Paper
22 October 2010 The review of dynamic monitoring technology for crop growth
Hong-wei Zhang, Huai-liang Chen, Chun-hui Zou, Wei-dong Yu
Author Affiliations +
Abstract
In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong-wei Zhang, Huai-liang Chen, Chun-hui Zou, and Wei-dong Yu "The review of dynamic monitoring technology for crop growth", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78242L (22 October 2010); https://doi.org/10.1117/12.864811
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Cited by 3 scholarly publications.
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KEYWORDS
Data modeling

Remote sensing

Vegetation

Systems modeling

Atmospheric modeling

Environmental sensing

Satellites

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