Study on ENVISAT ASAR data assimilation in rice growth model for yield estimation

Shenbin Yang; Shuanghe Shen; Bingbai Li; Bingxiang Tan

doi:10.1117/12.824488

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12 August 2009 Study on ENVISAT ASAR data assimilation in rice growth model for yield estimation

Shenbin Yang; Shuanghe Shen; Bingbai Li; Bingxiang Tan

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Proceedings Volume 7456, Atmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III; 74560Y (2009) https://doi.org/10.1117/12.824488
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

In this paper, a practical scheme for assimilation of multi-temporal and multi-polarization ENVISAT ASAR data in rice crop model to map rice yield has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the temporal single-polarized rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000. The assimilation method consists in re-initializing the model with optimal input parameters allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e. the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced finally. The scheme was applied over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production situation, with an accuracy of 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.

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Shenbin Yang, Shuanghe Shen, Bingbai Li, and Bingxiang Tan "Study on ENVISAT ASAR data assimilation in rice growth model for yield estimation", Proc. SPIE 7456, Atmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III, 74560Y (12 August 2009); https://doi.org/10.1117/12.824488