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Our aim was to determine the effect of soil characteristics and root traits on soil aggregate stability at an inter- and intra-site scale in a range of agro-ecosystems. We also evaluated the effect of soil depth and the type of land use on aggregate stability. Soil aggregate stability, soil physicochemical properties and fine root traits were measured along land use gradients (from monocultures to agroforestry systems and forests), at two soil depths at four sites (Mediterranean and tropical climates) with contrasting soils (Andosol, Ferralsol, Leptosol and Fluvisol). Aggregate stability was much lower in deep than in surface soil layers, likely linked to lower soil organic carbon (SOC) and lower root mass density (RMD). Locally, and consistently in all sites, land use intensification degrades soil aggregate stability, mainly in surface soil layers. Soil organic carbon, cation exchange capacity and root traits: water-soluble compounds, lignin and medium root length proportion were the most important drivers of aggregate stability at the inter-site level, whereas SOC, root mass and root length densities (RMD, RLD) were the main drivers at the intra-site level. Overall, the data suggest different controls on soil aggregate stability globally (soil) and locally (roots). Conversion from forests to agricultural land will likely lead to greater C losses through a loss of aggregate stability and increased soil erosion.

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -3) for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m(exp 3) m(exp -3)) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year (exp -1)) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

Inspired by previous studies that have indicated consistent or even well-constrained (relatively low variability) relations among carbon (C), nitrogen (N) and phosphorus (P) in soils, we have endeavored to explore general soil C:N:P ratios in China on a national scale, as well as the changing patterns of these ratios with soil depth, developmental stages and climate; we also attempted to determine if well-constrained C: N:P stoichiometrical ratios exist in China's soil. Based on an inventory data set of 2,384 soil profiles, our analysis indicated that the mean C:N, C:P and N:P ratios for the entire soil depth (as deep as 250 cm for some soil profiles) in China were 11.9, 61 and 5.2, respectively, showing a C: N: P ratio of ~ 60: 5:1. C:N ratios showed relatively small variation among different climatic zones, soil orders, soil depth and weathering stages, while C:P and N:P ratios showed a high spatial heterogeneity and large variations in different climatic zones, soil orders, soil depth and weathering stages. No well-constrained C:N:P ratios were found for the entire soil depth in China. However, for the 0-10 cm organic-rich soil, which has the most active organism-environment interaction, we found a well-constrained C:N ratio (14.4, molar ratio) and relatively consistent C:P (136) and N: P (9.3) ratios, with a general C:N:P ratio of 134:9:1. Finally, we suggested that soil C:N, C:P and N:P ratios in organic-rich topsoil could be a good indicator of soil nutrient status during soil development.

Arid and semi‐arid grassland ecosystems cover about 15% of the global land surface and provide vital soil carbon (C) and nitrogen (N) sequestration. Although half of the soil C and N is stored in deep soils (below 30 cm), no regional‐scale study of microbial properties and their functions through the soil profile has been conducted in these drylands. To explore the distribution and determinants of microbial properties and C and N mineralization rates through soil profile along aridity gradient at a regional scale, we investigated these variables for four soil layers (0–20, 20–40, 40–60 and 60–100 cm) in 132 plots on the Mongolia Plateau. Soil microbial properties (biomass and bacteria:fungi ratio) and C and N mineralization rates decreased with increasing soil depth and aridity at the regional scale. Aridity‐induced declines in soil microbial properties mainly resulted from the negative effects of aridity on ANPP/root biomass and soil organic C (SOC) in the surface soil layers (0–20 and 20–40 cm) but from the direct and indirect (via SOC and soil C/N) negative effects of aridity in the deep soil layers (40–60 and 60–100 cm). Aridity‐induced declines in soil C mineralization rates mainly resulted from the negative indirect effect of aridity on SOC and microbial properties in each soil layer, with weaker effects of SOC and stronger effects of soil microbes in the deep soil layers. Aridity‐induced declines in soil N mineralization rates mainly resulted from the negative indirect effect of aridity on SOC in the three soil layers above 60 cm and mainly resulted from the negative direct effect of aridity in the 60–100 cm soil layer. Aridity via direct or indirect effects strongly determined the patterns of soil microbial properties and C and N mineralization throughout soil profiles on the Mongolian Plateau. These findings suggest that the increases in aridity are likely to induce changes in soil micro‐organisms and their associated functions across soil depths of semi‐arid grasslands, and future models should consider the dynamic interactions between substrates and microbial properties across soil depths in global drylands. A plain language summary is available for this article. Plain Language Summary

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m³ m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m³ m−3 (0.030 m³ m−3) at the 9-km scale and 0.035 m³ m−3 (0.026 m³ m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m³ m−3 (0.032 m³ m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Integrated agricultural production systems with trees, grain crops and forage species are important for land use optimization. However, they can result in non-uniform changes in physical and chemical soil properties. The objective of this work was to evaluate chemical and physical soil properties in a eucalyptus-based agroforestry system. The experiment was conducted in a Red–Yellow Argisol in Southeast Brazil. Eucalyptus (Eucalyptus grandis × E. camoldulensis) seedlings were planted in rows 12.0 m apart, and 2.0 m between plants. For 4 years the inter-row space was cropped to soybeans (Glycine max L. Merrill), Sunn hemp (Crotalaria juncea) and maize (Zea mays L.) in association with palisade grass (Urochloa brizantha). After that, the forage was grazed by beef cattle. Five years after the implementation of the experiment, chemical and physical soil analyses were performed along the profile. Non-uniform changes were observed in fertility and soil physics in the transect between the eucalyptus planting lines, both at the soil surface layers and in depth. Integrated crop/livestock production systems, where eucalyptus is intercropped with annual crops and forage grasses for grazing, results in lower soil fertility near tree lines and up to 100 cm deep over time. Next to the tree line there is an increase in soil compaction and reduced aggregate stability in the uppermost soil layer, while microporosity and soil structuring are increased in the soil deeper layers. These effects are probably due to animal trampling under the trees.

The objectives of this study were to explore the effects of long-term and continued application of fertilizers and manures on microbial biomass, soil biological activity and their seasonal variations in surface and subsurface soils in relation to soil fertility. For this, soils were sampled in spring, summer and autumn from Shenyang Long-term Experimental Station, northeastern China. The results showed that soil total nitrogen (N), organic carbon (C), basal respiration, microbial biomass and enzymatic activity increased in manure-amended surface soils, but decreased with soil depth. Long-term application of inorganic fertilizers significantly decreased soil pH value, sucrase activity and microbial biomass C, but increased soil metabolic quotient (qCO₂). However, no significant effect of inorganic fertilizers on soil total N, urease activity and microbial biomass N was observed in comparison with CK0 (neither tillage nor fertilization) and CK (no fertilizers). There was no significant difference between CK0 and CK in soil total N, organic C and microbial activity in surface soil layer (0-20 cm), but these parameters in subsurface soil layer (20-40 cm) were higher in CK than in CK0. Moreover, seasonal changes were observed in terms of soil nutrient contents, enzymatic activity, microbial biomass and soil respiration. There were significant correlations between soil microbial biomass C and N, between organic C and sucrase activity and between total N and urease activity, respectively. It is recommended that combined use of organic manure with inorganic fertilizers should be considered to maintain higher microbial biomass, soil biological activity and soil fertility. Considering considerably high nutrients reserve and microbial activity in subsurface layers of soil and wind-erosion-caused nutrient loss in spring in north China, we also propose that low tillage should be considered to make use of nutrients in soils.

Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes.Wetter soils aremore prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soilmoisture on a global scale. Oneway to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.