Following observations of a drop in the West Antarctic Ice Sheet (WAIS) mass balance over the last few decades with the possibility to reach a tipping point leading to ineluctable glaciers outlets instability in the region, understanding the driving processes has become a priority. In particular, the Circumpolar Deep Water (CDW) intrusion onto the continental shelf in the Amundsen Sea is, nowadays, in the spotlight, and gathers the attention of both the observers and the modellers. This modelling study presents the analysis of a 1/12° simulation of the Amundsen Sea sector reproducing well the interannual-to-decadal variability of the ice-shelves basal melt rates. The development of a methodology to study the ocean state in the reference frame of the continental shelf break enables us to distinguish and characterize a western fresh shelf zone and an eastern warm shelf zone in the region. Connecting it with the more regional circulation, we try to shed light on the different mechanisms driving the CDW inflow onto the continental shelf in the region. In particular, we draw attention to the sea ice effect in terms of Ekman pumping along the shelf break, and we point out the possible initiation of a southern Antarctic Circumpolar Current (ACC) branch to the south-east of the Ross Gyre, which could control part of the variability along the Amundsen Sea shelf. Finally, we discuss correlations between the ocean variability at the shelf break and the one of the melt activity underneath the ice-shelves.

High altitude ecosystems react sensitively to climatic changes. To evaluate hydroclimate dynamics and ecosystem responses since the late glacial in a typical glacier influenced, high-altitude, lacustrine ecosystem, we analysed concentrations and hydrogen isotopic composition of different aquatic (alkenones, nC23:1, HBIs) and terrestrial (nC31) biomarker groups in Hala Hu. The lake is located on the northern margin of the Tibetan Plateau, i.e. in the transitioning zone between the East Asian summer monsoon (EASM) and westerlies influence. According to species association of ostracods, it experienced a lake level rise of ca. 45 m during the late glacial and early Holocene and reached its highest stand during early mid-Holocene (8 - 6.1 cal. kyr BP) followed by lake level decline. The organic geochemical data reveal asynchronous responses of the aquatic and terrestrial ecosystem in Hala Hu and its catchment to hydroclimatic forcing. We interpret δD values of terrestrial biomarkers as indicative for δD of growing season precipitation, with a potential contribution of surface meltwater runoff. Hence, most negative values during 9 - 7 cal. kyr BP indicate changes in moisture source, possibly related to EASM intensification. In contrast, rapid appearance and disappearance of aquatic biomarkers and shifts in theirs δD values between ca. 8 – 5 kyr BP, revealed distinct mid-Holocene aquatic regime-shifts and changes in lake hydrology. Maxima of terrestrial biomarker concentrations also occurred between 8 - 5 cal. kyr BP, suggesting expansion of terrestrial grasses and eventually increased biomarker flux via surface runoff. These results give evidence that neither shifts of vapour source nor increased precipitation amounts were the major triggers of ecosystem responses. Instead we hypothesize, that warmer temperatures during the Holocene climate optimum, receding glaciers and consequently increased meltwater discharge into the lake, caused the pronounced ecological changes. The rapid regime-shifts within phytoplankton communities in the mid-Holocene illustrate the sensitivity of high altitude mountain ecosystems within the marginal Asian monsoon region to climatic changes. This is of relevance in context with future changes as response to global warming.

Over the past several decades, scientists have focused on numerical sensitivity analysis to explain the relative importance of tropical cyclone (TC) forward speed on storm surge and wave height predictions. These past studies performed numerical analyses, but the results have not been sufficiently compared with long term observations. In this study, 42 years of tidal records and landfall TC best tracks in Japan were analyzed, demonstrating that TC pre-landfall forward speed is significantly correlated with both maximum storm surge and significant wave height. Coastal horizontal morphology was the determining factor among these correlations. Fast-moving TCs tended to amplify the storm surge along open coastlines (Pearson correlation coefficient, R = 0.62), but reduce it in semi-enclosed bays (R = -0.52). A similar tendency has also been observed for the case of wave height (open coast, R = 0.62; bay, R = -0.52). The negative correlation contrasts with the general perception that the coincidence of TC wind speed and forward speed vectors generates a larger storm surge. The influence of coastal morphology was most prominent for TCs with a central pressure lower than 956 hPa. Tropical cyclone operational forecasts are continuously improving; however, there is still scope to improve the precision of storm surge - wave predictions. These findings may be beneficial in two main areas. Firstly, considering TC transitional speed and coastal geometry (open coastline or bays) - meteorologists and oceanographers could provide more comprehensive surge-wave forecasts, and emergency management personnel could use pre-landfall forward speed for more precise early warning. Secondly, coastal areas at risk with no access to advanced weather forecasting could use these empirical findings along with other TC intensity related information (e.g., wind speed, central pressure, radius) for improvement of early-warning activities.

