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  • 1
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    Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring (2020)
    Springer
    Details
    Publication Date: 2020-10-28
    Description: Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.
    Print ISSN: 2512-2789
    Electronic ISSN: 2512-2819
    Topics: Geography , Geosciences
    Published by Springer
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  • 2
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    Using partial least squares (PLS) to estimate canopy nitrogen and biomass of paddy rice in Chinas Sanjiang Plain (2014)
    Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
    Details
    Publication Date: 2022-01-14
    Description: Abstract
    Description: Nitrogen (N) is one of the most essential elements in agriculture and ecology due to its direct role in determining crop yield and grain quality, as well as its association with canopy photosynthetic capacity and carbon-nitrogen cycling in the earth ecosystem. Remote sensing provides a useful way to capture canopy nitrogen and biomass with high spatial and temporal resolution. However, seasonal dynamics of plant morphophysiological variation hinder the simultaneous estimation of canopy N concentration (%N) and biomass using a traditional method such as vegetation indices because of the distinct dynamics of canopy biochemical and physical traits. In contrast, multivariate analysis method offers the capability of calibrating a model with multiple dependent variables of interest. Therefore, the main objective of this study was to, simultaneously, estimate canopy %N and biomass of rice using the partial least squares regression (PLSR) model. A field experiment was conducted for paddy rice fertilized with five N rates across five growth stages in 2008, located in the Sanjiang Plain, China. Results showed that the PLS regression model simultaneously explained 84% and 91% of the variation in %N and biomass, respectively, across the five growth stages. Our results also suggest that biomass is the dominant factor that affects the link between canopy dynamical traits and canopy reflectance measures. This study demonstrates that, by incorporating with PLSR for retrieving biophysical and biochemical properties from the full-spectrum analysis, to what extent canopy %N and biomass can be simultaneously estimated from canopy reflectance measurement.
    Keywords: Nitrogen ; Biomass ; Hyperspectral ; Remote Sensing ; Agriculture ; 550 Earth sciences
    Language: English
    Type: Text , Workshop paper
    Format: 5 Pages
    Format: 1130 Kilobytes
    Format: application/pdf
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