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.

Potassium (K) deficiency is a widespread constraint on maize production, yet how it influences the suberized apoplastic barriers that control radial K transport in roots, and whether the endodermis and the constitutively suberized exodermis respond differently, has remained unclear. We grew maize under K deficiency in soil and across a hydroponic K gradient, with abscisic acid (ABA) and fluridone treatments, and analysed seminal roots by Fluorol Yellow 088 staining, tissue-resolved suberin chemistry of endodermis and exodermis, rubidium (Rb⁺) flux, and RNA-sequencing with weighted gene co-expression network analysis (WGCNA). K deficiency selectively increased endodermal aliphatic suberin and eliminated endodermal passage cells in soil-grown roots, whereas exodermal suberin was unchanged. Although K⁺ influx remained high, root-to-shoot Rb⁺ translocation fell sharply, consistent with K retention. A single co-expression module linked the suberin-biosynthetic genes with several K transport genes, and the suberin programme responded to exogenous ABA, indicating ABA-dependent co-regulation. Soil and hydroponic systems converged on the same endodermis-specific anatomical response. Endodermal, but not exodermal, barrier reinforcement limits K leakage from the stele in maize roots. This tissue-specific plasticity identifies the endodermal suberin biosynthesis programme, and its coordination with K⁺ uptake, as a target for improving K use efficiency. Highlight: Potassium deficiency reinforces the endodermal but not the exodermal suberin barrier in maize roots, restricting root-to-shoot K translocation through an ABA-dependent programme co-regulated with K transport.