threatened Southeast , 2009 .

[28]  E. Reimer,et al.  Validation of boundary layer parameters of climate model REMO: estimation of leaf area index from NOAA-AVHRR data for the Baltimos region , 2014, Theoretical and Applied Climatology.

[29]  Alan H. Strahler,et al.  Remote Sensing of Albedo Using the BRDF in Relation to Land Surface Properties , 2000 .

[30]  Wenjun Liu,et al.  Improving Land Surface Pixel Level Albedo Characterization Using Sub-Pixel Information Retrieved from Remote Sensing , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[31]  F. Giorgi,et al.  Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations , 2005 .

[32]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[33]  F. Ulaby,et al.  Microwave radar response to canopy moisture, leaf-area index, and dry weight of wheat, corn, and sorghum☆ , 1981 .

[34]  G. Rybicki.  Radiative transfer , 2019, Climate Change and Terrestrial Ecosystem Modeling.

[35]  U. Karstens,et al.  A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period , 2001 .

[36]  Woo-Kyun Lee,et al.  ESTIMATION OF LAI USING LIDAR REMOTE SENSING IN FOREST , 2007 .

[37]  A. Pitman.  The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .

[38]  Alessandra Conversi,et al.  Comparative Analysis , 2009, Encyclopedia of Database Systems.

[39]  Richard G. Jones,et al.  Global high resolution versus Limited Area Model climate change projections over Europe: quantifying confidence level from PRUDENCE results , 2005 .

[40]  C. Valeo,et al.  A robust new method for the remote estimation of LAI in montane and boreal forests , 2007 .

[41]  D. Jacob,et al.  Slowdown of the thermohaline circulation causes enhanced maritime climate influence and snow cover over Europe , 2005 .