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The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation. [ABSTRACT FROM AUTHOR]

Inflationary deformation and very long period (VLP) earthquakes frequently accompany basaltic caldera collapses, yet current interpretations do not reflect physically consistent mechanisms. We present a lumped parameter model accounting for caldera block/magma momentum change, magma chamber pressurization, and ring fault (assumed vertical) shear stress drop. Pressurization of the underlying magma chamber is represented by a tri‐axial expansion source, and the combined caldera block/magma momentum change by a vertical single force. The model is applied to Kīlauea 2018 caldera collapse events, accurately predicting near field static/dynamic ground motions. In addition to the tri‐axial expansion source, the single force contributes significantly to the VLP waveforms. For an average collapse event with fully developed ring fault, Bayesian inversion constrains ring fault stress drop to ∼0.4 MPa and the pressure increase to ∼1.9 MPa. That the predictions fit both geodetic and seismic observations confirms that the model captures the dominant caldera collapse mechanisms. Plain Language Summary: Episodic caldera collapses at basaltic volcanoes can be hazardous, and forecasting them requires physically consistent interpretations of geophysical observations. We use a physics‐based approach to explain caldera collapse induced ground motions, such as during the 2018 collapse of Kīlauea. We show that, the most fundamental physical mechanisms of caldera collapse involve a pressure increase in the underlying magma chamber, due to rapid reduction of its volume, and a time‐varying force, due to the acceleration of caldera block/magma. The physics‐based model will allow for more accurate interpretations of seismic data collected from less well monitored caldera collapses. Key Points: Caldera block/magma momentum change and chamber pressurization parsimoniously explain co‐collapse inflation and very long period eventsCoupled tri‐axial expansion source and vertical single force arise in the point source limit, contributing to very long period waveformsInversion constrains co‐collapse shear strength drop, pressurization, and mass of caldera block/magma during Kīlauea's 2018 events [ABSTRACT FROM AUTHOR]

Ice cover plays a critical role in physical, biogeochemical, and ecological processes in lakes. Despite its importance, winter limnology remains relatively understudied. Here, we provide a primer on the predominant drivers of freshwater lake ice cover and the current methodologies used to study lake ice, including in situ and remote sensing observations, physical based models, and experiments. We highlight opportunities for future research by integrating these four disciplines to address key knowledge gaps in our understanding of lake ice dynamics in changing winters. Advances in technology, data integration, and interdisciplinary collaboration will allow the field to move toward developing global forecasts of lake ice cover for small to large lakes across broad spatial and temporal scales, quantifying ice quality and ice thickness, moving from binary to continuous ice records, and determining how winter ice conditions and quality impact ecosystem processes in lakes over winter. Ultimately, integrating disciplines will improve our ability to understand the impacts of changing winters on lake ice. Plain Language Summary: Lakes are experiencing accelerated rates of warming, including shorter seasonal duration of ice cover, later ice‐on, earlier ice‐off, and in some years no ice cover at all. Lake ice has been historically studied independently by four subdisciplines: observations by in situ and remote sensing scientists, controlled mesocosm experiments by limnologists, and process‐based models by physical modelers. Here, we highlight opportunities for collaboration between disciplines and provide guidelines to successfully integrate the disciplines to tackle the most urgent questions surrounding lake ice loss in warming climates. Key Points: Ripe opportunity to merge disciplines and harmonize data streams to further understand lake ice dynamics in changing wintersDevelop integrated plans for future data collection by moving from ice cover and phenology to multifaceted descriptions of ice conditionsOperationalize development of skill sets to work in multidisciplinary teams by constructing a workforce and developing a common language [ABSTRACT FROM AUTHOR]

Forests play a significant role in protecting people, settlements in mountainous terrains from hydrogeomorphic hazards, including shallow landslides. Although several studies have investigated the interactions between forests and slope instabilities, a full understanding of them has not yet been obtained. Additionally, models that incorporate forest stand properties into slope failure probability analyses have not been developed. In principle, physical-based models, which are powerful tools for landslide hazard analyses, represent an appropriate approach to linking stand properties and slope stability. However, the reliability of these models depends on numerous parameters that describe highly complex geotechnical and hydrological processes (e.g. potential failure depth, saturation ratio, root reinforcement, etc.) that are difficult to measure and model. In particular, the spatial heterogeneity of root reinforcement remains a problem, and the use of physically based models from a forest management perspective has been limited. This paper presents a procedure for assessing slope stability in terms of the Factor of Safety that accounts for forest stand characteristics such as tree density, average diameter at breast height and minimum distance between trees. The procedure combines a three-dimensional (3D) slope stability model with an evaluation of the variability of root reinforcement in terms of a probability distribution, according to forest characteristics. Monte Carlo simulation is used to account for the residual uncertainties in both stand characteristics and 3D stability model parameters. The proposed method was applied in a subalpine catchment in the Italian Alps, mainly covered by coniferous forest and characterized by steep slopes and high landslide risk. The results suggest that the procedure is highly reliable, according to landslide inventory maps [area under the ROC curve (AUC) is 0.82 and modified success rate (MSR) is 0.70]. Thus, it represents a promising tool for studying the role of root reinforcement in landslide hazard mapping and guiding forest management from a slope stability perspective. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The hot deformation behavior of Ti6242 alloy holding a duplex microstructure (primary α+transformed β) was investigated. This was performed through hot compressing the experimental material at temperatures in the range of 1000 °C to 1100 °C, where the alloy was in its single-phase β region. The hot deformations were conducted under different strain rates of 0.003, 0.03 and 0.3 s −1 up to the true strain of 0.7. The microstructural examinations reveal newly recrystallized grains, which were formed in the vicinity of initial β grain boundaries under all testing conditions. A yield drop phenomenon is observed during straining at the strain rates of 0.003 and 0.03 s −1 . However, a continuous yielding at early stages of deformation is characterized for the case of 0.3 s −1 . In the former condition, the yield drop phenomenon is considered to mask the typical flow softening behavior, which is commonly attributed to the occurrence of dynamic recrystallization (DRX). A numerical approach was employed to determine the effective alloying element in dislocation pinning and yield drop. The results indicate that the hot deformation activation energy at the upper yield points varies between 222 and 301 kJ/mol. The activation energy at lower strain rates is found to be equal to that of Al diffusion in β titanium. The atom distribution maps reveal that the aluminum atoms in the form of atom clusters play an important role in yield drop phenomenon. [ABSTRACT FROM AUTHOR]

We develop a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong interferences (trunks of a forest). These physical models for the MMT and the interferences are integrated in low-rank subspaces and are based on scattering and polarimetric properties. Several images, called subspace SAR images, can be generated and combined considering these subspace models. We then propose a new approach for target detection and interference reduction based on the combination of SAR subspace images. We show that our SAR algorithm outperforms the classical SAR imagery algorithm on both simulated data and real data in the context of foliage penetration detection. [ABSTRACT FROM PUBLISHER]