Range Of Soil Moisture Research Articles

Surface Soil Moisture Retrievals Under Forest Canopy for $L$-Band SAR Observations Across a Wide Range of Incidence Angles by Inverting a Physical Scattering Model

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

An Optimal Irrigation Scheduling for Drip Irrigated Onion in A- Semi-Arid Region Using the Computer Program CROPWAT 8.0

Efficient utilization of available water resources requires appropriate management strategies considering the changing environmental conditions. The present study used a widely adopted crop water requirement estimation model-CROPWAT 8.0 for estimation and scheduling of irrigation requirement for onion crop grown under Vertisol in the Rabi season in the semi-arid region of Raichur district. The soil moisture at the root zone was not allowed to fall below 50% depletion. The irrigation events brought the soil moisture back to the field capacity level. The total water requirement for the 1st and 2nd seasons was 428.77 mm and 399.98 mm respectively at 90% irrigation efficiency. CROPWAT based two days irrigation scheduling scenario was found to be appropriate to maintain optimal soil moisture range within the crop root zone at different crop stages.

Analysis and Modeling of the Complex Dielectric Constant of Bound Water with Application in Soil Microwave Remote Sensing

Complex dielectric constant (CDC) of bound water determines the accuracy of the complex dielectric constant of wet soil. According to electrical double-layer structure and dielectric properties, the bound water on clay particle surface is divided into strongly bound water and weakly bound water. Based on this classification, models for the complex dielectric constants of bound water and soil are established taking into consideration factors such as temperature, moisture, texture, and microwave frequency. The results show that the fundamental reason why the complex dielectric constant of bound water is between that of ice and free water is the adsorption force which forms the electrical double-layer structure on the surface of clay particles. Low-concentration cationic solution could exist in free soil water and was found as the reason for the higher salinity and conductivity of free soil water, as compared to the measured soil solution. Results of soil CDC model are in good agreement with measured data across a wide range of microwave frequencies and soil temperature, moisture, and texture. The absolute root mean square error analysis also shows that the soil CDC model in this paper compared to the other models is more accurate.

Plant architecture evolution for higher yields in cluster bean (Cyamopsis tetragonoloba) under arid conditions

Cluster bean [Cyamopsis tetragonoloba (L.) Taub] is an annual legume well adapted to limiting soil moisture and wide range of temperature fluctuations. In order to realize the potential of the crop, systematic cluster bean improvement efforts in India were initiated since 1961. Early released varieties were identified through multilocation germplasm testing. Selections, hybridization and mutagenesis have been contributing to recent varieties with more shares emanating from hybridization. Some of the earlier released varieties are still popular with the farmers. The traits responsible for high yield in guar varieties have not been analyzed and probably resulting in release of similar types with no or marginal gains. Hence, present investigation was undertaken to evaluate popular varieties with respect to seed yield and other associated morpho-physiological traits. Thirty varieties procured from the Agriculture Research Station, Durgapura (SKRAU, Bikaner), CCHAU, Hisar and CAZRI, Jodhpur were evaluated during kharif 2013, 2014 and 2015 at the Central Research Farm, Central Arid Zone Research Institute, Jodhpur, India. The analysis of variance indicated non-significant variation for important yield and yield attributing traits like pods/plant, pod length, seed weight and nodes per plant. The various traits like branches per plant, number of clusters and pods per cluster complemented so as to have comparable number of pods per plant and yield. Most of the contribution to variance comes from environments indicating adaptive plasticity in cluster bean irrespective of varieties. The varieties released meticulously incorporated early partitioning and increased proportion of reproductive phase for high yield.

Changes in Prescribed Fire Frequency Alter Ecosystem Carbon Dynamics

Changes in fire frequency from historical norms are becoming more frequent due to both changes in management and climate change factors. There is uncertainty about whether increasing fire frequency will lead to decreased carbon pools due to shorter inter-fire recovery periods, or increased carbon pools due to lowered fire intensity due to lighter fuel loads. Additionally, data are needed to determine whether plant and soil carbon pools respond similarly and whether ecosystem responses are consistent across environmental gradients that can affect fire intensity, such as soil moisture. We measured soil and vegetation carbon pools and fluxes at sites that had experienced different experimental burn treatments over the previous 8 years and across a range of soil moisture in a longleaf pine (Pinus palustris) ecosystem in North Carolina, USA. We found that increasing fire frequency, assessed by either the number of days since a previous fire or the number of fires a plot had experienced over the previous 8 years, significantly reduced carbon stocks in the litter pool and soil carbon pool and reduced the productivity of understory plants. Total carbon stocks also significantly declined, and there was a marginally significant shift away from soil carbon and toward tree carbon as being the dominant carbon pool in the system with increasing fire. None of the results showed any interaction with soil moisture, suggesting that in this landscape, fire effects are consistent across an important environmental gradient. Over the timeframe of this study, management that increases prescribed fire frequency appears to reduce carbon storage.

