Precision Estimation of Rice Nitrogen Fertilizer Topdressing According to the Nitrogen Nutrition Index Using UAV Multi-Spectral Remote Sensing: A Case Study in Southwest China

作者：Lijuan Wang, Qi Ling, Zhan Liu, Mingzhu Dai, Yu Zhou, Xiaojun Shi, Jie Wang · 发表于：Plants · 年份：2025 · DOI：10.3390/plants14081195 · 被引用次数：4 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

The precision estimation of N fertilizer application according to the nitrogen nutrition index (NNI) using unmanned aerial vehicle (UAV) multi-spectral measurements remains to be tested in different rice cultivars and planting areas. Therefore, two field experiments were conducted using varied N rates (0, 60, 120, 160, and 200 kg N ha−1) on two rice cultivars, Yunjing37 (YJ-37, Oryza sativa subsp. Japonica Kato., the Institute of Food Crops at the Yunnan Academy of Agricultural Sciences, Kunming, China) and Jiyou6135 (JY-6135, Oryza sativa subsp. indica Kato., Hunan Longping Gaoke Nongping seed industry Co., Ltd., Changsha, China), in southwest China. The rice canopy spectral images were measured by the UAV’s multi-spectral remote sensing at three growing stages. The NNI was calculated based on the critical N (Nc) dilution curve. A random forest model integrating multi-vegetation indices established the NNI inversion, facilitating precise N topdressing through a linear platform of NNI-Relative Yield and the remote sensing NNI-based N balance approaches. The Nc dilution curve calibrated with aboveground dry matter demonstrated the highest accuracy (R2 = 0.93, 0.97 for shoot components in cultivars YJ-37 and JY-6135), outperforming stem (R2 = 0.70, 0.76) and leaf (R2 = 0.80, 0.89) based models. The RF combined with six vegetation index combinations was found to be the best predictor of NNI at each growing period (YJ-37: R2 is 0.70–0.97, RMSE is 0.02~0.04; JY-6135: R2 is 0.71–0....