Rapid and accurate estimation of plant potassium accumulation (PKA) using hyperspectral remote sensing is of significance for the precise management of crop K fertilizer. This study focused on the separation of non-negative matrix factorization (NMF) for hyperspectral reflectance from the ground and unmanned aerial vehicle (UAV) platforms and its mitigation effect on the water and soil background. Pure vegetation spectra were extracted from the canopy mixed spectra using NMF, and then a partial least-squares regression (PLSR) model was established based on the extracted vegetation spectra and rice PKA to construct an estimation model of rice PKA. The results showed that the green light and red edge bands contributed significantly to the rice PKA estimation. NMF could effectively extract pure vegetation and water and soil spectra from mixed spectra, and enhance the green peak, red valley, and red edge information of the extracted vegetation spectra. Compared with spectral indices, the PLSR performed best for ground and UAV data. Besides, the R 2 of the PLSR model based on NMF-extracted vegetation spectra increased by 15.15% to 0.76%, and the verified RMSE and RE decreased by 16.93% and 16.77% to 3.19 g m −2 and 45.07%, respectively. Hyperspectral dataset testing from different years, growth stages and varieties, and UAV platforms showed that NMF could improve the estimation accuracy of rice PKA. This study showed that NMF could be applied to both ground and UAV hyperspectral platforms to improve the estimation accuracy of rice K nutrition.

中文翻译：

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基于非负矩阵分解的高光谱反射估算水稻钾素积累

利用高光谱遥感快速准确地估算植物钾素积累（PKA）对于作物钾肥的精准管理具有重要意义。本研究的重点是从地面和无人机 (UAV) 平台分离高光谱反射的非负矩阵分解 (NMF) 及其对水和土壤背景的缓解作用。利用NMF从冠层混合光谱中提取纯植被光谱，然后基于提取的植被光谱和水稻PKA建立偏最小二乘回归（PLSR）模型，构建水稻PKA估计模型。结果表明，绿光和红边带对水稻 PKA 估计有显着贡献。NMF可以有效地从混合光谱中提取纯植被和水土光谱，并增强提取的植被光谱的绿峰、红谷和红边信息。与光谱指数相比，PLSR 在地面和无人机数据方面表现最好。此外，基于NMF提取植被光谱的PLSR模型的R 2 增加了15.15%至0.76%，验证的RMSE和RE分别下降了16.93%和16.77%至3.19 g m -2 和45.07%。不同年份、不同生长阶段和品种以及无人机平台的高光谱数据集测试表明，NMF 可以提高水稻 PKA 的估计精度。本研究表明，NMF 可应用于地面和无人机高光谱平台，以提高水稻 K 营养的估计精度。PLSR 对地面和无人机数据表现最佳。此外，基于NMF提取植被光谱的PLSR模型的R 2 增加了15.15%至0.76%，验证的RMSE和RE分别下降了16.93%和16.77%至3.19 g m -2 和45.07%。不同年份、不同生长阶段和品种以及无人机平台的高光谱数据集测试表明，NMF 可以提高水稻 PKA 的估计精度。本研究表明，NMF 可应用于地面和无人机高光谱平台，以提高水稻 K 营养的估计精度。PLSR 对地面和无人机数据表现最佳。此外，基于NMF提取植被光谱的PLSR模型的R 2 增加15.15%至0.76%，验证的RMSE和RE分别下降16.93%和16.77%至3.19 g m -2 和45.07%。不同年份、不同生长阶段和品种以及无人机平台的高光谱数据集测试表明，NMF 可以提高水稻 PKA 的估计精度。本研究表明，NMF 可应用于地面和无人机高光谱平台，以提高水稻 K 营养的估计精度。生长阶段和品种，无人机平台表明 NMF 可以提高水稻 PKA 的估计精度。本研究表明，NMF 可应用于地面和无人机高光谱平台，以提高水稻 K 营养的估计精度。生长阶段和品种，无人机平台表明 NMF 可以提高水稻 PKA 的估计精度。本研究表明，NMF 可应用于地面和无人机高光谱平台，以提高水稻 K 营养的估计精度。