Unique properties of nanomaterials can be leveraged to increase the sustainability of food production and distribution.

This study investigates the ability of machine learning models to retrieve the surface soil moisture of a grassland area from multispectral remote sensing carried out using an unoccupied aircraft system (UAS). In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture. We tested four different machine learning algorithms and interrogated the models to rank the importance of different variables and to understand their relationship with surface soil moisture. All the machine learning algorithms we tested were able to predict soil moisture with good accuracy. The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content. Variable importance analysis revealed that the four most important variables were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

Modifications to landscapes and flow regimes of rivers have altered the function, biodiversity, and productivity of freshwater ecosystems globally. Reestablishing geomorphological and hydrological conditions necessary to sustain ecosystems is a central challenge for restoration within highly altered systems. Meeting this challenge requires simultaneously addressing multiple and interacting stressors within the context of irreversible changes and socio‐economic constraints. Traditionally, river restoration approaches either physically change the landscape or channel (channel‐floodplain manipulation) or adjust hydrology (environmental flows), and such actions are often independent. We juxtapose these two subfields of river restoration, which have undergone parallel transformations, from goals of reproducing static representations of form and flow regime to goals of reestablishing processes. The parallel transformations have generated shared ideas, which point to benefits of coupling channel‐floodplain manipulation and environmental flow actions to achieve process‐based goals. Such coupling supports comprehensive river restoration efforts aimed at supporting resilient ecosystems within human dominated landscapes in a nonstationary climate. We identify four elements of coupled approaches for restoring highly modified rivers: (1) identify physical and ecological process potential given interactive effects of altered landscapes and flows; (2) consider capacity for sustaining identified processes under potential future change; (3) model alternatives for coupled restoration actions to support identified processes; and (4) evaluate alternatives using metrics representing integrative effects of coupled actions. We suggest these emergent elements contribute to the development of standard practices for restoring highly modified rivers and encourage an increasing number and quality of coupled applications.

Soils account for the largest terrestrial pool of carbon and have the potential for even greater quantities of carbon sequestration. Typical soil carbon (C) stocks used in global carbon models only account for the upper 1 meter of soil. Previously unaccounted for deep carbon pools (>1 m) were generally considered to provide a negligible input to total C contents and represent less dynamic C pools. Here we assess deep soil C pools associated with an alluvial floodplain ecosystem transitioning from agricultural production to restoration of native vegetation. We analyzed the soil organic carbon (SOC) concentrations of 87 surface soil samples (0–15 cm) and 23 subsurface boreholes (0–3 m). We evaluated the quantitative importance of the burial process in the sequestration of subsurface C and found our subsurface soils (0–3 m) contained considerably more C than typical C stocks of 0–1 m. This deep unaccounted soil C could have considerable implications for global C accounting. We compared differences in surface soil C related to vegetation and land use history and determined that flooding restoration could promote greater C accumulation in surface soils. We conclude deep floodplain soils may store substantial quantities of C and floodplain restoration should promote active C sequestration.