文献类型： 外文期刊

作者： Liu Yang ¹ ; Feng Hai-kuan ¹ ; Huang Jue ² ; Yang Fu-qin ⁵ ; Wu Zhi-chao ¹ ; Sun Qian ¹ ; Yang Gui-jun ¹ ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

2.Shandong Univ Sci & Technol, Coll Surveying Sci & Engn, Qingdao 266590, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China

5.Henan Univ Engn, Coll Civil Engn, Zhengzhou 451191, Peoples R China

关键词： Potato; Above-ground biomass; Hyperspectral characteristic parameter; Green edge parameter; Plant height

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.452； 五年影响因子：0.401 ）

ISSN： 1000-0593

年卷期： 2021 年 41 卷 3 期

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) is an important index to evaluate crop growth and yield estimation, and plays an important role in guiding agricultural management. Therefore, the rapid and accurate acquisition of biomass information is of great significance for monitoring the growth status of potato and improving the yield. The hyperspectral images, measured plant height (H), above-ground biomass and three-dimensional coordinates of ground control point (GCP) were obtained in budding potato period, tuber formation period, tuber growth period, starch accumulation period and mature period. Firstly, based on UAV hyperspectral image and GCP to generate the DSM of the experimental field, the plant height ( H-dsm) of potato was extracted by DSM. Then the first-order differential spectrum, vegetation index and green edge parameters are calculated using UAV hyperspectral images. Furthermore, the correlation between hyperspectral characteristic parameter (HCPs) green edge parameter (GEPs) and potato AGB was analyzed. The first seven hyperspectral characteristic parameters and the optimal green edge parameter (OGEPs) with good correlation with AGB were selected for each growth period. Finally, the AGB of different growth period was estimated by partial least square regression (PLSR) and random forest (RF) based on the combination of HCPs, HCPs and OGEPs, HCPs and OGEPs and H-dsm. The results show that: (1) the H-dsm, is highly fitted to H (R-2 = 0. 84, RMSE = 6. 85 cm, NRMSE = 15. 67%). (2) The optimal green edge parameters obtained in each growth period are not completely the same. The OGEPs of the budding period, the tuber growth period and the starch accumulation period are R-sum, and the OGEPs of the tuber formation period and the mature period are Dr(min) and SDr, respectively. (3) Compared with HCPs, the accuracy of AGB estimation could be improved by adding OGEPs to HCPs, OGEPs and H-dsm to HCPs at different growth period of potato, and the latter improved the accuracy more greatly. (4) The R-2 of AGB modeling and verification estimated by PLSR and RF showed an upward trend from budding period to tuber growth period and then began to decrease. On the whole, R-2 decreased after increased. The estimation of AGB by PLSR is better than RF in each growth period, among which the AGB estimation of tuber growth period was the best. Therefore, the estimation accuracy of potato AGB can be improved by combining the OGEPs and plant height in HCPs and using PLSR method.