基于遥感和作物生长模型的多尺度冬小麦估产研究

doi:10.3964/j.issn.1000-0593(2021)07-2205-07

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Abstract Food security is a guarantee for social harmony, political stability and sustainable development of the economy. However, current research on crop yield estimation is mostly regional and empirical, relying too much on ground-measured data. Scalable Crop Yield Mapping (SCYM） is a satellite data based framework for estimating crop yield.It can be quickly applied to the estimated yield of different spatial scales and different types of crops without relying on measured data. This framework provides an important theoretical basis for multi-scale crop yield estimation research. We took the winter wheat of Anhui Province from 2012 to 2018 as the study object. Firstly, the sensitive parameters determined by the predecessors and their fluctuation ranges in the study area are summarized. Combined with a large amount of site data, the parameters optimization of the WOFOST model was completed. Secondly, random forest models were established based on the simulated yield, simulated leaf area index (LAI) at different periods, and selected meteorological indicators. Finally, the MODIS-LAI under the best observation date combination replaced the simulated LAI for the corresponding time periods to estimate the winter wheat yield in Anhui Province. The main outcomes in this study are as follows: (1) The overall correlation between the estimated outputs and the measured data of the stations is 0.758 (R² is 0.575), and the RMSE is 790.92 kg·ha^-1. The sites with higher production accuracy are mainly distributed in the Huaibei Plain (<1%), while the areas with high errors are concentrated in the hilly areas of southern Anhui (>40%). (2) The winter wheat yield in Anhui Province from 2012 to 2018 was estimated by SCYM. According to the spatial distribution of the 7-year average yield estimation, the yield is gradually decreasing from north to south. The high-value areas are located in the Huaibei Plain in northern Anhui, and the low-value areas are distributed in central Anhui and southern Anhui. (3) The average measured yield from 2012 to 2018 is 6 058.00 kg·ha^-1, while the average yield of the SCYM is 5 984.95 kg·ha^-1. The correlation between them in the interannual time series is 0.822, and the RMSE is 189.96 kg·ha^-1. In seven years, the relative error each year does not exceed 6%. This study shows that the SCYM framework is feasible for estimating winter wheat yield in Anhui Province and has a good effect on yield forecast. This method can improve the regionality and empiricism of the previous crop yield estimation models to a certain extent. Meanwhile, it also solves the shortcomings of computationally intensive methods, which are costly and difficult to popularize. Thus, SCYM has great potential in applying of regional scales, and it will provide an extremely important theoretical basis and practical value for agricultural production in the future.

Key words：Remote sensing; Yield estimation; WOFOST; Winter wheat; Anhui Province

Received: 2020-07-09 Accepted: 2020-11-20

ZTFLH:

TP79

Corresponding Authors: ZHAO Wei-qing E-mail: iwqzhao@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)07-2205-07 OR https://www.gpxygpfx.com/EN/Y2021/V41/I07/2205