DOI: 10.1016/j.jag.2015.02.003
Scopus记录号: 2-s2.0-84943649840
论文题名: Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2
作者: Zurita-Milla R ; , Laurent V ; C ; E ; , van Gijsel J ; A ; E
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 43 起始页码: 7
结束页码: 18
语种: 英语
英文关键词: Model inversion
; Radiative transfer modelling
; Self-organizing map
; SLC
; variables
; Vegetation biophysical and biochemical
Scopus关键词: algorithm
; artificial neural network
; data inversion
; forest canopy
; numerical model
; radiative transfer
; Sentinel
; unsupervised classification
英文摘要: Monitoring biophysical and biochemical vegetation variables in space and time is key to understand the earth system. Operational approaches using remote sensing imagery rely on the inversion of radiative transfer models, which describe the interactions between light and vegetation canopies. The inversion required to estimate vegetation variables is, however, an ill-posed problem because of variable compensation effects that can cause different combinations of soil and canopy variables to yield extremely similar spectral responses. In this contribution, we present a novel approach to visualise the ill-posed problem using self-organizing maps (SOM), which are a type of unsupervised neural network. The approach is demonstrated with simulations for Sentinel-2 data (13 bands) made with the Soil-Leaf-Canopy (SLC) radiative transfer model. A look-up table of 100,000 entries was built by randomly sampling 14 SLC model input variables between their minimum and maximum allowed values while using both a dark and a bright soil. The Sentinel-2 spectral simulations were used to train a SOM of 200 × 125 neurons. The training projected similar spectral signatures onto either the same, or contiguous, neuron(s). Tracing back the inputs that generated each spectral signature, we created a 200 × 125 map for each of the SLC variables. The lack of spatial patterns and the variability in these maps indicate ill-posed situations, where similar spectral signatures correspond to different canopy variables. For Sentinel-2, our results showed that leaf area index, crown cover and leaf chlorophyll, water and brown pigment content are less confused in the inversion than variables with noisier maps like fraction of brown canopy area, leaf dry matter content and the PROSPECT mesophyll parameter. This study supports both educational and on-going research activities on inversion algorithms and might be useful to evaluate the uncertainties of retrieved canopy biophysical and biochemical state variables. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79553
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, Enschede, Netherlands; UMR TETIS, Irstea, 500 rue J.F. Breton, Montpellier Cedex 5, France; Royal Netherlands Meteorological Institute (KNMI), PO Box 201, De Bilt, Netherlands
Recommended Citation:
Zurita-Milla R,, Laurent V,C,et al. Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,43