Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77% and 95%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.

Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques—in this case, laser-induced breakdown spectroscopy (LIBS)—and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.

Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate change and agricultural practices is challenging because existing SOC monitoring methods are very time and resource consuming. This study examined the use of visible near-infrared spectroscopy (Vis-NIR) as a speedy method to predict SOC and to monitor spatial and temporal changes in SOC compared with labor-intensive traditional laboratory (TL) measurements. For SOC prediction, topsoil (0–25 cm) and subsoil (25–50 cm) samples in the Danish soil spectral library for the years 1986 and 2009 were used. Empirical Bayesian Kriging was used to map SOC. The Vis-NIR predictions indicated that average topsoil and subsoil SOC had decreased slightly in Denmark from 1986 to 2009, and this was confirmed by TL measurements of SOC. In East Denmark, Vis-NIR predictions differed significantly from the measured SOC values. For subsoil samples, the ability of Vis-NIR to predict SOC levels varied. In West Jutland, Central Jutland, North Jutland, and Thy, Vis-NIR–predicted SOC levels did not differ from TL-measured levels, showing good predictive ability. For topsoil samples, the spatial pattern of change in TL-measured and predicted SOC was consistent during the 23-year study period, but there were significant discrepancies in the corresponding SOC change patterns for subsoil samples. To conclude, Vis-NIR is a promising method for monitoring spatial and temporal changes in SOC at the national scale, especially in the topsoil. Some difficulties can arise in low SOC subsoils, so more systematic work is needed to improve the method for practical applications.