The fine-scale mapping of soil organic matter (SOM) in croplands is vital for the sustainable management of soil. Traditionally, SOM mapping relies on laboratory methods that are labor-intensive and costly. Recent advances in unmanned aerial vehicles (UAVs) afford new opportunities for rapid and low-cost SOM mapping at the field scale. However, the conversion from UAV measurements to SOM maps requires specific transfer models that still rely on local sampling. This study aimed to develop a method for predicting topsoil SOM at a high resolution on the field scale based on soil color information gained from low-altitude UAV imagery and machine learning. For this, we performed a UAV survey in cropland within the German loess belt. We used two fields, one for training and one for validation of the model, to test the model transferability. We analyzed 91 soil samples for SOM in the laboratory for the model calibration and 8 additional samples for external model validation. A random forest model (RF) showed good performance for the prediction of SOM based on UAV-derived color information with an RMSE of 0.13% and with an RPIQ of 2.42. The RF model was used to predict SOM at a point-support of 1 × 1 m. The SOM map revealed spatial patterns within the fields with a uniform spread of the prediction uncertainty. The validation of the model performed similarly to the calibration with an RMSE of 0.12% and an RPIQ of 2.05, albeit with a slight bias of 0.05%. This validation using external data showed that prediction models are transferable to neighboring fields, thus permitting the prediction on larger scale farms or enabling carbon monitoring over time.

Urban green spaces (UGS) and peri-urban green spaces (P-UGS) play a crucial role in reducing the land surface temperature within the urban environment, especially during heat waves. Although their cooling effect generally is due to shading and evaporation, the role of soil texture and soil water availability on surface cooling remains largely unexplored. This study investigated the impact of soil texture on the spatio-temporal patterns of LST in different UGSs and P-UGSs in Hamburg (Germany) during a hot summer drought period. The LST and the Normalized Differentiated Moisture and Vegetation Indices (NDMI, NDVI) were calculated based on two Landsat 8 OLI/TIRS images from July 2013. Non-spatial and spatial statistical approaches such as stepwise backward regression or Hotspot (Getis-Ord Gi*) analyses were applied explaining LST distributions in relation to soil texture within each UGS and P-UGS. All GSs were clearly characterized as surface cooling islands whereas, for each GS, an individual thermal footprint was observed. Within all GSs, the LST patterns showed a significant negative relationship to NDMI values, whereas the NDVI values and the elevation were of minor importance. Soil texture was found to influence the LST distribution significantly in most UGSs and P-UGSs, where sites on clay-rich soils showed the highest LST values compared to sites on sand- or silt-rich soils. For example, in parks, clayey soils showed a mean LST of 25.3 °C whereas sand-dominated sites had a mean LST of only 23.1 °C. This effect was consistent throughout all statistical approaches, for both dates and across most GSs. This unexpected result was explained by the very low unsaturated hydraulic conductivity in clayey soils which limits plant water uptake and transpiration rates responsible for the evaporative cooling effect. We concluded that soil texture has to be considered for understanding and managing the surface cooling capacity of UGSs and P-UGSs.

Solar photovoltaic panels (PV) provide great potential to reduce greenhouse gas emissions as a renewable energy technology. The number of solar PV has increased significantly in recent years and is expected to increase even further. Therefore, accurate and global mapping and monitoring of PV modules with remote sensing methods is important for predicting energy production potentials, revealing socio-economic drivers, supporting urban planning, and estimating ecological impacts. Hyperspectral imagery provides crucial information to identify PV modules based on their physical absorption and reflection properties. This study investigated spectral signatures of spaceborne PRISMA data of 30 m low resolution for the first time, as well as airborne AVIRIS-NG data of 5.3 m medium resolution for the detection of solar PV. The study region is located around Irlbach in southern Germany. A physics-based approach using the spectral indices nHI, NSPI, aVNIR, PEP, and VPEP was used for the classification of the hyperspectral images. By validation with a solar PV ground truth dataset of the study area, a user’s accuracy of 70.53% and a producer’s accuracy of 88.06% for the PRISMA hyperspectral data, and a user’s accuracy of 65.94% and a producer’s accuracy of 82.77% for AVIRIS-NG were achieved.

Sea level rise, a possible increase in frequency and intensity of storms and other effects of global warming exert pressure on the coastal regions of the North Sea. Also storm surges threaten the basis of existence for many people in the affected areas. As well as for building coastal protection or offshore structures, detailed knowledge of wave data, especially the wave height, is of particular interest. Therefore, the nearshore wave climate at the island Norderney is measured by buoys since the early 1990s. Caused by crossing ships or weather impacts, these buoys can be damaged. This leads to a huge amount of missing data in the wave data time series, which are the basis for numerical modelling, statistical analysis and developing coastal protection.Artificial neural networks are a common method to reconstruct and forecast wave heights nowadays. This study shows a new technique to reconstruct and forecast significant wave height measured by buoys in the nearshore area of the Norderney coastline. Buoy data of the period 2004 to 2017 from the NLWKN – Coastal Research Station at Norderney were used to train three different statistical and machine learning models namely linear regression, feed-forward neural network and long short-term memory (LSTM), respectively. An energy density spectrum was tested against calculated sea state parameter as input. The LSTM – a recurrent neural network – is the proposed algorithm to reconstruct wave height data. It is especially designed for sequential data, but was performed on wave spectral data in this study for the first time. Depending on the input parameter of the respectively model, the LSTM can reconstruct and forecast time series of arbitrary length.Using information about wind speed and direction and water depth, as well as the wave height of two neighboring buoy stations, the LSTM reconstructs the wave height with a correlation coefficient of 0.98 between measured and reconstructed data.Unfortunately, the forecasting and reconstruction error of extreme events is highly underestimated, though these events are of great interest for climate and ocean science. Currently, this error is being specifically attempted to improve. Compared to numerical modeling, the machine learning approach requires less computational effort. Results of this study can be used to complete spatial and temporal wave height datasets, providing a better basis for trend analysis in relation to climate change and for validating numerical models for decision making in coastal protection and management.