Assessing the influence of topsoil and technosol characteristics on plant growth for the green regeneration of urban built sites

Achieving urban regeneration through the creation of new green areas is a widely promoted strategy to improve the quality of life in densely built neighborhoods. "De-sealing" actions can compensate for the creation of new built-up areas, as demonstrated by the EU-funded Life + project ‘Save our Soils for LIFE’ (SOS4LIFE, LIFE15ENV/IT/000225), in which guidelines for de-sealing have been published. For the generation of new urban greening, it is important to know the characteristics of the soils used in order to better define the most appropriate landscaping decisions and management practices. In this study the physical and chemical characteristics of topsoils and technosols (soils enclosed under sealed surfaces) were assessed in relation to growth and leaf gas exchanges in two ornamental species (V. tinus and E. x ebbingei), in two partner municipalities of the project, Carpi and San Lazzaro di Savena (north-east Italy), during a three-year trial. Results of the study confirmed the dependence of plant growth on the chemical evolution of the soils, and identified the optimal soil moisture range based on soil texture and soil-plant water relationships. In addition, the technosols were found to actually be beneficial for plant growth, due to their high drainage capacity and nutrient content.

Irrigation scheduling technologies reduce water use and maintain turfgrass quality

AbstractSmart irrigation controllers have demonstrated potential for turfgrass water conservation in humid and temperate environments but have not been comprehensively tested in arid environments. The objective of this study was to determine the accuracy of a wireless capacitance sensor over a wide soil moisture range and to ascertain if smart irrigation controllers resulted in water savings without reducing quality of tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.] and bermudagrass (Cynodon dactylon L.). A two‐yr study was conducted to compare turfgrass quality, root morphology, and water use of plots irrigated with a constant run time to plots for which irrigation was scheduled using soil moisture sensors (SMS), evapotranspiration (ET) [Climate Logic (CL)] controllers, or 80% of historic ET (ET80) for tall fescue and 60% (ET60) for bermudagrass. Sensors accurately tracked soil moisture up to salinity levels of 4 dS m−1. Turf performance and root morphology were not affected by irrigation treatments for either grass. Compared to tall fescue plots irrigated with constant run time, plots irrigated using ET80 and CL required 38% less water, and SMS plots used 44% less than tall fescue. Scheduling bermudagrass irrigation by ET60, CL, and SMS resulted in a 29, 42, and 39% reduction in water applied compared to constant run time. The majority of water savings was in spring and fall. Water requirement for bermudagrass during the summer did not differ between the scheduling treatments. Our study confirms that smart irrigation controllers can be used as an effective measure to conserve water in an arid environment.

Improving the Horton infiltration equation by considering soil moisture variation

Soil water infiltration simulation is a subject receiving great interest in hydrological cycle modelling. The traditional Horton equation is based on the curve of infiltration capacity-rainfall duration time. However, the infiltration process is directly affected by soil moisture content, rather than rainfall duration. The objective of this study was to determine a relationship between infiltration capacity and soil moisture content in order to improve the Horton infiltration equation. Artificial rainfall-infiltration experiments were used to determine a series of power functions. The improved equation was cross-validated with observations from 32 experiments of multiple rainfall intensities and antecedent soil moisture. The simulation performance and uncertainty of the improved equation were compared with those of the original Horton equation to verify its accuracy. The results showed that infiltration rate decreases nonlinearly as soil moisture increases, and finally approaches a stable infiltration rate when the soil is saturated. Overland flow simulations by the improved Horton equation closely matched the observations from all 32 experiments over a soil moisture range of 0.222–0.349 m3/m3. The simulation performance was rated as good for most of the experiments for both the calibration and validation data sets. Compared with the original Horton equation, the simulation performance of the improved equation clearly improved estimation of infiltration, particularly as quantified by the Nash-Sutcliffe efficiency coefficient (NSE) and the coefficient of determination (R2). The number of simulations with NSE values greater than 0.65 increased 11.59% and 2.50% for the calibration and validation data sets, respectively. The number of simulations with R2 values greater than 0.90 increased 31.14% and 22.50%, respectively. The uncertainty intervals of the improved Horton equation became a little greater than those of the original Horton equation. For all 32 experimental simulations, the average relative length of the uncertainty interval at the 95% confidence level increased from 40.52% with the original Horton equation to 49.17% with the improved Horton equation. The number of observations falling within the 95% confidence interval increased from 92.13% to 95.94% with the improved Horton equation. Most of the observations were accurately simulated using the improved Horton equation. The greatest improvements in simulating overland flow were seen for the experiments with low flow simulations. The study results provide insights into soil infiltration mechanisms, and also provide references to support improved infiltration simulation by considering soil moisture variation.

