Inversion of biophysical parameters of potato based on an active learning pool-based sampling strategy
文献类型： 外文期刊
作者： Ma, Yuanyuan 1 ; Song, Xiaoyu 1 ; Zhang, Jie 3 ; Pan, Di 1 ; Feng, Haikuan 1 ; Yang, Guijun 1 ; Qiu, Chunxia 2 ; Sun, Heguang 1 ; Zheng, Chunkai 1 ; Li, Pingping 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Rm A1008,Beijing Nongke Bldg,11 Shu Guang Hua Yuan, Beijing 100097, Peoples R China
2.Xian Univ Sci & Technol, Coll Geomat, Xian, Peoples R China
3.Nanjing Agr Univ, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing, Jiangsu, Peoples R China
4.Huazhong Agr Univ, Coll Informat, Wuhan 430070, Peoples R China
关键词： PROSAIL radiative transfer model; look-up table (LUT); active learning; hybrid methods; Euclidean distance-based diversity (EBD) algorithm
期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：2.6； 五年影响因子：2.9 ）
ISSN： 0143-1161
年卷期： 2025 年 46 卷 1 期
页码：
收录情况： SCI
摘要： Timely estimations of leaf chlorophyll content (LCC) and leaf area index (LAI) can provide critical information for potato field management. We employed a hybrid method that integrated machine learning with a radiative transfer model to estimate potato growth parameters. A look-up table was generated using the PROSAIL model, which was used as an unlabelled sample set. Measurements were taken from a potato field, and the data were labelled according to growth period and variety. Then, training samples for potato LCC, LAI, and canopy chlorophyll content (CCC) were selected from the simulated unlabelled sample set using the Euclidean distance-based diversity algorithm based on different labelled data sets. The training sample size required to accurately estimate the parameters varied considerably depending on the parameter, variety, and growth period, despite using the same labelled data set. Moreover, our results indicate that the growth period has a substantial impact on model accuracy and needs to be considered when constructing the labelled data set. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato parameter estimation, which provides a valid tool for accurately monitoring crop growth and growth health.
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