Smartphone global navigation satellite system (GNSS) positioning in building-obstructed environments is degraded by non-line-of-sight reception and multipath. Visibility-based shadow matching (SM) exploits three-dimensional building models to constrain cross-street position but has limited along-street observability. This study proposes a pedestrian dead reckoning (PDR)-constrained SM framework with two implementations: extended Kalman filter fusion (EKF-PDR/SM) using heading-orthogonal candidate search and anisotropic SM measurement uncertainty, and PDR-driven SM using candidate propagation, visibility scoring, and GNSS-proximity weighting. Evaluation used one smartphone and one pedestrian completing one traversal of each of two campus routes with different satellite-visibility conditions. Under common PDR prediction and process-noise settings, EKF-PDR/SM achieved cross-street/along-street root mean square errors (RMSEs) of 3.57/8.28 m on Route 1 and 2.46/7.83 m on Route 2, compared with 5.23/8.98 m and 4.29/12.38 m for EKF-PDR/GNSS, respectively. PDR-driven SM achieved the lowest directional RMSEs among the six compared methods: 2.92/7.47 m on Route 1 and 1.54/5.89 m on Route 2. All errors were evaluated relative to common model-derived reference trajectories. These controlled results support the use of PDR constraints to improve visibility-based SM under the tested conditions.

GNSS coordinate series noise modeling is essential for reliable geophysical signal estimation and uncertainty assessment. The generalized Gauss–Markov (GGM) noise model and the Matérn process (MP) noise model are widely used to describe low-frequency spectral flattening in GNSS coordinate series, but they differ in their definition domains, parameterizations and autocovariance function (ACF) structures. These differences may lead to misconceptions, complicate noise model comparison and practical implementation. Building on a systematic review of the theories of GGM and MP, this study proposes a generalized Matérn process (GMP) noise model. By introducing a fractional step size hyperparameter μ into the differencing operator, GMP provides a unified framework that continuously connects the two models: when μ = 1, GMP reduces to GGM; as μ → 0, the spectrum of GMP approaches that of MP. The preferred range of μ is investigated using 420 GNSS coordinate series from 140 global GNSS sites, considering differences in geographical region, coordinate component and length of observations. The results show that the preferred values of μ are robustly concentrated within the interval [0.7, 1]. Large-scale validation is then conducted using 846 GNSS coordinate series from 282 global GNSS sites. The experimental results show that under AIC, BIC and BIC_tp, the proposed WN + GMP family consistently accounts for a large proportion of the optimal models. These results demonstrate that GMP provides a more general noise model family for GNSS coordinate series noise modeling and can improve the fidelity and flexibility of stochastic noise modeling.

Annual cropland data are widely used to delimit agricultural land for soil organic carbon (SOC) mapping and spatial aggregation, yet mapped cropland may not coincide with land under agricultural use at soil sampling. Such a mismatch changes the land entering aggregation and may alter both the aggregated mean of predicted SOC and the agricultural land it represents. This difference has rarely been quantified with the SOC prediction surface held fixed. At Xinglongzhuang Coal Mine in eastern China, an agricultural sampling frame established before fieldwork was verified during March 2022 field sampling and finalized as the sampling-period agricultural-use extent (Full) for comparison with Esri 2022 Crops. Within Full, Esri-covered land was Covered and the remainder Uncovered. A random forest fitted to 155 model-development sites generated the SOC prediction surface; it was independently evaluated using 60 probability-sampled validation sites, while area of applicability (AOA) analysis assessed predictor-space support. Uncovered comprised 25.25% of Full; 86.47% occurred >10 m from the Full boundary and 97.84% was within the AOA. Independent validation yielded a root mean square error (RMSE) of 2.272 g kg⁻¹. Mean predicted SOC was 14.338 g kg⁻¹ in Covered and 8.732 g kg⁻¹ in Uncovered; Full averaged 12.922 g kg⁻¹, 1.415 g kg⁻¹ (9.9%) lower than Covered. A design-based residual correction gave a Full–Covered contrast of −1.592 g kg⁻¹ (95% confidence interval: −2.036 to −1.147 g kg⁻¹). The Full–Covered difference equals the Uncovered area proportion multiplied by the mean predicted SOC difference between Uncovered and Covered. Across the three reporting zones, these two components ranked differently, so Uncovered area proportion alone did not track the magnitude of the aggregation difference. With the prediction surface fixed, including Uncovered land with lower predicted SOC reduced the mean for Full relative to Covered. The reported mean is therefore tied to the agricultural extent used for aggregation and, in changing landscapes, to the period represented by that extent.

To mitigate the impact of north–south strip errors inherent in Gravity Recovery and Climate Experiment (GRACE) spherical harmonic coefficient solutions, this research develops a state-space model to generate a more robust solution. The efficacy of the state-space model is demonstrated by comparing its performance with that of conventional filtering methods and hydrological modeling schemes. The method is subsequently applied to estimate the GRACE Groundwater Drought Index in the California Central Valley basin, a region significantly affected by drought during the GRACE observation period. This analysis quantifies the severity of droughts and floods while investigating the direct influences of precipitation, runoff, evaporation, and anthropogenic activities. By incorporating the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation, the study offers a detailed causal analysis and proposes a novel methodology for water resource management and disaster early warning. The results indicate that a moderate-duration flood event in 2006 resulted in a recharge of 19.81 km³ of water resources in the California Central Valley basin, whereas prolonged droughts in 2008 and 2013, lasting over 15 months, led to groundwater depletion of 41.53 km³ and 91.45 km³, respectively. Precipitation and runoff are identified as the primary determinants of local drought and flood conditions. The occurrence of ENSO events correlates with sustained precipitation variations over the subsequent 2–3 months, resulting in corresponding changes in groundwater storage.