Abstract
This paper puts forward a novel approach for model inversion of leaf area index (LAI) of vegetation based on the integrated arithmetic of data assimilation and genetic-particle swarm algorithm (DAGS). The article expounds the design principle of electromagenetic wave radiative transfer model (ERTM) for vegetation canopies. On this basis, this study constructs the inversion model of LAI based on DAGS. Furthermore, this experiment realizes the model inversion of LAI with the aid of Remote Sensing (RS) multi-spectral data and biophysical component data of vegetation canopies, which are provided by the multispectral RS observation data set (MOD15A2). The bullet points of the text are summarized as follows. (1) The contribution proposes DAGS for LAI inversion. (2) The article discusses ERTM model for electromagenetic wave radiative transfer mechanism of vegetation canopies. (3) This text achieves LAI inversion with the help of RS multi-spectral data and biophysical component data of vegetation canopies supplied by MOD15A2. The experimental results demonstrate the validity and reliability of the model inversion of LAIby making use of DAGS. The proposed algorithm exploits a novel algorithmic pathway for the model inversion of LAI by means of RS multi-spectral data and biophysical component data of vegetation canopies.
Highlights
Leaf area index (LAI) is an important biophysical parameter of vegetation canopies, and it is generally utilized to characterize the space-time distribution regularities of the earth's surface vegetation
By making use of data assimilation and genetic-particle swarm algorithm (DAGS), this paper simulates the spectral reflectance of vegetation canopies, and sets up the component parameter of vegetation to be retained round an initial value, and to achieve the model inversion of LAI
The morbid problem of the model inversion is still existent, and so far it is still the bottle-neck issue of the model inversion research. Since both the traditional retrieval approach of LAI as well as the inversion method based on a priori knowledge is correspondingly limited to the parameter inversion of a certain specific Remote Sensing (RS) observed moment, so the retrieval result is provided with greater uncertainty
Summary
Leaf area index (LAI) is an important biophysical parameter of vegetation canopies, and it is generally utilized to characterize the space-time distribution regularities of the earth's surface vegetation. The model inversion of LAI based on ERTM is an opposite problem, and it possesses the uncertainty, that is, it dissatisfies the existence of the solution, the uniqueness of the solution, and the continuous dependence which the solution acts on the observed data (Goward, et al 2008) This is caused by two main reasons below: [1] a different model parameter can produce almost the same reflective spectral. The one-dimensional assimilation is that the data obtained with the observation directly replaces the model predicted value that is most close to the observed data in accordance with the minimum distance algorithm Such update mode is defined as direct inserting of the observed data or direct updating of the forecast value. L is the model operator; s j is the j th observed parameter of L ; G j is the observed operator; D j is the covariance matrix of the error of the observation field
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