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Transfer-Learning-Based Approach for Yield Prediction of Winter Wheat from Planet Data and SAFY Model

文献类型： 外文期刊

作者： Zhao, Yu 1 ; Han, Shaoyu 2 ; Meng, Yang 1 ; Feng, Haikuan 1 ; Li, Zhenhai 2 ; Chen, Jingli 4 ; Song, Xiaoyu 2 ; Zhu, Yan 1 ; Yang, Guijun 1 ;

作者机构： 1.Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, Nanjing 210095, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China

4.Qingdao Hengxing Univ Sci & Technol, Coll Agr, Qingdao 266100, Peoples R China

关键词： yield; satellite remote sensing; crop growth model; SAFY; transfer learning; aboveground dry biomass

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 21 期

页码：

收录情况： SCI

摘要： Crop production is one of the major interactions between humans and the natural environment, in the process, carbon is translocated cyclically inside the ecosystem. Data assimilation algorithm has advantages in mechanism and robustness in yield estimation, however, the computational efficiency is still a major obstacle for widespread application. To address the issue, a novel hybrid method based on the combination of the Crop Biomass Algorithm of Wheat (CBA-Wheat) to the Simple Algorithm For Yield (SAFY) model and the transfer learning method was proposed in this paper, which enables winter wheat yield estimation with acceptable accuracy and calculation efficiency. The transfer learning techniques learn the knowledge from the SAFY model and then use the knowledge to predict wheat yield. The main results showed that: (1) The comparison using CBA-Wheat between measured AGB and predicted AGB all reveal a good correlation with R-2 of 0.83 and RMSE of 1.91 t ha(-1), respectively; (2) The performance of yield prediction was as follows: transfer learning method (R-2 of 0.64, RMSE of 1.05 t ha(-1)) and data assimilation (R-2 of 0.64, RMSE of 1.01 t ha(-1)). At the farm scale, the two yield estimation models are still similar in performance with RMSE of 1.33 t ha(-1) for data assimilation and 1.13 t ha(-1) for transfer learning; (3) The time consumption of transfer learning with complete simulation data set is significantly lower than that of the other two yield estimation tests. The number of pixels to be simulated was about 16,000, and the computational efficiency of the data assimilation algorithm and transfer learning without complete simulation datasets. The transfer learning model shows great potential in improving the efficiency of production estimation.

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