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Abstract. Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, nearreal-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R 2 = 0.98; MCA6: R 2 = 0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R 2 = 0.63) and converted near-infrared (R 2 = 0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera.Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.

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The nutritive value of pasture is an important determinant of the performance of grazing livestock. Proximal sensing of in situ pasture is a potential technique for rapid prediction of nutritive value. In this study, multispectral radiometry was used to obtain pasture spectral reflectance during different seasons (autumn, spring and summer) in 2009–2010 from commercial farms throughout New Zealand. The analytical data set (n = 420) was analysed to develop season‐specific and combined models for predicting pasture nutritive‐value parameters. The predicted parameters included crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, lignin, lipid, metabolizable energy (ME) and organic matter digestibility (OMD) using a partial least squares regression analysis. The calibration models were tested by internal and external validation. The results suggested that the global models can predict the pasture nutritive value parameters (CP, ADF, NDF, lignin, ME and OMD) with moderate accuracy (0·64 ≤ r2 ≤ 0·70) while ash and lipid are poorly predicted (0·33 ≤ r2 ≤ 0·40). However, the season‐specific models improved the prediction accuracy, in autumn (0·73 ≤ r2 ≤ 0·83) for CP, ADF, NDF and lignin; in spring (0·61 ≤ r2 ≤ 0·78) for CP and ash; in summer (0·77 ≤ r2 ≤ 0·80) for CP and ash, indicating a seasonal impact on spectral response.

A field method has been developed for rapid in situ assessment of soil carbon (C) and nitrogen (N) content using a portable spectroradiometer (ASD FieldSpecPro). The technique was evaluated at 7 field sites in permanent pasture, and in 1-year, 3-year, and 5-year pine-to-pasture conversions on Pumice, Allophanic, and Tephric Recent Soils in the Taupo and Rotorua region of New Zealand. A total of 210 samples were collected from 2 depths: 37.5 and 112.5 mm. Field measurement of diffuse spectral reflectance was recorded from a flat sectioned horizontal soil surface of a soil core using a purpose-built contact probe attached by fibre optic cable to the spectroradiometer. A 15-mm soil slice was collected from each cut surface for analysis of total C and N using a LECO Analyser. Soils had a wide range of total C and N (0.26–11.21% C, 0.02–1.01% N). Partial least-squares regression analysis was used to develop calibration models between smoothed-first derivative 5-nm-spaced spectral data and LECO-measured total C and N. The models successfully predicted total C and N in the validation sets with the best prediction for C (RPD 2.01, r2 0.75, RMSEP 1.21%) and N (RPD 2.66, r2 0.86, RMSEP 0.07%). Prediction accuracy using different selection methods of calibration and validation set is reported. This study indicates that in situ assessment of soil C and N by field spectroscopy has considerable potential for spatially rapid measurement of soil C and N in the landscape.