Compared to traditional biochar (BC), nano-biochar (NBC) boasts superior physicochemical properties, promising extensive applications in agriculture, ecological environments, and beyond. Due to its strong adsorption and migration properties, NBC may carry nutrients or pollutants to deeper soil layers or even groundwater, causing serious environmental risks. Nevertheless, the migration rules and mechanisms of NBC in soil are still unclear. Therefore, this study employed soil column migration experiments to systematically explore the migration rules and mechanisms of NBC under various flow rates, initial soil water contents, soil depths, and soil textures. The results showed that regulated by smaller particle size differences and greater surface charges, NBC exhibited a stronger migration ability compared with traditional BC. As the soil texture transitioned from fine to coarse, the migration capability of NBC significantly improved, driven by both pore structure and interaction forces as described by the DLVO theory. The migration ability of NBC was also greatly boosted as the soil transitioned from saturated to unsaturated conditions, primarily because of preferential flow. When the flow rate increased from 70% KS to 100% KS and 130% KS, the migration ability of NBC also increased accordingly, as changes in injection flow rates altered the velocity distribution of pore water. NBC in 25 cm soil columns was more prone to shallow retention compared with 10 cm soil columns, resulting in weaker overall migration ability. In addition, through fitting of the two-site kinetic model and related parameters, the penetration curves of NBC under various variable conditions were effectively characterized. These findings could offer valuable insights for NBC’s future efficient, rational, and sustainable utilization, facilitating the evaluation and mitigation of its potential environmental risks.

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km² grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.

Accurate mapping of soil total nitrogen (STN) is fundamental for advancing sustainable and precision soil management. While digital soil mapping (DSM) has increasingly relied on Earth observation (EO) data, the potential of various synthetic aperture radar (SAR) features, particularly interferometric coherence and texture, remains underexplored for large-scale STN prediction. This study aimed to systematically evaluate the potential of multiple Sentinel-1 SAR-derived features, including backscatter coefficients, interferometric coherence, and texture metrics, for modeling and mapping STN across the Iberian Peninsula. We integrated 4296 soil samples from the 2018 LUCAS dataset with multi-source environmental covariates processed via the Google Earth Engine (GEE) platform. Nine modeling scenarios were designed to compare individual and combined contributions of Sentinel-1, Sentinel-2, topographic, and climatic variables using random forest (RF) and extreme gradient boosting (XGBoost) algorithms. The results indicated that the selection of SAR-derived features significantly influences prediction accuracy. Among individual Sentinel-1 feature groups, texture metrics and interferometric coherence outperformed the traditionally used backscatter coefficients, emphasizing their effectiveness in STN mapping. Specifically, texture-based and coherence-based models achieved R² values of 0.34 to 0.35 and 0.33, respectively, whereas backscatter-only models yielded the lowest accuracy (R² = 0.29 to 0.30). The integration of all three radar categories substantially improved performance (R² = 0.39 to 0.42), surpassing the performance of models based solely on Sentinel-2 optical data (R² = 0.33 to 0.34). The most comprehensive model, which combined multi-source EO data with topographic and climatic variables, achieved the highest overall accuracy with R² values of 0.51 for RF and 0.52 for XGBoost. Variable importance analysis confirmed that satellite-derived variables were the most influential group. Spatial predictions successfully captured the heterogeneity of STN across the peninsula, with higher concentrations in humid, mountainous regions and lower values in arid central plateaus and southern regions. This study demonstrates that integrating diverse Sentinel-1 radar information, particularly coherence and texture, provides a robust alternative or complement to optical data, offering a powerful tool for large-scale soil property mapping.

