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• Traditional methods for soil specific surface area (SSA) are costly and laborious. • We propose models to estimate SSA from a single hygroscopic water content (w h) • Separate SSA models are developed for soil samples grouped by clay mineralogy. • Considering soil clay mineralogy in SSA models significantly improved performance. • Models based on w h were superior to models based on texture, carbon or CEC. The soil specific surface area (SSA) is an important variable for soil science and geoenvironmental engineering applications, but traditional measurement methods are difficult and time-consuming. Regression models or pedotransfer functions are often used to estimate SSA from other soil properties (e.g., clay content and cation exchange capacity), but these models do not consider the impact of clay mineralogy. Hygroscopic water content (w h) is intimately linked to these soil properties, which suggests that w h may be a better parameter for SSA estimation. This study (i) proposes regression models that estimate SSA from w h at different relative humidity values (5 to 90%) for kaolinite-rich samples (KA), illite-rich or mixed clay samples (IL/MC), montmorillonite-rich samples (ML), and a combination of all samples (ALL) and (ii) compares the performance of the w h models to other published models that comprise clay, silt and soil organic carbon contents and cation exchange capacity. We found that the sample-specific w h regression models accurately estimated SSA for KA, IL/MC and ML samples. For KA and IL/MC samples, the performance of the KA model (e.g., for adsorption, average RMSE = 10.5 m2/g) and IL/MC model (average RMSE = 21.3 m2/g) were better than the ALL-calibration model (KA: average RMSE = 18.7 m2/g; ML: average RMSE = 22.4 m2/g). For ML samples, similar model performance between the ML-calibration model (average RMSE = 41.4 m2/g) and the ALL-calibration model (average RMSE = 41.1 m2/g) was observed. In addition, the model performance of regression models based on w h was superior to models published in the literature that are based on clay, silt and soil organic carbon contents and cation exchange capacity. Overall, this study confirms that a single measure of w h can provide reliable estimates of the SSA while revealing a significant impact of clay mineralogy on model performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil sampling methods were assessed using 720 samples at two locations. • Calculated composite sampling decreased significant effects especially in sandy soils. • Sampling in-row resulted in greater crop effect on research results. • Soil enzyme activities required more samples compared to other soil properties. • A Monte Carlo simulation can be used to estimate needed sample size. A wide variety of soil sampling methods have been adopted, yet limited research has addressed how sampling methods can impact research results particularly for spatially variable soil properties. To address these concerns, we intensively sampled two existing soil health trials in Texas for analysis of soil extracellular enzymatic activities (EEAs), permanganate oxidizable C (POXC), soil organic C (SOC), and pH. The data generated was used to quantify the effect of composite sampling, sampling position (in-row vs between-row), and number of samples on statistical results. We compared a Monte Carlo simulation approach with a traditional power analysis to quantify the impact of the number of samples on our results. Calculated composite sampling resulted in lower statistically significant results compared to discrete sampling, demonstrating lower sensitivity of composite sampling. Sampling in-row resulted in greater values specifically for EEAs, pH, POXC, and SOC. Simulation generated estimated number of samples were mainly driven by the effect size and spatial variation. EEAs were highly variable, resulting in a higher estimated number of samples as compared to other soil properties such as POXC and SOC. In our study with two sites, soil with greater clay content resulted in overall lower estimated number of samples compared to a relatively sandy soil, demonstrating a greater sampling requirement for sandy soil. The simulation approach had a weak correlation with traditional power analysis. The simulation predicted lower estimated number of samples when compared to power analysis, which can help save cost in the long run. Overall, factors like composite sampling, sampling in-row vs between-row, and number of samples taken did impact research results. The impact of these sampling strategies on soil parameters of interest needs to be investigated to achieve the most accurate research results in soil science. [ABSTRACT FROM AUTHOR]