Metal compounds abundant in Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals, for example, are proposed to have served as primitive metalloenzyme cofactors for their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis and astrobiology.

Modelling and monitoring seasonal snow are critical for water resource management, flood forecasting and avalanche risk prediction. Snowmelt processes are of particular importance. The behaviour of liquid water in snow has a big influence on melting processes but is difficult to measure, monitor and model. Recent work has shown the promise of electrical self potential measurements for monitoring snow hydrology. To further this aim, detailed modelling of internal water fluxes in melting snow was carried out with FSM and Crocus. These models were coupled to an electrical model of unsaturated flow in snow to create synthetic electrical self potential observations. These synthetic observations were compared to measurements made during two winter field seasons at Col de Porte in the French Alps with a prototype geophysical array installed in October 2018. Different representations of liquid water flow were used to force the electrical model to evaluate its ability to reproduce the measured self potential signals, and to investigate the suitability of the self potential array as part of a coupled geophysical monitoring and modelling system.

Open data, data that anyone can access, use, and share have found its place in the current research landscape. Yet, there are many waters to navigate for open access data. Repositories such as BCO-DMO are here to make that journey easier; juggling data principles and policies, funding requirements, publication specifications, research specifics, archiving and discovery through online search engines. BCO-DMO is a domain-specific repository serving highly heterogeneous biological and chemical oceanographic data. The scale of our data ranges from atmospheric aerosols to the bottom of the seafloor, microbes to megafauna. We assist investigators supported through NSF and private funders throughout the Data Life Cycle. We not only have in-house data managers that extensively curate the data and assist submitters in complying with the F.A.I.R Data Principles, but we also develop resources and architecture to tackle technology hurdles associated with these principles to ease data sharing on the behalf of researchers: Designing and implementing ontologies to increase discovery of the data Developing systems to track provenance Creating and improving standard operating procedures to ensure consistent data processing Providing APIs for improved interoperability Developing an automated submission system to ease data sharing burden To make sure that everything we develop addresses community needs, we continue to seek feedback on our processes (i.e what hurdles does the community have with their data and how can we help to overcome them) through a strategic planning committee and continued communication with submitters. We take part in several broader data community initiatives to improve data interoperability and reuse and demonstrate trustworthiness by obtaining CoreTrustSeal certification. How do you determine an appropriate repository to openly share your data? Is BCO-DMO on your list of choices? Let’s hear about it.

Thunderstorms exhibiting anomalous charge structures (ACSs, i.e., anomalous storms) are comprised by more riming ice carrying net positive versus negative charge, thought to result from increased mixed-phase cloud liquid water content (LWC). Anomalous storms are rarely observed in the United States (US) outside of the Great Plains (GP) region, where environmental conditions that suppress warm precipitation efficiency and support robust updrafts are thought to favor increased mixed-phase LWC. Two rare anomalous supercells in the Southeastern (SE) US exhibited similar charge structure characteristics as observed in GP anomalous storms, including a deep positive (negative) charge layer associated with riming (non-riming) ice in the lower (upper) mixed-phase region. However, most characteristics associated with SE anomalous environments were not consistent with those in the GP. A more rigorous evaluation of hypotheses concerning ACS development and their observation in the SE compared electrical, kinematic, microphysical, and environmental properties between the two anomalous and two SE normal supercells (i.e., exhibiting normal charge structures). Similar metrics of warm precipitation efficiency were observed in each. However, lower relative humidity in the charging region of the SE anomalous storms uniquely matched environmental characteristics in GP anomalous storms and differentiated SE anomalous from normal environments, suggesting the relative importance of saturation ratio alongside LWC in positive charging of riming ice. Differences were also observed in charge region characteristics and flash locations between a SE anomalous and normal storm. As riming ice increased in the negative charge region of the ACS, flash initiation locations were increasingly observed in stronger updrafts and updraft gradients compared with the normal storm. The evolution of microphysical characteristics of the negative charge region in the anomalous storm suggested an increase in normal alongside anomalous charging, indicating that variability in charging polarity may impact spatial flash relationships with the updraft. Further work is needed to diagnose whether particle-scale charging variability influences flash rate relationships with convective parameters such as updraft or graupel volume.

