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Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning

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Abstract

Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the values simulated by the coupled model were 0.92, 1.12 g m−2, and 409.70 kg ha−1, respectively, which were obtained when the soil-adjusted vegetation index was used as a partitioning zone and assimilating parameter. These results demonstrated that the coupled model of the crop model and RS data, based on the simulation sub-zones had a good prediction accuracy. The results provide important technical support for increasing model efficiency, when crop models need to be applied at the spatial scale.

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Acknowledgements

This research was supported by the National 863 High-tech Program (2013AA102301), the National Natural Science Foundation of China (31371535), the Jiangsu Collaborative Innovation Center for Modern Crop Production and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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  1. National Engineering and Technology Center for Information Agricultural University, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, 210095, Jiangsu, People’s Republic of China

    Caili Guo, Ling Zhang, Xiang Zhou, Yan Zhu, Weixing Cao, Xiaolei Qiu, Tao Cheng & Yongchao Tian

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  1. Caili Guo
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  2. Ling Zhang
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  3. Xiang Zhou
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  4. Yan Zhu
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Correspondence to Yongchao Tian.

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Guo, C., Zhang, L., Zhou, X. et al. Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning. Precision Agric 19, 55–78 (2018). https://doi.org/10.1007/s11119-017-9498-5

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