Thomas Piernicke

Monitoring heavy metal concentrations in agricultural soils is essential for assessing crop quality and food safety risks, yet conventional laboratory analyses are slow, expensive, and impractical for large-scale surveys. Visible, near-infrared (vis–NIR) and X-ray fluorescence (XRF) spectroscopy offer rapid, non-destructive alternatives, but their high-dimensional, noisy spectra require effective preprocessing. This study evaluates the Gaussian pyramid scale-space—a multi-resolution framework from image processing—as a unified denoising and dimensionality reduction approach for vis–NIR and XRF spectra. We analyzed 100 soil samples from the DEMMIN agricultural site (northeastern Germany) for six heavy metals (Cr, Mn, Fe, Cu, Zn, Pb). Eight Gaussian pyramid octaves were generated from each spectrum, with first- and second-derivative variants. Cubist regression models were trained for all combinations of metal, sensor, octave, and derivative. XRF single-sensor models demonstrated strong performance for Mn, Fe, Cu, Zn, and Pb (R² = 0.88–0.99; RPD = 2.89–9.34) but moderate performance for Cr (R² = 0.45). Vis–NIR models were uninformative across all metals, consistent with background-level concentrations and low soil organic carbon. Optimal predictive resolution occurred at intermediate octaves (0.08–0.32 keV), representing 4- to 16-fold reduction of the original sampling interval. Low-level fusion of vis–NIR and XRF did not improve XRF-alone performance. Cubist feature-importance analysis recovered characteristic Kα and L-series emission energies, confirming physically meaningful spectral exploitation. The Gaussian pyramid scale-space provides a principled, computationally efficient preprocessing strategy for evaluating heavy metals in soil via XRF spectroscopy.

Calculates the water balance of starch potatoes from Normalized Distance Vegetation Index (NDVI) images, German Weather Service (DWD) reference evapotranspiration, German Weather Service RADOLAN precipitation data and irrigation information. For more details see Piernicke et al. (2025) .

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems.