This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

Rainfall-induced landslides are widely distributed in many countries. Rainfall impacts the hydraulic dynamics of groundwater and, therefore, slope stability. We derive an analytical solution of slope stability considering effective rainfall based on the Richards equation. We define effective rainfall as the total volume of rainfall stored within a given range of the unsaturated zone during rainfall events. The slope stability at the depth of interest is provided as a function of effective rainfall. The validity of analytical solutions of system states related to effective rainfall, for infinite slopes of a granite residual soil, is verified by comparing them with the corresponding numerical solutions. Additionally, three approaches to global sensitivity analysis are used to compute the sensitivity of the slope stability to a variety of factors of interest. These factors are the reciprocal of the air-entry value of the soil alpha, the thickness of the unsaturated zone L, the cohesion of soil c, the internal friction angle phi related to the effective normal stress, the slope angle beta, the unit weights of soil particles gamma(s), and the saturated hydraulic conductivity K-s. The results show the following: (1) The analytical solutions are accurate in terms of the relative differences between the analytical and the numerical solutions, which are within 5.00% when considering the latter as references. (2) The temporal evolutions of the shear strength of soil can be sequentially characterized as four periods: (i) strength improvement due to the increasing weight of soil caused by rainfall infiltration, (ii) strength reduction controlled by the increasing pore water pressure, (iii) strength reduction due to the effect of hydrostatic pressure in the transient saturation zone, and (iv) stable strength when all the soil is saturated. (3) The large alpha corresponds to high effective rainfall. (4) The factors ranked in descending order of sensitivity are as follows: alpha > L > c > beta > gamma(s) > K-s > phi.

The structural integrity of buried pipelines is threatened by the effects of Permanent Ground Deformation (PGD), resulting from seismic-induced landslides and lateral spreading due to liquefaction, requiring accurate analysis of the system performance. Analytical fragility functions allow us to estimate the likelihood of seismic damage along the pipeline, supporting design engineers and network operators in prioritizing resource allocation for mitigative or remedial measures in spatially distributed lifeline systems. To efficiently and accurately evaluate the seismic fragility of a buried operating steel pipeline under longitudinal PGD, this study develops a new analytical model, accounting for the asymmetric pipeline behavior in tension and compression under varying operational loads. This validated model is further implemented within a fragility function calculation framework based on the Monte Carlo Simulation (MCS), allowing us to efficiently assess the probability of the pipeline exceeding the performance limit states, conditioned to the PGD demand. The evaluated fragility surfaces showed that the probability of the pipeline exceeding the performance criteria increases for larger soil displacements and lengths, as well as cover depths, because of the greater mobilized soil reaction counteracting the pipeline deformation. The performed Global Sensitivity Analysis (GSA) highlighted the influence of the PGD and soil-pipeline interaction parameters, as well as the effect of the service loads on structural performance, requiring proper consideration in pipeline system modeling and design. Overall, the proposed analytical fragility function calculation framework provides a useful methodology for effectively assessing the performance of operating pipelines under longitudinal PGD, quantifying the effect of the uncertain parameters impacting system response.

An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure-foundation-backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation during the construction period. Finite element analyses are conducted, incorporating a transient thermal creep model for concrete and an elasto-plastic consolidation model for the soil. The modified Cam-clay model is employed to describe the elasto-plastic behavior of the soil. Subsequently, global sensitivity analyses are conducted to determine the relative importance of the model parameters on the system's response, using Garson's and partial derivative algorithms based on the backpropagation (BP) neural network. The results indicate that the integrated system exhibits pronounced time-dependent displacement and stress, with dangerous values appearing during specific periods. These values are easily neglected, highlighting the importance of integrated time-dependent analysis. Construction activities, particularly the backfilling process, could cause a sudden change in stress and significantly impact the stress redistribution of the superstructure. Additionally, the mechanical properties of concrete have a significant impact on the stress on the superstructure, while the mechanical properties of the soil control the settlement of the integrated system.

The non-Lambertian surface features varying particle size and discrete distribution, resulting in reflectance to be unevenly distributed in different directions. Mine soil with a high content of coarse particles and non-uniform particle distribution exhibits significant non-Lambertian properties on its surface. Consequently, not only vertical observation of the reflectance spectra but also multi-angle reflectance spectra are related to the physical and chemical properties (e.g. soil organic carbon, moisture content and particle size) of mine soil. Understanding the bidirectional reflectance distribution of mine soil with various particle sizes is essential for accurately estimating soil properties using spectroscopy. Current estimations of soil properties using spectroscopy mainly focus on vertical observations, overlooking the bidirectional reflectance characteristics. This study reports the bidirectional reflectance distribution of mine soil with various particle sizes. Furthermore, the performance of different bidirectional reflectance distribution function (BRDF) models in simulating the bidirectional reflectance of mine soil with various particle sizes was evaluated. Soil samples from three typical mine areas were collected and sieved into seven particle sizes ranging from 25 to 3500 mu m. The bidirectional reflectance in the Vis-NIR wavelength region was measured in a laboratory using the Northeastern University bidirectional reflectance measurement system. The performance of five BRDF models (isotropic multiple scattering approximation, anisotropic multiple scattering approximation, H2008, H2012 and SOILSPECT) in modelling the bidirectional reflectance distribution of mine soil with different particle sizes was compared. Sobol's sensitivity indices were used to quantify the contributions of the parameters in the BRDF models. The results showed that (1) small mine soil particles (25 mu m) exhibited greater reflectance than large particles (3500 mu m). Large particles (3500 mu m) exhibited backward scattering, whereas small particles (25 mu m) exhibited extremely forward scattering characteristics because of the high silicon dioxide content; (2) the SOILSPECT model outperformed the other BRDF models in simulating the bidirectional reflectance of mine soil and had the smallest root mean square error (0.004-0.04); (3) the single-scattering albedo (omega) parameter had the greatest contribution in the SOILSPECT model. Four parameters in the phase function (b, b ', c and c ') effectively indicated the scattering behaviour of mine soil with different particle sizes. These findings improve our understanding of the scattering characteristics of mine soil with various particle sizes and can be used to improve the accuracy of extracting particle size and other soil properties from mine soil.