This study investigates how course design for a first year introductory class on climate change impacts student understanding of this global issue. The course was developed to situate the scientific knowledge of climate change alongside the political, economic, and social dimensions of this human-caused crisis. This intentional course development was to give students a more holistic understanding of the causes, impacts, and solutions. The curriculum is widely interdisciplinary with scientific concepts, like the functioning of the climate system and tools of climate system research, taught alongside politics, economics, media analysis, and social justice. A teaching as research project was conducted to assess how adding the sociopolitical context impacted student's ability to situate their knowledge of the scientific basis of climate change using systems thinking. As climate change, and scientific research, operate within the social and political landscape students became more informed citizens through learning these connections. Student participants in the study have a range of backgrounds and were all majors within the college of natural sciences during their first semester of college, though not all majoring in earth sciences

The subseasonal Madden-Julian oscillation (MJO) is among the most important modes of tropical variability on the planet and is a dominant driver of subseasonal-to-seasonal (S2S) prediction globally. The past decade has seen substantial advances in MJO prediction using dynamical forecast models, which now routinely outperform traditional statistical MJO forecasts (e.g. multiple linear regression models). At the same time, an increasing body of literature has demonstrated that machine-learning methods represent a new frontier in Earth science, opening the door to more advanced statistical forecast models of the MJO. In this study, we explore whether state-of-the-art machine learning methods can be used to make real-time MJO forecasts that outperform traditional statistical models and do comparably well to dynamical models. In particular, we utilize neural networks trained on observational tropical fields to attempt to make skillful forecasts of MJO convection out to several weeks lead time. Through contrasting the machine-learning models’ behavior with simpler statistical models and dynamical forecast models, we explore the advantages and disadvantages of statistical versus dynamical forecasts. A novel aspect of our analysis is the use of cutting-edge techniques to allow us to visualize how our neural network models makes their predictions. These techniques, such as layer-wise relevance propagation, can lead to new insights into regions of MJO predictability, allowing us to better interpret sources of MJO prediction skill within the machine-learning model. We further diagnose whether our machine-learning models contain well-known aspects of MJO prediction found in dynamical models, such as an increase in prediction skill during boreal winter or during certain phases of the stratospheric quasi-biennial oscillation.

The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using NDVI dataset as an indicator of the photosynthetic capacities of vegetation. Hydroclimatic variability is analyzed using satellite-based precipitation datasets, observed river discharge and satellite measurements of terrestrial water storage (TWS). Our results show that the vegetation in the Basin varies from energy- to water-limited. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI (r=-0.48 to -0.65), suggesting that during this period the vegetation is mainly energy-dependent. Outside this period, these correlations are positive (r=0.55 to 0.9), suggesting that vegetation depends mainly on water availability. This higher water dependence is more noticeable during the vegetation dry season (VDS; June-October). Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests remain energy-limited during the beginning of the VDS, but they become water-dependent almost at the end. Savannas show a different behaviour, where water dependence occurs months before the onset of the VDS. On the other hand, unlike the other variables, the TWS better explains the NDVI in evergreen forests during the VDS (r=0.7 to 0.85). This is probably because evergreen forests are more dependent on deep soil water. A spatial analysis between hydroclimatic variables and the NDVI shows the predominance of positive correlations in most of the basin. However, specific areas do not show significant correlations. The weak relationship in these areas is explained by two factors i) very wet conditions during most of the year in the “rainfall hotspot” regions, where the vegetation is not water-limited, and ii) recent land-use changes (deforestation) that break the natural response in the hydroclimate-vegetation system. These findings provide new evidence on the impacts of the land cover changes on the natural relationship between vegetation and hydroclimatic variability, which is particularly relevant given the increasing rates of deforestation in this region during the recent years.

