Abstract
It is very important to obtain continuous regional crop parameters efficiently in the agricultural field. However, remote sensing data can provide spatial-continuous / temporal-disperse crop information while crop growth model can provide temporal-continuous / spatial-disperse crop information. Therefore, the assimilation between crop growth model and remote sensing data is an efficient way for obtaining continuous vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images, were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model.
Highlights
Data assimilation, which is of momentous theoretical and application values, has been successfully applied to numerous fields, such as atmosphere [1-3], weather [4-6], agriculture [7-9], ocean [10-12], land surface [13-15] and hydrology [16-18]
We regarded the discrepancy (C) between each rice pixel’s measured LAI (MLAI) retrieved from satellite imagery and simulative LAI (SLAI) calculated with the World Food Studies (WOFOST) model as the standard for measuring the assimilation effect, which is calculated in Eq (2)
We evaluated the assimilation precision in three cases: a) SLAIe, calculated from the WOFOST model that sets experiential SPAN (50) as the input parameter; b) SLAIc, calculated from the WOFOST model that sets the optimized SPAN of the assimilation implemented on the CPU as the input parameter; and c) SLAIg, calculated from the WOFOST model that sets the optimized SPAN of the assimilation implemented on the graphic processing unit (GPU) as the input parameter
Summary
Data assimilation, which is of momentous theoretical and application values, has been successfully applied to numerous fields, such as atmosphere [1-3], weather [4-6], agriculture [7-9], ocean [10-12], land surface [13-15] and hydrology [16-18]. Assimilating remote sensing (RS) information into a crop model can provide continuous regional plant growth information and has been applied to a wide range of agricultural fields in crop growth assessment, agricultural environmental control and farmland management decision [19-23]. On the basis of various RS data, crop growth models and assimilation algorithms, researchers have conducted numerous studies on the different applications of assimilating RS information into crop models [20, 22, 25-27]. Huang [28] conducted an experiment on regional winter wheat yield forecasting with moderate-resolution imaging spectroradiometer-leaf area index (MODIS-LAI) products on the basis of assimilation between the World Food Studies (WOFOST) model [29-32] and ensemble Kalman algorithm method [33-36].
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