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The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400-1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R 2 LAI = 0.79, RMSE LAI [m 2 m −2 ] = 0.18, R 2 CHL = 0.77, RMSE CHL [µg cm −2 ] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R 2 yield = 0.88, RMSE yield [dt ha −1 ] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.From the farmer's perspective, the most important economic parameter is achieved yields. An overdose of N fertilizer, within the legal limits, results higher costs without adding value in terms of additional yield. Further possible regulations for the application of fertilizers should only have a limited negative impact on yields. With controlled experiments, directly comparing the harvested yield resulting from different N applications, one can identify the effects of reduced fertilization. Moreover, new concepts of monitoring these effects during vegetative growth enables the development of precision farming applications, especially created for efficient N fertilization [3].Remote sensing technology at various scales has often proved to be a suitable tool for agricultural crop monitoring [4]. In particular, UAV-supported remote sensing enables very precise monitoring of individual areas through lower flight altitudes and high-resolution data [5]. In recent years, the development of UAV-based hyperspectral recording systems has made rapid progress [6]. In comparison to manned aircraft based systems, the sensors are smaller, lighter, and less costly during acquisition and processing [7]. The great potential of this technology has been demonstrated [8].Hyper...

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical-statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011: R 2 cv = 0.739, 2012: R 2 cv = 0.85, 2011 and 2012: R 2 cv = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R 2 iv = 0.912) and RFR (R 2 iv = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R 2 iv = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building.