Soil moisture experiment in the Luan River supporting new satellite mission opportunities

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm–5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval.

Controls of Temporal Variations on Soil Respiration in a Tropical Lowland Rainforest in Hainan Island, China

Soil respiration represents the largest carbon (C) flux from terrestrial ecosystems to the atmosphere. We created a study site in tropical lowland rainforest and used static chamber method to measure the temporal variations of soil respiration and their relationship with environmental factors at monthly time scale. The temporal variations of soil respiration showed a seasonal pattern related to soil temperature (p < .01) and soil moisture (p < .05). We tested different regression models to explore the relationship between soil respiration and environmental factors. Soil respiration had a better fit with soil temperature than with soil moisture in single-factor models. At different temperatures, the Q10 values from different models changed in rather different ways. We found that the mixed quadratic model composite of soil temperature and moisture had the best-fitting effect (R2 = .74) on soil respiration and could better explain the seasonal variation. In a certain soil moisture range close to 15%, soil respiration increased with soil temperature. However, soil respiration became restricted when the moisture was greatly higher or lower than this value. Furthermore, at low soil temperatures (lower than 16°C), higher soil moisture could decrease soil respiration rapidly. Thus, soil respiration in a tropical lowland rainforest is co-controlled by soil temperature and moisture. This study expands our observations of soil respiration in tropical forests and how it responds to environmental factors, which is important for reducing errors in evaluation and scaling up of soil carbon flux in climate change studies.

DEVELOPMENT EXPERIENCE AND WAYS OF IMPROVEMENT OF IRRIGATION MANAGEMENT SYSTEMS

The paper provides an overview of models and software used in decision support systems in irrigation. The models of biomass accumulation or evapotranspiration are the base of decision support systems in irrigation. The overview of the most famous systems is given, as well as an innovative irrigation control system "Irrigation online" is presented. &#x0D; The objective of the work is to share the experience of development and implementation of irrigation management systems and outline the ways of their improvement.&#x0D; The "Irrigation online" system consists of hardware and software components. The part of the system's hardware is located in the field consisting of iMetos or Davis weather stations, as well as of own-developed equipment. The software part, intended for storing, processing and providing recommendations, is hosted and run on a server. It sends the recommendations about start watering and necessary irrigation rates to a user’s computer or mobile device.&#x0D; The system is based on modelling of moisture transfer, automated measurements of soil moisture and meteorological indicators in the field and weather data from automated forecast web-sites. Water retention curve of soil and the dependence of the moisture transfer coefficient on the head, which are the input parameters of the model, are given for every layer according to the van Genuchten-Mualem Model.&#x0D; The application of the system took place in 2019 in SE EF“Askaniiske” Kherson region and LLC “APC “Mais” in Cherkasy region. The system "Irrigation Online" provided the recommendations on watering winter rape, wheat, corn, soybeans, alfalfa and potatoes. &#x0D; The system provided the recommendations on watering winter rape, wheat, corn, soybeans, alfalfa and potatoes. It was specified that the use of the system "Irrigation Online" enables to schedule irrigation regimes, the implementation of which requires watering with less (by 15-25%) in comparison with the current irrigation rates, due to which more favourable conditions for the maximum realization of crop varieties and hybrids potential are created. It is accompanied by enhancing the environmental safety of irrigation as a result of minimization of irrigation water losses for infiltration.&#x0D; Irrigation control system "Irrigation Online" uses a range of soil moisture suction pressure rather than a soil moisture range as an optimum moisture supply range for plants. For setting up irrigation terms and rates, the value of suction pressure, which corresponds to the part of water field capacity when it is determined by water retention curve of soil, is taken. The pre-irrigation threshold of suction pressure is the value, which at non-irrigation for some short period will not cause water stress for plants &#x0D; Monitoring of meteorological parameters and soil moisture level in the "Irrigation Online" system allows daily adjusting irrigation terms and rates for next 5 day period and significantly improves the accuracy of their forecasting.

Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing.

On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity.

Study of the relation between soil erodibility and hydrological characteristics

Hydrological characteristics such as soil water retention curve and hydraulic conductivity, for which, however, there are no standardized measurement methods for the entire range of soil moisture, are most important for assessing the erodibility of soil. In the article, based on the energy approach to soil erodibility analysis, a relation was proposed linking the characteristic of soil erodibility, – the specific power spent on the destruction and removal of soil in the places of its natural occurrence, with soil hydrological characteristics. Modeling erosion processes based on the proposed ratio may allow both describing the erosion properties depending on the initial soil moisture and obtaining numerical information on the soil hydrological characteristics using measured values of soil erodibility characteristics at any time during the vegetation period.

Soil Water Infiltration Model for Sprinkler Irrigation Control Strategy: A Case for Tea Plantation in Yangtze River Region

The sprinkler irrigation method is widely applied in tea farms in the Yangtze River region, China, which is the most famous tea production area. Knowledge of the optimal irrigation time for the sprinkler irrigation system is vital for making the soil moisture range consistent with the root boundary to attain higher yield and water use efficiency. In this study, we investigated the characteristics of soil water infiltration and redistribution under the irrigation water applications rates of 4 mm/h, 6 mm/h, and 8 mm/h, and the slope gradients of 0°, 5°, and 15°. A new soil water infiltration model was established based on water application rate and slope gradient. Infiltration experimental results showed that soil water infiltration rate increased with the application rate when the slope gradient remained constant. Meanwhile, it decreased with the increase in slope gradient at a constant water application rate. In the process of water redistribution, the increment of volumetric water content (VWC) increased at a depth of 10 cm as the water application rate increased, which affected the ultimate infiltration depth. When the slope gradient was constant, a lower water application rate extended the irrigation time, but increased the ultimate infiltration depth. At a constant water application rate, the infiltration depth increased with the increase in slope gradient. As the results showed in the infiltration model validation experiments, the infiltration depths measured were 38.8 cm and 41.1 cm. The relative errors between measured infiltration depth and expected value were 3.1% and 2.7%, respectively, which met the requirement of the soil moisture range consistent with the root boundary. Therefore, this model could be used to determine the optimal irrigation time for developing a sprinkler irrigation control strategy for tea fields in the Yangtze River region.

Rainfall frequency, not quantity, controls isopod effect on litter decomposition

Increasing climate variability is one of the dominant components of climate change, resulting particularly in altered rainfall patterns. Yet, the consequences of rainfall variability on biogeochemical processes that contribute to greenhouse gas emissions has received far less attention than have changes in long-term mean rainfall. In particular, it remains unclear how leaf litter decomposition responds to changes in rainfall frequency compared to changes in cumulative rainfall quantity, and if changes in rainfall patterns will differentially affect organisms in the decomposer food web (e.g., microbial decomposers that break down leaf litter through saprotrophic processes versus detritivores that directly ingest leaf litter). To address this knowledge gap, we disentangled the relative importance of cumulative rainfall quantity and rainfall frequency on both microbial- and detritivore-driven litter decomposition, using the isopod Armadillidium vulgare as a model macro-detritivore species and simulating rainfall in a full-factorial microcosm experiment. We found that microbially-driven decomposition was positively related to cumulative rainfall quantity, but tended to saturate with increasing cumulative rainfall quantity when rainfall events were large and infrequent. This saturation appeared to result from two mechanisms. First, at high level of cumulative rainfall quantity, large and infrequent rainfall events induce lower litter moisture compared to smaller but more frequent ones. Second, microbial activity saturated with increasing litter moisture, suggesting that water was no longer limiting. In contrast, isopod-driven decomposition was unaffected by cumulative rainfall quantity, but was strongly controlled by the rainfall frequency, with higher isopod-driven decomposition at low rainfall frequency. We found that isopod-driven decomposition responded positively to an increase in the weekly range of soil moisture and not to mean soil or litter moisture, suggesting that an alternation of dry and moist conditions enhances detritivore activity. Collectively, our results suggest that A. vulgare morphological and behavioral characteristics may reduce its sensitivity to varying moisture conditions relative to microbial decomposers. We conclude that the activity of microorganisms and isopods are controlled by distinct aspects of rainfall patterns. Consequently, altered rainfall patterns may change the relative contribution of microbial decomposers and detritivores to litter decomposition.

