文献类型： 外文期刊

作者： Pan, Di ¹ ; Li, Changchun ¹ ; Yang, Guijun ² ; Ren, Pengting ² ; Ma, Yuanyuan ² ; Chen, Weinan ² ; Feng, Haikuan ² ; Chen, Riqiang ² ; Chen, Xin ² ; Li, Heli ² ;

作者机构： 1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Peoples R China

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

3.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China

关键词： soybean; multispectral; initial anthesis (IA(DAS)); red edge chlorophyll index (CIred edge); phenology curve

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 22 期

页码：

收录情况： SCI

摘要： Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IA(DAS)) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IA(DAS), including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IA(DAS) of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IA(DAS), in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IA(DAS) of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IA(DAS) identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R-2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IA(DAS), with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IA(DAS) was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IA(DAS) of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype-environment interactions. Overall, this study demonstrated that the IA(DAS) of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.