Providing farmers with the tools to monitor their crops continuously and reliably can aid the scaling of global food production to meet the ever-growing demand. The optical Normalised Difference Vegetation Index (NDVI) is commonly used to monitor crop greenness as an indicator for biomass, but it is limited by clouds and signal saturation. Synthetic Aperture Radar (SAR) imagery, which is not hampered by clouds, can be used to complement the NDVI. The Biomass Proxy (BP) combines the NDVI and SAR data, but spatial biomass estimations still strongly depend on the NDVI and therefore face the same limitations. This study aims to assess the potential value of spatial SAR data to approximate the in-field biomass distribution. The SAR signal from Sentinel-1 and the NDVI signal from Sentinel-2 were analysed temporally and spatially for fields of maize, barley, oat, and spring wheat in the Dutch province of Flevoland. It was assumed that consistent SAR patterns in the spatial signal correspond to biophysical changes in the monitored crops. A framework to detect these patterns and include them in the BP was created based on combining cluster detection with spatial autocorrelation. The components of this framework demonstrate that backscatter intensity, phenological stage and crop type influence the probability of consistent patterns and that consistent patterns could not be observed from the spatial NDVI signal. Moreover, it was found that the BP’s sensitivity to the input signals depends on crop type. With the knowledge of when and where consistent patterns occur, targeted research can be done to understand the spatial SAR signal better and, thereby, optimally use all available information.

This thesis explores the application of Distributed Temperature Sensing (DTS) technology to observe temperature gradients and turbulent eddies within and above a forest vegetation layer. Atmospheric temperature gradients tie directly to heat transfer, a primary mode of which are turbulent eddies. The goal in studying these elements is to enhance the understanding of land-atmosphere coupling, which plays a role in climate and weather modelling. The data collection took place in the first two weeks of November 2022 at a meteorology study site in Loobos, the Veluwe. A fiber optic cable was installed, running from the forest floor to 1.5 times the canopy height along a tower. The DTS technology provided high-resolution temperature measurements at one-second intervals. Fluctuations in these profiles could potentially be associated with turbulent eddies. By analyzing temperature profiles over time and integrating meteorological data, the study provided insights into the temporal evolution of vertical temperature distribution at the test site and its sensitivity to weather conditions. Phenomena such as direct sunlight, rainfall, canopy effects, and inversion layers were identified in the DTS data and validated with other meteorological measurements. Notably, sharp temperature inversions observed on two nights exhibited strong correlations with wind speed and its variation. Increases in wind speed and variability were associated with lower inversion heights.

This project is a consulting project for Community Cloud Forest Conservation (CCFC) on how to obtain and communicate to relevant stakeholders an understanding of the impact of land use change and climate change on the hydrological balance of the cloud forest ecosystem in the Sierra Yalijux. The outcomes of the project will be used by CCFC and partners in four areas: Rural water committee capacity building with municipal and village leadership groups, environmental education with the ministry of education, reforestation, and conservation carbon/water credit prioritization with the national forestry institute, and to create thesis topics for bachelors level students with Universidad Rafael Landívar and Universidad de San Carlos. In order to achieve this goal, we divided our efforts in four areas: First, a description of the situation and a review of literature to identify gaps in scientific and practical understanding of local cloud forest hydrology (Chapter 2). Second, an analysis of the situation at a regional scale using publicly available historical data such as remote sensing data and data from the national meteorological authority (Chapter 3). Third, identifying important hydrological processes in the Cloud Forest micro-climate (Chapter 4) and prototyping and testing measurement setups (Chapter 5). Fourth, making suggestions on how to apply the results to the intended impact areas that CCFC has (Chapter 6). Our recommendations to CCFC for capacity building with water committees are based on a literature re view, we found that the presence of Cloud Forest is expected to increase base flow in springs due to its ability to capture additional hydrological inputs in the dry season, increase moisture recycling after heavy rain events, and store water in the soil. We recommend working with water committees to outline the recharge zones of their springs, run some simple calculations on water availability based on precipitation, and develop manage ment plans for the area. Our recommendations for further research are based on the research approaches we describe at the regional scale and the prototyping of field methodologies that we tested. A more permanent setup for data collection is being developed jointly with the Universidad de San Carlos at CCFC’s nature preserve.