Effect of Environmental Factors on the Abundance of Culturable Nitrate Reducing/Denitrifying Bacteria from Contaminated and Uncontaminated Tallgrass Prairie Soil

Various environmental factors have been proposed, such as soil moisture levels, carbon, and nitrate sources to affect the abundance of nitrate reducing (NR) and denitrifying (DN) bacteria. In this study, the strength of the association of the abundance of NR and DN bacteria with various environmental factors is estimated using multivariate statistics. Soil samples were collected from tallgrass prairie soils that had been contaminated with crude oil or brine (e.g. salt water) up to 10 years previously and from parallel uncontaminated sites. The abundance of culturable NR and DN bacteria in the soil samples was estimated by 5-tube MPN method using nitrate broth, while total petroleum hydrocarbons (TPH), sodium chloride, nitrate, and moisture were measured in the contaminated and the parallel uncontaminated sites. Viable heterotrophic bacteria and NR and DN bacteria from all sites were obtained from samples with a broad range of soil moisture (around 10-30% water/g soil) regardless of the source (e.g. site) of the isolates. Our results showed that the abundance of NR and DN bacteria from the contaminated sites was not less than that from the uncontaminated sites and reflected soil moisture, with greater bacterial numbers from wetter soils. Also, current remediation treatments (e.g. nitrate, hay, watering) of contaminated sites sometimes, but not consistently, were associated with greater abundance of NR and DN bacteria. Therefore, no long-term effect of contamination on the abundance of NR and DN bacteria was shown.

ADVANCES IN SOIL MOISTURE RETRIEVAL FROM NEAR-SURFACE MEASUREMENTS USING SATELLITE REMOTE SENSING

Abstract. Soil moisture influences numerous environmental processes occurring over large spatial and temporal scales. It profoundly influences the hydrological and meteorological activity together with climate predictions and hazard analysis. Space-borne sensors are capable of retrieving the surface soil moisture over a region on a regular basis. Latent heat measurements of soil, reflectance based methods, microwave measurements and synergistic approaches are some of the techniques used since long for providing soil moisture estimates over regional and global scales. Due to the dynamic interaction of soil with crops, retrieval of surface soil moisture is always challenging. This paper gives a brief overview of advance in soil moisture retrieval techniques, and an attempt to generate surface soil moisture from fine-resolution satellite remote sensing data. The optical remote sensing explores the linear relationship between land surface reflectance and soil moisture content, and through development of empirical spectral vegetation indices. Another way to estimate soil moisture emerged by measuring amplitude of diurnal temperature, which is closely related to thermal conductivity and heat capacity of soil. Emergence of radiometric satellite measurements at fine resolution has reached at a higher level of technology these days. Microwave remote sensing techniques have a long legacy of providing surface soil moisture estimates with reasonable accuracy. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Passive and Active) missions launched in 2009 and 2015 respectively, are completely dedicated for providing soil moisture at global scale with a spatial resolution of 35 km &amp;amp; 3–40 km. These soil moisture products, however, provides data at highly coarser spatial resolution. The launch of Sentinels gave insight by providing active radar and optical data at higher resolution (∼10 m). Sentinel-1 is the first SAR (Synthetic Aperture Radar) constellation having 6-day revisit time providing data in C-band with dual polarisations. However, no algorithm or methodology is available to generate surface soil moisture product at a finer resolution from dual polarisations. Sentinel-1 data has been used to generate regional surface soil moisture image through modelling. The same has been also used for generating surface soil moisture map of IARI farm at New Delhi. Dubois, a bare surface model, was tested for its suitability for surface soil moisture retrieval of the farm. In addition, radar- based Soil moisture (SM) proxy method was used over Sentinel-1 data for the month of July 2018, and validated through actual surface soil moisture (gravimetric) measurements. Results were satisfactory for a range of 4–16 m3 m−3 of soil moisture, with coefficient of determination (R2) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21–33 m3 m−3), which occurred after the rainfall, the R2 value reduced to 0.22 with larger RMSE. Results suggested that SM-proxy approach might work well for a limited range (drier part) of soil moisture content, and not for the wet soil.

Phenotypic plasticity of floral volatiles in response to increasing drought stress.