Accurate spatial information on soil pH is essential for soil management, agricultural decision-making, and ecosystem assessment. Although Earth observation (EO) data have played an increasingly important role in digital soil mapping (DSM), most studies have relied mainly on optical imagery, while synthetic aperture radar (SAR) information, especially interferometric coherence, remains underutilized for soil pH prediction. This study explored the value of Sentinel-1-derived interferometric coherence and backscatter images, Sentinel-2 optical imagery, and topographic–climatic variables for national-scale mapping of soil pH across Spain. Models were developed using random forest (RF) and boosted regression trees (BRT) with 3867 LUCAS 2018 topsoil samples under 11 prediction scenarios representing different radar configurations, radar-derived feature types, and multi-source data integration strategies. VH backscatter performed better than VV backscatter, while combining backscatter from both polarizations and both orbit directions further improved performance within the backscatter-only group. When different predictor groups were used separately, coherence images achieved R² values of 0.49–0.52, outperforming all other individual predictor groups. Under BRT, adding coherence to backscatter increased R² from 0.45 to 0.56 for pH in CaCl₂ and from 0.46 to 0.57 for pH in H₂O, and the further inclusion of Sentinel-2 optical imagery slightly improved performance. The best performance was achieved by integrating all satellite-derived variables with topographic and climatic predictors, with R² values of 0.62 for both pH in CaCl₂ and pH in H₂O under BRT. Variable importance analysis further identified coherence as the most influential predictor group within the evaluated predictor set, with short-temporal-baseline coherence features ranking highest. The predicted maps revealed clear spatial heterogeneity, with lower pH values mainly in northern and northwestern Spain and higher values in central and southeastern regions. These findings demonstrate the added value of Sentinel-1 interferometric coherence for national-scale soil pH mapping.

While the application of Synthetic Aperture Radar (SAR) in soil pH prediction has been explored, its predictive capacity has been underestimated because the radar indicators employed are mostly mathematical combinations of backscatter observations. We combined soil pH observations from the LUCAS 2018 topsoil dataset across the Iberian Peninsula with Sentinel-1 backscatter, texture, and interferometric coherence features to develop random forest models and compared their performance with models based on Sentinel-2 optical data. Coherence values decreased and spatial details diminished as the temporal baseline increased, while shorter baselines provided better modeling performance. Models incorporating multiple temporal baselines outperformed those using a single baseline. Among models using a single type of radar information, coherence-based models incorporating all temporal baselines achieved the highest accuracy (R² = 0.54–0.55), followed by texture-based models, while backscatter-based models performed the poorest (R² = 0.46–0.48). Notably, most radar-based models achieved higher accuracy than those using Sentinel-2 optical data alone (R² = 0.44–0.46). Integrating diverse Sentinel-1 radar features further improved prediction accuracy, achieving R² = 0.59 for pH in H₂O and R² = 0.58 for pH in CaCl₂, while adding Sentinel-2 data only slightly enhanced accuracy. Coherence was the most influential variable group, particularly at short temporal baselines. These results highlight the substantial potential of diverse Sentinel-1 radar information for soil pH mapping.

Forest Land Use Intensity (F-LUI) is a multidimensional and dynamic property of forest ecosystems that cannot be directly observed by Remote Sensing (RS). Existing approaches commonly quantify individual management activities, forest attributes or ecological responses and therefore capture only specific dimensions of forest-use intensity. This concept-driven narrative review proposes a trait-based conceptual framework in which forest traits constitute the common observational interface linking anthropogenic forest use, ecosystem responses and RS observations. Based on this principle forest traits and their spatial, temporal and functional expressions are interpreted within six complementary F-LUI indicator families: Trait, Genesis, Structure, Taxonomy, Function and Socio-economics. Rather than defining a universal F-LUI index, the framework provides a multidimensional, sensor-independent architecture for integrating RS with in situ observations, forest inventories, management information and environmental data. Emerging approaches including Artificial Intelligence (AI) and Foundation Models, multi-sensor data fusion, semantic technologies, Knowledge Graphs and Digital Twins may support its operationalisation. Key challenges remain in standardisation, uncertainty quantification, attribution of management effects and transferability across forest types, management regimes and spatial and temporal scales. The proposed framework provides an open and extensible basis for harmonised, reproducible and operational trait-based monitoring of F-LUI.