Simulation of regional rice growth by combination remote sensing data and crop model

Jianmao Guo; Yanghua Gao; Junwei Liu; Dunyue Fei; Qian Wang

doi:10.1117/12.2068302

8 November 2014 Simulation of regional rice growth by combination remote sensing data and crop model

Jianmao Guo, Yanghua Gao, Junwei Liu, Dunyue Fei, Qian Wang

Proceedings Volume 9260, Land Surface Remote Sensing II; 92602J (2014) https://doi.org/10.1117/12.2068302
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Remote sensing monitoring the macroscopic vegetation situation and reflecting environmental factors influence the results and the process of crops; Crop growth simulation model using environmental factors simulate the process of crop growth, revealing the cause and essence of the process, both of them have advantages and disadvantages. Thus developing the study of combine remote sensing yield estimation and dynamic crop growth model is essential, it is a significant scientific issue studying the approach and method which can combine these two advanced technologies. In this paper, using multi-temporal remote sensing information and crop model ORYZA2000 combined method realizing the rice growth simulation in pixel scale, after the comparison between simulated result and the actual statistic value, accuracy is high and result is good. The combination of remote sensing information and crop simulation model is a complex issue, its result will be affected by many factors, combined with the field test in this study is a simplification of the actual situation, this will certainly affect the result’s accuracy.. This method has great practical significance and at the same time has positive application prospect. It can be used to monitor and evaluate crop growth condition, forecast crop yield and so on, thus can be used in decision support service on different regional scales and guiding agricultural production.

Citation Download Citation

Jianmao Guo, Yanghua Gao, Junwei Liu, Dunyue Fei, and Qian Wang "Simulation of regional rice growth by combination remote sensing data and crop model", Proc. SPIE 9260, Land Surface Remote Sensing II, 92602J (8 November 2014); https://doi.org/10.1117/12.2068302

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KEYWORDS

Remote sensing

Data modeling

Solar radiation models

Reflectivity

Vegetation

Radiative transfer

Satellites

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