• A regular evaluation of satellite soil moisture (SM) products was conducted. • Their accuracies in detecting SM anomalies and seasonality were identified. • l -band products can effectively capture SM seasonality over forest covers. • Products had difficulties in detecting SM variations over organic-rich soils. • AMSR2 showed higher accuracy to detect SM seasonality over arid areas. Satellite-based soil moisture (SM) products exhibit distinct error patterns at various timescales. Knowledge of their accuracy characteristics for capturing different SM signals is significant for many real-world applications in soil sciences. This study conducted a comprehensive evaluation of the following eight satellite-based SM products: the Soil Moisture and Ocean Salinity-Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la BIOsphere version 2 (SMOS-IC-V2), the Level 3 (L3) Soil Moisture Active Passive V7 (SMAP-V7), the combined European Space Agency Climate Change Initiative V06.1 (ESA CCI-V06.1), the L3 SMOS V3, the L3 Advanced Microwave Scanning Radiometer (AMSR2) V3 from the Japan Aerospace Exploration Agency (JAXA), the L3 Land Parameter Retrieval Model (LPRM) C1- and X- band AMSR2 V001, and the Chinese FengYun-3B V1. By using >1,500 sites from the International Soil Moisture Network and triple collocation analysis, this study aimed to identify not only the relative strengths of the products for detecting original SM signals, short-term SM anomalies, and long-term SM seasonality, but also the impacts of land cover, soil organic carbon (SOC), and soil texture on their retrieval accuracy. In terms of capturing the temporal dynamics of SM anomalies, SMAP-V7 outperformed SMOS-IC-V2 and ESA CCI-V06.1. In arid and semi-arid areas, LPRM C1-band AMSR2 V001 outperformed SMOS-ICV2 and SMAP-V7, in terms of detecting SM seasonality. SMAP-V7, SMOS-ICV2, and ESA CCI-V06.1 effectively captured SM seasonality over most forest zones and effectively detected short-term SM anomalies over croplands and grasslands. The l -band products showed the highest accuracy in terms of capturing the temporal dynamics of all the SM signals when the clay fraction was in the range of 20 %–25 %. Most products had difficulties in detecting SM variations over bare deserts, sandy and clay soils, and soils rich in organic matter (SOC content > 8.72 %). [ABSTRACT FROM AUTHOR]

Treated wastewater (TWW) is an important water resource, especially in semiarid and arid regions. However, there are concerns that irrigation with TWW could lead to degradation of soil physical and hydraulic properties. The objective of our study was to determine the effects of long-term (≥ 15 years) irrigation with secondary TWW on some basic and hydraulic soil properties of a clay soil. Undisturbed soil samples (cores) were taken to a depth of 4.5 m (in sections of 0.5 m) over a diagonal cross section of a five year old orchard irrigated with TWW. Samples were taken from five sites within the tree rows (i.e., representing soil directly affected by TWW; referred to as “within rows”) and four sites between the rows of trees (i.e., the control treatment representing soil that was not directly subjected to the irrigation water; referred to as “between rows”). Soil analyses included an array of basic properties, determination of a continuous particle size distribution and measurement of the saturated hydraulic conductivity (HC). The latter two were used for the computation of soil characteristic curve, Θ ( ψ ), and the unsaturated HC curve, K ( ψ ). Similar bulk density, moisture content, cation exchange capacity, pH and exchangeable sodium percentage (ESP) levels were observed for the TWW irrigated samples and the control ones. However, irrigation with TWW caused a significant reduction in the saturated HC, Ks . The computed Θ ( ψ ) curve at a given soil depth, averaged over the different sites, was similar for the TWW-irrigated samples and the control ones. On the contrary, the computed K ( ψ ) curve at a given soil depth, averaged over the different sites, for the TWW-irrigated samples were lower than those for the control samples at matric potential ≳− 100 cm (= pF ≲ 2); similar K ( ψ ) values were noted at pF > 2 for the two treatments. The observed differences in the hydraulic properties between the TWW-irrigated samples and the control ones in this specific matric potential range, albeit the similarity in their ESP, suggest that long term irrigation with TWW affected structural porosity via narrowing macro- and mesopores (> 70 and 30–70 μm, respectively). It is further postulated that this adverse impact of irrigation with TWW on structural porosity might be associated with previously reported effects of TWW on the composition of the dissolved organic matter in the soil solution. [ABSTRACT FROM AUTHOR]