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1. An Open-Source, Durable, and Low-Cost Alternative to Commercially Available Soil Temperature Data Loggers

   https://doi.org/10.3390/s22010148  

   Curasi, Salvatore R.; Klupar, Ian; Loranty, Michael M.; Rocha, Adrian V. (January 2022, Sensors)

   Soil temperatures play an important role in determining the distribution and function of organisms. However, soil temperature is decoupled from air temperature and varies widely in space. Characterizing and predicting soil temperature requires large and expensive networks of data loggers. We developed an open-source soil temperature data logger and created online resources to ensure our design was accessible. We tested data loggers constructed by students, with little prior electronics experience, in the lab, and in the field in Alaska. The do-it-yourself (DIY) data logger was comparably accurate to a commercial system with a mean absolute error of 2% from −20–0 °C and 1% from 0–20 °C. They captured accurate soil temperature data and performed reliably in the field with less than 10% failing in the first year of deployment. The DIY loggers were ~1.7–7 times less expensive than commercial systems. This work has the potential to increase the spatial resolution of soil temperature monitoring and serve as a powerful educational tool. The DIY soil temperature data logger will reduce data collection costs and improve our understanding of species distributions and ecological processes. It also provides an educational resource to enhance STEM, accessibility, inclusivity, and engagement. 

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2. Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery

   https://doi.org/10.3390/rs11172020  

   Miller, Gwen J.; Morris, James T.; Wang, Cuizhen (September 2019, Remote Sensing)

   Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite data are useful for estimating aboveground biomass, however, high-resolution data are needed to resolve the spatial details within a salt marsh. This study used 3-m resolution multispectral data provided by Planet to estimate aboveground biomass within two salt marshes, North Inlet-Winyah Bay (North Inlet) National Estuary Research Reserve, and Plum Island Ecosystems (PIE) Long-Term Ecological Research site. The Akaike information criterion analysis was performed to test the fidelity of several alternative models. A combination of the modified soil vegetation index 2 (MSAVI2) and the visible difference vegetation index (VDVI) gave the best fit to the square root-normalized biomass data collected in the field at North Inlet (Willmott’s index of agreement d = 0.74, RMSE = 223.38 g/m2, AICw = 0.3848). An acceptable model was not found among all models tested for PIE data, possibly because the sample size at PIE was too small, samples were collected over a limited vertical range, in a different season, and from areas with variable canopy architecture. For North Inlet, a model-derived landscape scale biomass map showed differences in biomass density among sites, years, and showed a robust relationship between elevation and biomass. The growth curve established in this study is particularly useful as an input for biogeomorphic models of marsh development. This study showed that, used in an appropriate model with calibration, Planet data are suitable for computing and mapping aboveground biomass at high resolution on a landscape scale, which is needed to better understand spatial and temporal trends in salt marsh primary production. 

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3. Demonstrating a High‐Resolution Gulf of Alaska Ocean Circulation Model Forced Across the Coastal Interface by High‐Resolution Terrestrial Hydrological Models

   https://doi.org/10.1029/2019JC015724  

   Danielson, Seth L.; Hill, David F.; Hedstrom, Katherine S.; Beamer, Jordan; Curchitser, Enrique (August 2020, Journal of Geophysical Research: Oceans)

   Abstract We demonstrate a linking of moderately high resolution (1 km) terrestrial hydrological models to a 3‐D ocean circulation model having similar resolution in the northern Gulf of Alaska, where a distributed line source of freshwater runoff exerts strong influence over the shelf's hydrographic structure and flow dynamics. The model interfacing is accomplished via mass flux boundary conditions through the ocean model coastal wall at all land‐ocean adjoining grid cells. Despite the high runoff volume and lack of a coastal mixing estuary, the implementation maintains numerical stability by prescribing depth invariant and surface‐intensified inflows at fast and slow discharge grid cells, respectively. Based on comparisons against in situ hydrographic data, the coastal sidewall mass flux boundary condition results in more realistic hindcast surface salinity and salinity gradient fields than models that distribute coastal runoff in the form of spatially distributed precipitation. Correlations with observed thermal and haline monthly anomalies reveal statistically significant hindcast temporal variability during the freshet season when the signal‐to‐noise ratio is large. Comparisons of ocean models forced by high‐ and low‐resolution hydrological models reveal differences in salinity, surface elevation, and velocity fields, highlighting the value and importance of accurate coastal runoff fields. The model results improve our understanding of the regional influence of runoff on sea level elevations and the distribution and fate of fresh water. Our approach has potential applications to biogeochemical modeling in regions where distributed line source freshwater coastal discharges deliver heat, momentum, and chemical constituents that may influence the marine carbon pump. 

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4. Topographical shifts in fine root lifespan in a mixed, mesic temperate forest

   https://doi.org/10.1371/journal.pone.0254672  

   Primka, Edward J.; Adams, Thomas S.; Buck, Alexandra; Eissenstat, David M. (July 2021, PLOS ONE)

   Zang, RunGuo (Ed.)

   Root lifespan, often is estimated in landscape- and ecosystem-level carbon models using linear approximations. In water manipulation experiments, fine root lifespan can vary with soil water content. Soil water content is generally structured by complex topography, which is largely unaccounted for in landscape- and ecosystem-scale carbon models. Topography governs the range of soil water content experienced by roots which may impact their lifespan. We hypothesized that root lifespan varied nonlinearly across a temperate, mesic, forested catchment due to differences in soil water content associated with topographic position. We expected regions of the landscape that were too wet or too dry would have soils that were not optimal for roots and thus result in shorter root lifespans. Specifically, we hypothesized that root lifespan would be longest in areas that consistently had soil water content in the middle of the soil water content spectrum, while in soils at either very low or very high soil water content, root lifespan would be relatively short. We tested this hypothesis by collecting and analyzing two years of minirhizotron and soil moisture data in plots widely distributed in the Shale Hills catchment of the Susquehanna-Shale Hills Critical Zone Observatory in Pennsylvania. We found that fine root lifespans were longer in traditionally wetter topographic regions, but detected no short term (biweekly) effect of soil moisture on root lifespan. Additionally, depth in soil, soil series, slope face orientation, and season of birth strongly affected root lifespans across the catchment. In contrast, lifespan was unaffected by root diameter or mycorrhizal association. Failure to account for these variables could result in erroneous estimates of fine root lifespan and, consequentially, carbon flux in temperate forested regions. 

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5. Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

   https://doi.org/10.1038/s41598-021-86480-z  

   Falco, Nicola; Wainwright, Haruko M.; Dafflon, Baptiste; Ulrich, Craig; Soom, Florian; Peterson, John E.; Brown, James Bentley; Schaettle, Karl B.; Williamson, Malcolm; Cothren, Jackson D.; et al (December 2021, Scientific Reports)

   Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management. 

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