Soil moisture detection research, which influences crop growth, land use, and soil erosion, is receiving significant attention. This study proposes a nondestructive, integrated ultrawideband (UWB)-based framework for soil moisture measurement and prediction. The method utilizes a UWB-loaded unmanned aerial vehicle (UAV) to gather radar echo data, circumventing soil damage issues inherent in current research and equipment. We first employ time-frequency analysis methods to convert the echo signals into 2-D spectrograms, constructing datasets labeled with soil moisture. Then, a trained neural network is used to predict the soil moisture at single point. Additionally, a novel interpolation method is proposed to enhance prediction accuracy (ACC) for the ridge-furrow structure of farmland. The experimental results demonstrate that the proposed algorithm achieves a soil moisture measurement ACC of 98% in both vegetated and nonvegetated conditions, indicating strong robustness. In terms of moisture distribution prediction, the mean squared error (mse) of soil moisture spatial distribution prediction is reduced by 42% compared to traditional methods. Therefore, this system provides technical support for efficient, large-scale, and nondestructive soil information collection.

Mapping superficial and subsurface conditions play an important role in analysis and design of geotechnical structures and facilities. The mechanical parameters of sandy clays soil have significant spatial variability or heterogeneity due to the complex deposition process of soil, leading to the high uncertainty of the quantifications of its parameters. Therefore, understanding the spatial variability of the parameters is an important approach to reduce uncertainty. This paper deals with the characterization of spatial variability of soil interface in Japoma-Cameroon, using standard penetration test, cone penetration tests and pressuremeter test data. The vertical and horizontal variability was taken into account, and the data are analyzed within a heterogeneous layer using Bayesian kriging and random field theory, probability density function, coefficient of variation (COV) and scale of fluctuation (theta v). The study reveals that lognormal and normal distributions, respectively, fit the histograms of NSPT, qc, Em and Pl, with all parameters increasing with depth. The COV values, ranging from 30.10 to 88.75%, reflect significant site heterogeneity. Parameters N, qc and Em are adjusted by the exponential cosine autocorrelation function, while Pl uses the exponential sinusoidal function. Fluctuation scales theta v values vary from zero to 1.98 m vertically, with horizontal scales extending up to hundreds of meters, indicating the influence of geological anisotropy. Semivariograms show limitations in small-scale structure capture, potentially overestimating the nugget effect. The exponential model's predictions are reliable for site microzonation, subsoil bearing capacity analysis and other geotechnical parameters based on NSPT.

Watery strata and the influence of pore water pressure cannot be ignored when calculating the deformation of existing tunnels induced by the excavation of new undercrossing tunnels. Many parameters can affect the deformation of existing tunnels during the excavation of a new undercrossing tunnel. In this work, an optimized method was developed for calculating the settlement of an existing tunnel undercrossed by a newly excavated tunnel in water-rich strata. This method includes a deterministic calculation model and a probability analysis model. Based on the constitutive behavior of the soil and the poroelasticity theory, the excess pore water pressure at the axis of the existing tunnel was obtained and used in the deterministic calculation model, which computes the deformation of the existing tunnel. In addition, we established a probability model based on Kriging metamodeling, the Latin Hypercube sampling (LHS) and Monte Carlo sampling (MCS) methods, and conducted global sensitivity analysis (GSA) and failure probability analysis. The optimized parameters can be input into the deterministic model to make more accurate predictions. The optimized method was applied in and validated by a metro project in Beijing.

Erosion causes significant damage to life and nature every year; therefore, controlling erosion is of great importance. Therefore, maintaining the balance between soil, plants, and water plays a vital role in controlling erosion. Aim of this study was to estimate some erodability parameters (structural stability index-SSI, aggregate stability-AS, and erosion ratio-ER) with indices and reflectance obtained via TripleSat satellite imagery using machine learning algorithms (support vector regression-SVR, artificial neural network-ANN, and K-nearest neighbors-KNN) in Samsun Province, Vezirkopru, Turkiye. Various interpolation methods (inverse distance weighting-IDW, radial basis function-RBF, and kriging) were also used to create spatial distribution maps of the study area for observed and predicted values. Estimates were made using NDVI, SAVI, and ASVI indices obtained from satellite images and NIR reflectance. Accordingly, the ANN algorithm yielded the lowest MAE (2.86%), MAPE (9.46%), and highest R2 (0.82) for SSI estimation. For AS and ER estimation, SVR had the highest predictive accuracy. Given the RMSE values in spatial distribution maps for observed and estimated values (SSI 7.861-7.248%, AS 14.485-14.536%, and ER 4.919-3.742%), the highest predictive accuracy was obtained with kriging. Thus, it was concluded that erosion parameters can be successfully estimated with reflectance and index values obtained from satellite images using SVR and ANN algorithms, and low-error distribution maps can be created using the kriging method.

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

Boreal forest and wetland have important influences on the development and protection of the ecosystem-dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi-period land use/land cover (LULC) data and long-time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025 degrees C per yr) and extents of permafrost showed a decreasing trend (-3668 km(2)yr(-1)) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 x 10(4) and 3.8754 x 10(4) km(2), respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km(2) from 1980s to 2010s . Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China.