The eWaterCycle platform(https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. Reproducibility is a key ingredient of FAIR, and one of the driving principles of eWaterCycle. While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data. To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/). Using ESMValTool the eWaterCycle platform can perform commonly required pre-processing steps such as cropping, re-gridding, and variable derivation in a standardized manner. If needed, it also allows for custom steps for a Hydrological model. Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models. Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/). The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer. The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling. Ultimately, we hope to make it possible to fully reproduce a Hydrological model experiment from data pre-processing to analysis, using only a few clicks.

The event of 8 September 2017 was characterized by the effects of the arrival of two interplanetary coronal mass ejections on September 6th and 7th and a resultant geomagnetic storm. This storm event has been widely studied due to its extreme geo-effectiveness in the global geospace. In the inner magnetosphere, the effects included a distinct intensification of the ring current and a severely eroded plasmasphere. However, little attention has been paid to the role that the observed substorm injections played on the storm-time ring current. Starting at 1209 UT on September 8th, multiple substorm onsets occurred spreading over a wide magnetic local time range on the dawn side. Multiple substorm injections were observed simultaneously at geosynchronous orbit by the Los Alamos National Laboratory satellites and the Geostationary Operational Environmental Satellites, and by both the Exploration of energization and Radiation in Geospace/Arase and the Van Allen Probes missions deep in the inner magnetosphere. Subsequent buildup of the ring current was observed. In this study, we will investigate the role of the substorm injections on the extreme ring current response by numerical simulations with the physics-based Comprehensive Inner Magnetosphere-Ionosphere model using the geosynchronous data as boundary conditions to the model. Since the ring current has a strong influence on the inner magnetospheric dynamics, we also consider its impacts on the dynamics of the electric field and the plasmasphere. Furthermore, this study addresses the critical need to include substorms in evaluating the geo-effectiveness of geomagnetic storms.

Bifurcations and tipping points (TPs) are an important part of the Earth system’s behavior. These critical points represent thresholds at which small changes in the system’s parameters or in the forcing abruptly switch the system from one state or type of behavior to another. Current concern with TPs is largely due to the potential contribution of anthropogenic forcing to one or more climatic subsystems undergoing such abrupt, and possibly irreversible, transitions. Paleorecords of past climate have been shown to contain abrupt transitions, or “jumps,” which may represent former instances of tipping. Robustly identifying and describing such transitions is essential to properly understanding the underlying bifurcation mechanisms. Doing so may provide valuable information for identifying critical TPs in current and future climate evolution.Here we present a robust methodology for detecting abrupt transitions in proxy records that is applied to a set of ice core, speleothem, and marine sediment records of the last climate cycle and the Holocene. This methodology is based on the nonparametric Kolmogorov-Smirnov (KS) test for the equality, or not, of the probability distributions associated with two samples drawn from a time series, before and after any potential jump. The KS test is augmented by several other criteria and it is evaluated against other jump detection algorithms, aiming to establish a “gold standard” for abrupt-transition detection. This objective approach to identifying climate TPs will allow us to construct better nonlinear and stochastic models of bifurcations in the Earth’s climate and its interactions with ecosystems within the TiPES (Tipping Points in the Earth System) EU-funded project.