The non-linear relation between Evapotranspiration (ET) and the atmospheric moisture demand in terms of vapor pressure deficit (VPD) has been widely reported. Observations point towards a diverse range of potential drivers; however, without uncovering why the identified factors are found to control diurnal ET-VPD hysteresis. Modelling efforts have laid a theoretical foundation to unravel the compound effect of surface and atmospheric states on this hysteresis. However, so far these models have been unable to realistically incorporate the non-linear feedbacks between atmosphere and surface, and lack a vegetation representation that reflects the underlying biological mechanisms. To unravel in what manner the characteristics of ET-VPD hysteresis are controlled, multi-scale observations spanning biology, meteorology, and hydrology from the CloudRoots campaign are combined with modelling in a novel manner. For the first time a proof-of-concept is delivered for reproducing ET-VPD hysteresis with a model that incorporates both surface-atmosphere feedbacks as well as a mechanistic vegetation component. Observations are used for parameter initialization and evaluation to ensure that the modelled hysteresis curve and underlying processes represent a realistic case. Next, this calibrated model is used for a sensitivity analysis. The results show that both the early morning state of the surface and the state of the atmosphere influence the characteristics of the hysteresis loop with respect to its width, height, and initial slope. Via control of the stomatal aperture, soil moisture stress and the vegetation’s capacity to assimilate CO2 for photosynthesis affect the height and width of the curve. The height of the hysteresis loop is significantly affected by entrainment of warm dry air, while its width is minorly impacted. The characteristic fingerprint of entrainment is distinctly different to that of soil moisture stress and the capacity for CO2 uptake. The sensitivity analysis applied to our observationally inspired case, appears to facilitate quantification of the strongest model responses to changing external forcings and parameters. Therefore, the presented approach offers a promising tool, allowing further research into the controlling mechanisms of ET-VPD hysteresis.

Fog is of critical importance to forecast accurately, not least because of the hazard it presents to human safety. Yet, while weather forecasts have improved significantly over recent decades—and continue to improve—fog remains a particularly challenging phenomenon to predict. The research presented within this thesis takes a step back from prediction, and aims to better understand the conditions under which fog forms and deepens. Topics investigated include the observational likelihood of fog, the near-surface conditions during the infancy of a fog layer, the spatial variability of fog (and the influences thereon), and the growth and evolution of a fog layer.@en

Litter decomposition in soils is a microbial process linked to soil respiration, affected by soil composition as well as environmental factors such as temperature and moisture. For alpine areas in particular, these stand to vary significantly with climate change. To characterize the impacts of these projected changes on the carbon fluxes and carbon cycling in the environment, this study uses turf transplants along an altitudinal gradient in Western Norway. Warming is simulated for alpine and subalpine soils. Because the soils differ in composition, this enables the quantification of the influence of soil composition on soil litter decomposition and respiration. Decomposition is quantified using the standard Tea Bag Index (TBI) where the mass loss of buried rooibos and green tea bags over an extended period is used to model the labile and recalcitrant fraction of litter in the soil. Respiration CO2 fluxes are quantified from concentration measurements in an infrared gas analyzer (IRGA, LI-84A, LICOR) across a prescribed period and bare soil area. Results from this study show that simultaneous optimal soil moisture and temperature conditions maximize soil respiration and litter decomposition rates. These optimal ranges are 15-35% for soil moisture and the maximum measured 22 oC for temperature. However, the impacts of the conditions individually are more complex: soil moisture is positively correlated with soil respiration, while the correlation with temperature is inconclusive. Decomposition rates and stabilization factors for the Liahovden, the alpine site, consistently exceed those for Joasete, the sub-alpine site, which contains less soil organic matter (SOM) and carbon. With warming, Joasete exhibits opposing behavior to that of Liahovden: reduced decomposition rates and litter stabilization. These findings suggest that the availability of nutrients due to soil moisture and the soil composition itself are the most important factors determining the carbon emissions and cycling in the soil. Nevertheless, under coupled optimal conditions (warmer climate with soil moisture at approximately 35%), there is a clear maximum in flux. The complexity of the interrelations of soil moisture and temperature for different soil types supports further research on the topic, with more replicates and soil moisture content variation plot by plot.