ARTICLES

Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data

Expand
  • 1. State Key Laboratory for Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China

Received date: 2013-07-04

  Revised date: 2013-08-07

  Online published: 2013-09-29

Abstract

Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.

Cite this article

LIU Xiang, LIU Rong-Gao, CHEN Jing-Meng, CHENG Xiao, ZHENG Guang . Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data[J]. Journal of Geo-information Science, 2013 , 15(5) : 734 -743 . DOI: 10.3724/SP.J.1047.2013.00734

References

[1] Ganopolski A, Kubatzki C, Claussen M, et al. The influence of vegetation-atmosphere-ocean interaction on climate during the mid-Holocene[J]. Science, 1998,280 (5371):1916-1919.

[2] Watson D J. Comparative physiological studies in the growth of field crops. I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years[J]. Annals of Botany, 1947,11(41):41-76.

[3] Monteith J L, Unsworth M H. Principles of Environmental Physics (2nd Edition)[M]. London: Edward Arnold, 1973.

[4] Smith N J, Clark D R. Estimating salad leaf area index and leaf biomass from diffuse light attenuation[J]. Canadian Journal of Forest Research, 1990,20(9):1265-1270.

[5] Chen J M, Black T A. Defining Leaf-Area Index for non-flat leaves[J]. Plant Cell and Environment, 1992,15(4): 421-429.

[6] Chen J M, Menges C H, Leblanc S G. Global mapping of foliage clumping index using multi-angular satellite data [J]. Remote Sensing of Environment, 2005,97(4):447-457.

[7] Myneni R B, Ross J, Asrar G, et al. A review on the theory of photon transport in leaf canopies[J]. Agricultural and Forest Meteorology, 1989, 45(1-2):1-153.

[8] Arora V. Modeling vegetation as a dynamic component in soil-vegetation-atmosphere transfer schemes and hydrological models[J]. Reviews of Geophysics, 2002,40(2):1-26.

[9] Sellers P J, Dickinson R E, Randall D A, et al. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere[J]. Science, 1997,275(5299): 502-509.

[10] Zhao M, Running S W. Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009[J]. Science, 2010,329(5994):940-943.

[11] Leuning R, Zhang Y Q, Rajaud A, et al. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation[J].Water Resources Research, 2008,44(10):1-17.

[12] Peterson D L, Aber J D, Matson P A, et al. Remote sensing of forest canopy and leaf biochemical contents[J]. Remote Sensing of Environment, 1988,24(1):85-108.

[13] Liang S L. Quantitative remote sensing of land surface [M]. New Jersey:Wiley Interscience, 2004,93-100.

[14] Nilson T, Peterson U. A forest canopy reflectance model and a test case[J]. Remote Sensing of Environment, 1991, 37(2):131-142.

[15] Masson V, Champeaux J L, Chauvin F, et al. A global database of land surface parameters at 1-km resolution in meteorological and climate models[J]. Journal of Climatology, 2003,16(9):1261-1282.

[16] Chen J M, Pavlic G, Brown L, et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J]. Remote Sensing of Environment, 2002, 80(1):165-184.

[17] Houborg R, Soegaard H, Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data[J]. Remote Sensing of Environment, 2007,106(1):39-58.

[18] Chen J M. Evaluation of vegetation indices and a modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996(22):229-242.

[19] Jacquemoud S, Baret F. PROSPECT - a model of leaf optical-properties spectra[J]. Remote Sensing of Environment, 1990,34(2):75-91.

[20] Dawson T P, Curran P J, Plummer S E. LIBERTY - Modeling the effects of leaf biochemical concentration on reflectance spectra[J]. Remote Sensing of Environment, 1998,65(1):50-60.

[21] Verhoef W, Light-scattering by leaf layers with application to canopy reflectance modeling-the SAIL model[J]. Remote Sensing of Environment, 1984,16(2):125-141.

[22] Li X W, Strahler A H. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy- effect of crown shape and mutual shadowing[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30 (2):276-292.

[23] Chen J M, Leblanc S G. A four-scale bidirectional reflectance model based on canopy architecture[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(5): 1316-1337.