A primary source of uncertainty in terrestrial biosphere model (TBM) projection of carbon uptake and water cycling from ecosystems is the relationship between CO2 assimilation (A) and water loss via stomatal conductance (gs). A common mathematical framework for modeling this relationship is the “Unified Stomatal model”, which relates A to gs over environmental conditions and is governed by two terms, the stomatal slope (g1) and intercept (g0). Given their importance in determining the relationship between forest productivity and climate, an accurate and mechanistic understanding of the g1 and g0 parameters is crucial, particularly in wet tropical broadleaf forests where changes in water cycling could impact global weather patterns. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where the environmental conditions are modified for a single leaf, or (2) a survey approach, where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions. We compare the curve and survey approaches by conducting a comprehensive measurement campaign in which we paired diurnal gas exchange surveys with leaf level response curves for the estimation of g1 and g0 on six tropical species across a full range of leaf phenological stages. We examine how these different estimates impact model projection of gs, and how the consideration of a diurnal effect on g1 and g0 can improve predictions relative to a model using parameter estimates which are fixed over the photoperiod. Our results showed that age is an important factor to consider in estimates of g0, however there was no effect of leaf age on estimates of g1. The survey approach identified a diurnal trend associated with g1 and g0, which when accounted for improved model projections of diurnal trends in gs. We found that while both approaches yield equally statistically valid estimates of g1 and g0 at a fixed point in time, they are not directly comparable across diurnal timescales, where shifting water supply and carbon demand lead to dynamic canopy scale water use efficiency (WUE). These results suggest that to improve the accuracy of modelled gs in tropical forests, TBMs should recognize and implement diurnal variation in stomatal parameters which are associated with diurnal shifts in WUE.

Combining auxiliary variables and ground data of forest parameters using the model-based approach has become a frequently used methodology to produce synthetic estimates for small areas. These small areas arise as it may not be financially feasible to take ground measurements or some areas may be inaccessible to ground-based personnel. Until recently, these estimates have been calculated without providing a measure of the variance or in large homogeneous forested areas where the variability would be minimal. This paper uses a Random Forest algorithm to produce estimates of QMDBH (cm) and volume (m3 ha-1) and a k-nn technique to produce variance estimates in a previously unproven heterogeneous forest environment. The area of interest (AOI) was the commercial forest in the Slieve Bloom Mountains in the Republic of Ireland, where the majority species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured during the Summer in 2018 during which a LiDAR campaign was flown and Sentinel 2 satellite imagery captured, which were both used as auxiliary variables. Root mean squared errors (rmse) for the modeled estimates of QMDBH and volume were 4.4 cm and 111 m3 ha-1 and the r2 values were 0.65 and 0.76 respectively. An independent dataset of pre-harvest forest stands was used to validate the modeled variance estimates. The results showed that 73% of QMDBH measurements and 60% of volume measurements were within the confidence intervals of the estimated values. The mean percentage standard deviation for QMDBH and volume were 17% and 21% respectively. This application of variance estimation in such a heterogeneous forest landscape adds further weight to the applicability of the methodology in a range of forest landscapes. This finding is extremely important as it highlights the benefits of using earth observation data to produce estimates and confidence intervals for small areas in heterogeneous forest landscape.

SKB and several other waste management organizations have established the international SKB Task Force on Modelling of Groundwater Flow and Transport of Solutes (TF GWFTS) to support and interpret field experiments. An important objective of the task force is to develop, test and improve tools for conceptual understanding and simulating groundwater flow and transport of solutes in fractured rocks. Work is organized in collaborative modelling tasks. Task 9 focuses on realistic modelling of coupled matrix diffusion and sorption in heterogeneous crystalline rock matrix at depth. This is done by inverse and predictive modelling of different in-situ transport experiments. The ultimate aim is to develop models that in a more realistic way represent retention in fractured rock. Posiva’s REPRO (rock matrix REtention PROperties) experimental campaign has been performed at the ONKALO rock characterization facility in Finland. The two REPRO experiments considered were the Water Phase Diffusion Experiment, addressing matrix diffusion in gneiss around a single borehole interval (modelled in Task 9A), and the Through Diffusion Experiment, which is performed between sections of three boreholes and addressed by modelling in Task 9C. The Long-Term Diffusion and Sorption Experiment (LTDE-SD) was an in-situ radionuclide tracer test performed at the Äspö Hard Rock Laboratory at a depth of about 410 m below sea level. The experimental results indicated a possible deeper penetration of tracers into the rock matrix than expected and the shape of the penetration profiles were not according to theory. This experiment was modelled and interpreted in Task 9B. Task 9D is addressing the possible benefits of the detailed modelling of the experiments in safety assessment calculations. The task is performed by upscaling of Task 9A to conditions applicable for performance assessments of nuclear waste repositories. Of additional interest is the collective work performed by the task force to conceptually understand and interpret the field experiments, and at the same time increase the realism in solute transport modelling. This study would not have been possible without the support from the waste management organizations and the work by the multiple modelling teams.

