Plant potassium accumulation (PKA) plays an important role in evaluating the production capacity of crops. Remote sensing from unmanned aerial vehicles (UAVs) is increasingly used in precision agriculture, but relatively little is known about its use for determining the potassium (K) nutritional status of rice (Oryza sativa L.). This study compares the performance of different sets of spectral and textural indices derived from UAV data for estimating rice K nutritional status. A UAV equipped with three cameras (RGB, color-infrared (CIR) and multispectral cameras) was used to acquire imagery of rice canopies at different key growth stages. Immediately following the overflights, rice canopies were sampled for rice PKA. Regression models were then built to predict rice PKA with spectral and textural indices as predictor variables. Finally, stepwise multiple linear regression (SMLR) was used to determine if fusing spectral and textural indices significantly improved UAV-based PKA estimates. The renormalized difference vegetation index (RDVI) calculated from multispectral imagery proved to be the best predictor of rice PKA (R² = 0.72, RMSE = 5.42 g m⁻²), though spectral vegetation indices calculated from RGB and CIR imagery also showed moderately strong prediction capability (R² ≤ 0.56, RMSE ≥ 7.33 g m⁻²). Among the textural indices, the renormalized difference texture index [RDTI (MEA₈₀₀, MEA₆₈₀)] calculated by mean texture (MEA) of the multispectral imagery was the best predictor of rice PKA (R² = 0.74, RMSE = 5.57 g m⁻²) while textural indices calculated from the RGB and CIR imagery showed only weak relationships with rice PKA (R² ≤ 0.40, RMSE ≥ 8.11 g m⁻²). Fusing textural and spectral vegetation indices improved our ability to remotely sense PKA, with the SMLR model combination of RDTI (MEA₈₀₀, MEA₆₈₀) and spectral vegetation index DATT performing best (R² increased by 11.11% to 0.80 and RMSE decreased by 7.5% to 5.15 g m⁻²). Our findings suggest that spectral and textural indices derived from UAV data allow for accurate mapping of PKA and that fusion of the two further improves the accuracy.

植物钾积累（PKA）在评价作物生产能力方面起着重要作用。无人机 (UAV) 的遥感越来越多地用于精准农业，但对其用于确定水稻 ( Oryza sativa L.)钾 (K) 营养状况的了解相对较少。本研究比较了来自无人机数据的不同光谱和质地指数在估计水稻 K 营养状况方面的性能。一架配备三台相机（RGB、彩色红外 (CIR) 和多光谱相机）的无人机用于获取不同关键生长阶段的水稻冠层图像。飞越后立即对水稻冠层进行采样以获得水稻 PKA。然后建立回归模型以使用光谱和质地指数作为预测变量来预测水稻 PKA。最后，逐步多元线性回归 (SMLR) 用于确定融合光谱和纹理指数是否显着改善了基于 UAV 的 PKA 估计。从多光谱图像计算的重归一化差异植被指数 (RDVI) 被证明是水稻 PKA (R ² = 0.72，RMSE = 5.42 g m ^-2），尽管从 RGB 和 CIR 图像计算的光谱植被指数也显示出中等强的预测能力（R ²  ≤ 0.56，RMSE ≥ 7.33 g m ^-2）。在纹理指数中，通过多光谱图像的平均纹理（MEA）计算的重归一化差异纹理指数[RDTI（MEA ₈₀₀，MEA ₆₈₀）]是水稻PKA的最佳预测因子（R ²  = 0.74，RMSE = 5.57 g m ^-2 ) 而根据 RGB 和 CIR 图像计算的纹理指数仅显示与水稻 PKA 的微弱关系 (R ²  ≤ 0.40, RMSE ≥ 8.11 g m ^-2）。融合纹理和光谱植被指数提高了我们遥感PKA的能力，其中RDTI（MEA ₈₀₀，MEA ₆₈₀）和光谱植被指数DATT的SMLR模型组合表现最佳（R ²增加11.11％至0.80，RMSE下降7.5％至 5.15 克^-2 )。我们的研究结果表明，来自无人机数据的光谱和纹理指数可以准确映射 PKA，并且两者的融合进一步提高了准确性。