Inter-Comparison and Validation of the FY-3A/MERSI LAI Product Over Mainland China

Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval.

[1]  Clement Atzberger,et al.  Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based LAI Estimation of Wheat, Sugar Beet, and Maize , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Steven W. Running,et al.  Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products , 2003 .

[3]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[4]  N. Coops,et al.  Estimation of potential forest productivity across the Oregon transect using satellite data and monthly weather records , 2001 .

[5]  Ronggao Liu,et al.  Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research. , 2007, Journal of environmental management.

[6]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[7]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[8]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[9]  F. Baret,et al.  GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products , 2013 .

[10]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

[11]  Christopher Justice,et al.  Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Jing M. Chen,et al.  Improved LAI Algorithm Implementation to MODIS Data by Incorporating Background, Topography, and Foliage Clumping Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[13]  D. E. Harrison,et al.  Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update) , 2010 .

[14]  Jingfeng Huang,et al.  Evaluation of MODIS surface reflectance products for wheat leaf area index (LAI) retrieval , 2008 .

[15]  Yanlian Zhou,et al.  Retrieving leaf area index of forests in red soil hilly region using remote sensing data , 2009, International Conference on Earth Observation for Global Changes.

[16]  Darrel L. Williams,et al.  BOREAS in 1997: Experiment overview, scientific results, and future directions , 1997 .

[17]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[18]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[19]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[20]  Sylvain G. Leblanc,et al.  Multiple-scattering scheme useful for geometric optical modeling , 2001, IEEE Trans. Geosci. Remote. Sens..

[21]  Fumin Wang,et al.  New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice , 2007 .

[22]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[23]  Jing M. Chen,et al.  Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[24]  G. Bonan.  Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[25]  G. Bonan.  Land-Atmosphere interactions for climate system Models: coupling biophysical, biogeochemical, and ecosystem dynamical processes , 1995 .

[26]  Zhou Yanlian.  Retrieval of leaf area index for different grasslands in Inner Mongolia prairie using remote sensing data , 2011 .

[27]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[28]  Weimin Ju,et al.  Spatial and temporal variations of forest LAI in China during 2000–2010 , 2012 .

[29]  Y. Knyazikhin,et al.  Validation and intercomparison of global Leaf Area Index products derived from remote sensing data , 2008 .

[30]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[31]  B. Duchemin,et al.  VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products , 2004 .

[32]  Ji Jinjun.  The spatial-temporal distribution of leaf area index in China: a comparison between ecosystem modeling and remote sensing reversion , 2010 .

[33]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[34]  Antonio Di Gregorio,et al.  Land cover classification system (LCCS): classification concepts and user manual for software version 1.0 , 2000 .

[35]  Eric F. Vermote,et al.  Correction of MODIS surface reflectance time series for BRDF effects , 2012 .

[36]  Jun Yang,et al.  An Overview of a New Chinese Weather Satellite FY-3A , 2009 .

[37]  R. Nemani,et al.  Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument , 2005 .

[38]  S. Leblanc,et al.  Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .

[39]  Sylvain G. Leblanc,et al.  A four-scale bidirectional reflectance model based on canopy architecture , 1997, IEEE Trans. Geosci. Remote. Sens..

[40]  J. Chen.  Spatial Scaling of a Remotely Sensed Surface Parameter by Contexture , 1999 .

[41]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.