[24] Bicheron P, Leroy M. A method of biophysical parameter retrieval at global scale by inversion of a vegetation reflectance model[J]. Remote Sensing of Environment, 1999,67(3):251-266.

[25] Myneni R B, Hoffman S, Knyazikhin Y, et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data[J]. Remote Sensing of Environment, 2002,83(1-2):14-231.

[26] Bacour C, Baret F, Beal D, et al. Neural network estimation of LAI, fAPAR, fCover and LAIxC(ab), from top of canopy MERIS reflectance data: Principles and validation [J]. Remote Sensing of Environment, 2006,105(4):313- 325.

[27] Baret F, Hagolle O, Geiger B, et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION - Part 1: Principles of the algorithm[J]. Remote Sensing of Environment, 2007,110(3):275-286.

[28] Weiss M, Baret F, Garrigues S, et al. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products[J]. Remote Sensing of Environment, 2007,110 (3):317-331.

[29] Deng F, Chen J M, Plummer S, et al. Algorithm for global leaf area index retrieval using satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8):2219-2229.

[30] Liu Y, Liu R, Chen J M. Retrospective retrieval of longterm consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data[J]. Journal of Geophysical Research-Biogeosciences, 2012, 117 (G04003): 1-14.

[31] Baret F, Weiss M, Lacaze R, et al. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production[J]. Remote Sensing of Environment, 2013, in press.

[32] Los S O, Collatz G J, Sellers P J, et al. A global 9-yr biophysical land surface dataset from NOAA AVHRR data [J]. Journal of Hydrometeorology, 2000,1(2):183-199.

[33] Ganguly S, Schull MA, Samanta A, et al. Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory[J]. Remote Sensing of Environment, 2008,112(12):333-4343.

[34] Garrigues S, Lacaze R, Baret F, et al. Validation and intercomparison of global Leaf Area Index products derived from remote sensing data[J]. Journal of Geophysical Research- Biogeosciences, 2008,113(G2):1-20.

[35] Fang H, Wei S, Liang S. Validation of MODIS and CYCLOPES LAI products using global field measurement data[J]. Remote Sensing of Environment, 2012(119):43- 54.

[36] Liu Y, Liu R, Chen J M, et al. Expanding MISR LAI to high temporal resolution with MODIS observations[J]. IEEE Transaction on Geoscience and Remote Sensing, 2012,50(10):3915-3927.

[37] Ganguly S, Samanta A, Schull MA, et al. Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation, analysis and validation[J]. Remote Sensing of Environment, 2008,112(12): 4318-4332.

[38] Liu R G, Liu Y. Generation of new cloud masks from MODIS land surface reflectance products[J]. Remote Sensing of Environment, 2013(133):21-37.

[39] Liu R G, Chen J M, Liu J, et al. Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research[J]. Journal of Environmental Management, 2007,85(3):649-658.

[40] Xiao Z, Liang S, Wang J, J. et al. GLASS leaf area index product derived from MODIS time series remote sensing data[C]. The 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, July 22-27,2012.

[41] Pisek J, Chen J M. Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data[J]. Remote Sensing of Environment, 2009,113(11):2412-2423.

[42] Kobayashi H, Delbart N, Suzuki R, et al. A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest[J]. Journal of Geophysical Research-Biogeosciences, 2010(115):1-14.

[43] Zhao K G, Popescu S. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA [J]. Remote Sensing of Environment, 2009,113(8):1628- 1645.

[44] 周梦维,柳钦火,刘强,等.机载激光雷达的作物叶面积指数定量反演[J].农业工程学报,2011,27(4):207-213.

[45] Zheng G, Moskal L M, Kim S H. Retrieval of effective Leaf Area Index in heterogeneous forests with terrestrial laser scanning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(2):777-786.

[46] Delegido J, Verrelst J, Meza C M, et al. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems[J]. European Journal of Agronomy, 2013(46):42-52.

[47] Wang L J, Niu Z, Hou X H, et al. The study of LAI estimation using a new vegetation index based on CHRIS data[J]. Spectroscopy and Spectral Analysis, 2013,33(4): 1082-1086.

Outlines

/