Assimilation of sparse crown by using GO and GORT model with remotely sensed data in the Tarim River Basin, Xinjiang, China

Guli Jiapaer; Xi Chen; Pieter Kempeneers; Cun Chang; Zhongguo Ma

doi:10.1117/12.737691

12 November 2007 Assimilation of sparse crown by using GO and GORT model with remotely sensed data in the Tarim River Basin, Xinjiang, China

Guli Jiapaer, Xi Chen, Pieter Kempeneers, Cun Chang, Zhongguo Ma

Author Affiliations +

Proceedings Volume 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII; 67492G (2007) https://doi.org/10.1117/12.737691
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

The sparse crown along both riversides of the Tarim River plays an important role in firming the sand and restraining the desertification. It is very difficult to obtain the spectrum information from the remotely sensed data because of the low percentage of coverage of the sparse vegetation, which affects the classification accuracy of the identification of ground objects and the extraction of vegetation biophysics. It is a key obstruction in developing the quantification of the RS technology. Taking the sparse vegetation at the Tarim River Basin as the research object, this paper predicts the surface bidirectional reflectance of the discontinuous plant canopies in the extremely arid based on the observed ground spectrum. Two different approaches are presented for the tree and the shrub. The first is to simulate the spectrum of the tree with the Geometric Optical-Radiative Transfer model based on ground observation. In the second approach,the spectral responses of sparse shrub and bare soil have been simulated using the linear Geometric Optical (GO) model. Comparing the simulated bidirectional reflectance with actual remote sensing data (EO-1), the spectral differences of these data are analyzed.

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Guli Jiapaer, Xi Chen, Pieter Kempeneers, Cun Chang, and Zhongguo Ma "Assimilation of sparse crown by using GO and GORT model with remotely sensed data in the Tarim River Basin, Xinjiang, China", Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 67492G (12 November 2007); https://doi.org/10.1117/12.737691

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KEYWORDS

Vegetation

Reflectivity

Solar radiation models

Geometrical optics

Data modeling

Short wave infrared radiation

Lithium

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