Arid
DOI10.1016/j.rse.2007.03.030
Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and sigma(0) satellite data
Mangiarotti, S.1; Mazzega, P.2; Jarlan, L.1,3; Mougin, E.1; Baup, F.1,4; Demarty, J.5
通讯作者Mangiarotti, S.
来源期刊REMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
出版年2008
卷号112期号:4页码:1365-1380
英文摘要

Satellite radar backscattering coefficient sigma(0) data from ENVISAT-ASAR and Normalized Difference Vegetation Index (NDVI) data from SPOT-VEGETATION are assimilated in the STEP model of vegetation dynamics. The STEP model is coupled with a radiative transfer model of the radar backscattering and NDVI signatures of the soil and herbaceous vegetation. These models are driven by field data (rainfall time series, soil properties, etc.). While some model parameters have fixed values, some other parameters have target values to be optimized. The study focuses on a well documented 1 km(2) homogeneous area in a semi-arid region (Gourma, Mali).


We here investigate whether departures between model predictions and the corresponding data result from field data errors, in situ data lack of representativeness or some model shortcomings. For this purpose we introduce an evolutionary strategy (ES) approach relying on a bi-objective function to be minimized in the data assimilation/inversion process. Several numerical experiments are conducted, in various mono-objective and bi-objective modes, and the performances of the model predictions compared in terms of NDVI, backscattering coefficient, leaf area index (LAI) and biomass.


It is shown that the bi-objective ES leads to improved model predictions and also to a better readability of the results by exploring the Pareto front of optimal and admissible solutions. It is also shown that the information brought from the optical sensor and the radar is coherent; that the corresponding radiative transfer models are also coherent; that the representativeness of in situ data can be compared to satellite data through the modeling process. However some systematic biases on the biomass predictions (errors in the range 140 to 300 kg ha(-1)) are observed. Thanks to the bi-objective ES, we are able to identify some likely shortcoming in the vegetation dynamics model relating the LAI to the biomass variables. (C) 2007 Elsevier Inc. All rights reserved.


英文关键词assimilation multi-objective optimization Pareto vegetation dynamics model evolutionary strategy NDVI radar backscattering coefficient
类型Article
语种英语
国家France ; England
收录类别SCI-E
WOS记录号WOS:000254961500010
WOS关键词SAHELIAN GRASSLAND MODEL ; GENETIC ALGORITHM ; INVERSION TECHNIQUE ; SCATTEROMETER DATA ; SCATTERING MODEL ; REFLECTANCE ; SURFACE ; VALIDATION ; PARAMETERS ; MALI
WOS类目Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构French National Research Institute for Sustainable Development
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/159039
作者单位1.UPS, CNRS CNES, Ctr Etude Spatial Biosphere, Observ Midi Pyrenees, F-31401 Toulouse, France;
2.Univ Toulouse 3, CNRS IRD, Lab Meca & Transferts Geol, Observ Midi Pyrenees, F-31400 Toulouse, France;
3.European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England;
4.Univ Toulouse 3, UPS, F-31062 Toulouse, France;
5.Lab Sci Climat & Environm Vallee, F-91198 Gif Sur Yvette, France
推荐引用方式
GB/T 7714
Mangiarotti, S.,Mazzega, P.,Jarlan, L.,et al. Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and sigma(0) satellite data[J]. French National Research Institute for Sustainable Development,2008,112(4):1365-1380.
APA Mangiarotti, S.,Mazzega, P.,Jarlan, L.,Mougin, E.,Baup, F.,&Demarty, J..(2008).Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and sigma(0) satellite data.REMOTE SENSING OF ENVIRONMENT,112(4),1365-1380.
MLA Mangiarotti, S.,et al."Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and sigma(0) satellite data".REMOTE SENSING OF ENVIRONMENT 112.4(2008):1365-1380.
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