文献类型： 外文期刊

作者： Pan, Yuanyuan ¹ ; Wu, Wenxuan ¹ ; He, Jiaoyang ¹ ; Zhu, Jie ¹ ; Su, Xi ¹ ; Li, Wanyu ¹ ; Li, Dong ¹ ; Yao, Xia ¹ ; Cheng, Tao ¹ ; Zhu, Yan ¹ ; Cao, Weixing ¹ ; Tian, Yongchao ¹ ;

作者机构： 1.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, Minist Agr & Rural Affairs,Key Lab Crop Syst Anal, Jiangsu Key Lab Informat Agr,Natl Engn & Technol C, Nanjing 210095, Peoples R China

2.Jiangsu Acad Agr Sci, Wuxi Branch, Wuxi 214174, Peoples R China

关键词： Multi-FVC model; Multi-angle remote sensing; Soil background, PROSAIL; Gaussian process regression

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 222 卷

页码：

收录情况： SCI

摘要： Fractional vegetation cover (FVC) plays an important role in spectral unmixing, crop growth monitoring, crop light interception calculation, and yield estimation. However, the spectral reflectance would change with view zenith angles (VZAs), and retrieved FVC is also affected by VZAs. Therefore, in this paper, the observed multiangular (+45 degrees, degrees , +30 degrees, degrees , 0 degrees) degrees ) spectral datasets with different crops (wheat and rice), along with simulated spectral dataset, were used to explore method of correcting the influence of angle effect in FVC retrieval. Firstly, a simulated dataset of multi-angular hyperspectral data and directional FVC were constructed using the PROSAIL model, and a single-angular FVC retrieval model (Sin-FVC) was established based on the Gaussian process regression (GPR) algorithm. Then, the single-angular FVC (i.e., FVCB) B ) was converted into vertical FVC (FVCBv) B v ) based on the Beer-Lambert law. Finally, a Multi-FVC model was developed, which summed outputs of the SinFVC model (FVC0 degrees, 0 degrees , FVCBv) B v ) according to their respective weights (i.e., FVC correct ), to correct the angle effect. Using the pixel bisection model (FVCVI) VI ) as a comparison, different FVC retrieval models (Sin-FVC, Multi-FVC, and FVCVI) VI ) were compared and evaluated in terms of FVC retrieval accuracy, ability in weakening soil background, and LNC retrieval accuracy after spectral decomposition using different retrieved FVC (FVC0 degrees, 0 degrees , FVC-30 degrees v,-30 degrees v , FVC-45 degrees v,-45 degrees v , FVC correct ). The results showed that FVC correct had the highest FVC retrieval accuracy (RRMSE = 8.5 %) compared with FVC retrieved from other models (FVCVI, VI , FVC0 degrees, 0 degrees , FVC-30 degrees v and FVC-45 degrees v).-45 degrees v ). Meanwhile, when using the reflectance at NIR band in the black soil background as the baseline, after decoupling mixing spectra based on FVC correct , the relative offset (i.e., RO correct ) of each vegetation index (NDVI, EVI, SAVI, OSAVI) was minimum (least-ROcorrect- RO correct = 0.42 %). That is, the soil background was effectively suppressed by FVC correct spectral decomposition, along with the highest LNC retrieval accuracy, with an RRMSE of 14.2 %.