The efficiency of heat transfer in the outer shell of icy satellites is important to determine the evolution and thermal state of their interior with major implications for the cooling behavior of an internal ocean. In this study, we systematically investigate thermal convection in the ice shell of Europa using an Arrhenius viscosity and accounting for ice I material that is dependent on both grain size and strain rate. To this end, we employ the geodynamical code GAIA [1] with a mixed rheology approach similar to [2], and perform calculations in a 2D Cartesian box and spherical annulus geometry for two values of the ice shell thickness (i.e., 30 and 70 km). In our simulations, we test various constant grain size values. In a first serie of simulations, we tested the importance of the dislocation creep mechanism for modeling convection in Europa’s ice shell. Our results show that, in a mixed diffusion-dislocation creep rheology, diffusion creep is the dominant heat transfer mechanism, similar to the study of [3]. A pure dislocation creep rheology leads to a conductive ice shell. Dislocation creep may become dominant if its rheological prefactor increases by about 5 orders of magnitude, which even taking into account the uncertainty associated with rheological measurements is considered unrealistic. Additional simulations that use a mixed diffusion-basal slip rheology show that for ice shells, basal slip may be a relevant deformation mechanism in addition to diffusion creep. Another important aspect is that the efficiency of heat transfer is larger for a thick ice shell (70 km, compared to a thinner one (i.e., 30 km)). However, the dimensional surface heat flow obtained for a thin ice shell is larger than for a thicker one. This is caused by the rescaling of non-dimensional parameters to a dimensional heat flow. References: [1] Hüttig et al., PEPI 2013; [2] Schulz et al., GJI 2019; [3] Harel et al., Icarus 2020.

Remotely-sensed Solar Induced chlorophyll Fluorescence (SIF) is a novel promising tool to retrieve information on plants’ physiological status due to its direct link with the photosynthetic process. At the same time, narrow band Vegetation Indices (VIs) such as the MERIS Terrestrial chlorophyll index (MTCI), and the Photochemical Reflectance Index (PRI), as well as broad band VIs like the Normalized Difference Vegetation Index (NDVI), have been widely used for crop stress assessment. A match between these remote sensing products and the spatial distribution of soil units is expected; nevertheless, an in-depth analysis of such relationship has been rarely performed so that additional studies are required. In this contribution, we aimed at the comparison in the use of normalized SIF (SIF = SIF/PAR; computed with the Spectral Fitting Method, SFM) and VIs (MTCI, PRI and NDVI) for heat stress assessment in corn, sugar beet and potato at the beginning and towards the end of a heatwave occurring in Selhausen, Germany, 2018. Data were acquired with the HyPlant airborne sensor, which is a high performance imaging spectrometer with around 0.30 nm of spectral resolution in the Oxygen absorption bands. We compared different plots located in the upper (poorer soil characteristics for agriculture such as water holding capacity and content of coarse sediments) or lower landscape terraces; we also evaluated the different remote sensing products in comparison with site specific geophysics-based soil maps. At the beginning of the heat wave we found that, compared with VIs, SIF data showed a clearer differentiation of the stress conditions at a terrace level in potato and sugar beet. However, towards the end of the wave a significant decrease of MTCI and NDVI contrasted with higher SIF in sugar beet and corn. Nonetheless, those crops (sugar beet and corn) did not show significant SIF differences between terraces. A significant spatial match was found between SIF and geophysics-derived soil spatial patterns (p = 0.004-0.030) in fields where NDVI was more homogeneous (p = 0.028-0.499, respectively). This suggests the higher sensitivity of SIF to monitor heat stress compared with common VIs.