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A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle
摘要： Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.
关键词： UAV,multispectral imagery,radiative transfer model,LNC,vegetation index,non-parametric regression
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Power and Difference of the Up-and-Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Content in Wheat at the Leaf Scale
摘要： Leaf nitrogen content (LNC) can be used to diagnose the nutritional status and guide precise fertilization. Numerous previous researchers estimated LNC on reflectance spectrum or active chlorophyll fluorescence techniques with certain limitations. This study proposed a new technique of sun-induced chlorophyll fluorescence (SIF) for detecting LNC. We conducted an experiment to determine the optimal measurement point at the leaf scale for SIF and the best fluorescence yield indices (FY indices) extracted from SIF for LNC detection. The differences of the upward and downward FY indices were compared to determine the optimal FY indices. The results showed that the 1/3 distance from the leaf base is the optimal position for measuring SIF at the leaf level, and the downward FY indices are much better than the upward one with ratio peak FY687/FY739 as the best to monitor the LNC. The findings demonstrated that SIF can be utilized as a potential method for monitoring the LNC of winter wheat with higher efficiency.
关键词： ↓FY687/FY739,leaf nitrogen (LNC),upward and downward,fluorescence yield indices (FY indices),sun-induced chlorophyll fluorescence (SIF)
更新于2025-09-09 09:28:46