GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production

REMOTE SENSING OF ENVIRONMENT

卷:137

Article

Baret, F.¹  Weiss, M.¹  Lacaze, R.²  Camacho, F.³  Makhmara, H.⁴  Pacholcyzk, P.⁴  Smets, B.⁵ 

[1] INRA, EMMAH UMR 1114, Avignon, France

[2] HYGEOS, Toulouse, France

[3] EOLAB, Valencia, Spain

[4] CNES, Toulouse, France

[5] Vlaamse Instelling Technol Onderzoek, B-2400 Mol, Belgium

关键词: Data fusion;    FAPAR;    FCOVER;    Global products;    LAI;    MODIS;    Uncertainty;    VEGETATION;   

DOI  :  10.1016/j.rse.2012.12.027

来源: Elsevier

【 摘 要 】

Essential climate variables such as LAI or FAPAR are required for the monitoring, understanding and modeling of land surfaces at the global scale. While several products were already developed from the current medium resolution sensors, the few validation exercises currently achieved highlighted significant discrepancies and inconsistencies. The objective of this study is to develop improved global estimates of LAI, FAPAR and FCOVER variables by capitalizing on the development and validation of already existing products. In a first step, the performances of the MODIS, CYCLOPES, GLOBCARBON and JRC-FAPAR products were reviewed. The MODIS and CYCLOPES products were then selected since they provide higher level of consistency. These products were fused to generate the improved LAI, FAPAR and FCOVER values that were later scaled to closely match their expected range of variation. Finally, neural networks were trained to estimate these fused and scaled products from SPOT-VEGETATION top of canopy directionally normalized reflectance values. The resulting GEOV1 products are associated to quality control flags as well as quantitative estimates of uncertainties. Performances of the GEOV1 products are finally evaluated in a companion paper. The GEOV1 products are freely available to the community at www.geoland2.eu from 1999 up to present, globally at 1/112 degrees spatial sampling grid at the dekadal time step. (c) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free