Flowers emit a wide range of volatile compounds which can be critically important to interactions with pollinators or herbivores. Yet most studies of how the environment influences plant volatiles focus on leaf emissions, with little known about abiotic sources of variation in floral volatiles. Understanding phenotypic plasticity in floral volatile emissions has become increasingly important with globally increasing temperatures and changes in drought frequency and severity. Here quantitative relationships of floral volatile emissions to soil water content were analysed. Plants of the sub-alpine herb Ipomopsis aggregata and hybrids with its closest congener were subjected to a progressive dry down, mimicking the range of soil moistures experienced in the field. Floral volatiles and leaf gas exchange were measured at four time points during the drought. As the soil dried, floral volatile emissions increased overall and changed in composition, from more 1,3-octadiene and benzyl alcohol to higher representation of some terpenes. Emissions of individual compounds were not linearly related to volumetric water content in the soil. The dominant compound, the monoterpene α-pinene, made up the highest percentage of the scent mixture when soil moisture was intermediate. In contrast, emission of the sesquiterpene (E,E)-α-farnesene accelerated as the drought became more intense. Changes in floral volatiles did not track the time course of changes in photosynthetic rate or stomatal conductance. This study shows responses of specific floral volatile organic compounds to soil moisture. The non-linear responses furthermore suggest that extreme droughts may have impacts that are not predictable from milder droughts. Floral volatiles are likely to change seasonally with early summer droughts in the Rocky Mountains, as well as over years as snowmelt becomes progressively earlier. Changes in water availability may have impacts on plant-animal interactions that are mediated through non-linear changes in floral volatiles.

Impacts of moisture, soil respiration, and agricultural practices on methanogenesis in upland soils as measured with stable isotope pool dilution

Anoxic microsites can alter the habitat of upland soils and host diverse anaerobic processes that affect greenhouse gas production, nitrogen dynamics, and biodiversity. Microsites that are methanogenic indicate deeply reducing conditions that may have especially strong impacts on soil function. However, there have not been controlled studies to determine the regulators of methanogenic microsite formation or persistence and most studies have been limited to tropical or high organic matter soils. We hypothesized that upland methanogenesis, as an indicator of anaerobic activity, is primarily affected by soil moisture and organic matter. To test this hypothesis, we examined relationships between soil properties, rates of methanogenesis, and biogeochemical responses in an incubation experiment that manipulated soil source (semi-arid and mesic ecosystems), agricultural practice (conventional, no-till, and organic), and moisture (10%–95% water-filled porespace) of intact soil cores. Methanogenesis was correlated with factors related to both increased O2 demand (e.g., soil respiration) and decreased O2 diffusion (e.g., water-filled porespace), and the relative importance of these different mechanisms changed over four months. While the highest rates of methanogenesis occurred above 75% water-filled porespace, we observed methanogenesis over the full range of soil moistures. These are the driest soils shown to host methanogenesis, outside of biological soil crusts. Cores from plots with organic amendments had the highest rates of methanogenesis. Comparisons of methanogenesis and N-cycling revealed new relationships in upland soils: stronger methanogenesis was associated with more soil NH4+ and higher N2O emissions but less NO3−, likely due to reduced conditions causing increased denitrification and/or decreased nitrification. Our findings show that upland methanogenesis can arise from either increased O2 demand or decreased O2 diffusion, similar to wetland ecosystems, and that the presence of anoxic microsites appears to alter N-cycling. The current paradigm is that upland anaerobicity is generally a minor or moisture-related event, but we demonstrate here that it can be persistent, occur across the full range of soil moisture, and may result in significant impacts on nutrient availability. These and other anaerobic impacts on soil function and biodiversity may occur over the entire landscape of temperate ecosystems.

Mapping the Salt Content in Soil Profiles using Vis‐NIR Hyperspectral Imaging

Core Ideas Vis‐NIR hyperspectral imaging can be used to predict the soil salt content (SSC) in soil profiles. The least squares support vector machine (LS‐SVM) model predicted SSC more accurately than the partial least squares regression (PLSR) model in the field. Hyperspectral imaging is an efficient and nondestructive method for mapping and characterizing the SSC distribution in soil profiles. Recently, visible and near‐infrared (Vis‐NIR) hyperspectral imaging has shown great potential in fine mapping of soil properties in laboratory. Whether it could be used to predict soil salt content (SSC) in the soil profile under field conditions still remained to be determined. In this study, hyperspectral images were acquired in situ from a soil profile with a Vis‐NIR imaging spectrometer, and the optimum SSC prediction model was built to determine SSC of each pixel, and the fine SSC distribution maps were generated. The observed soil profile was located at an experimental station in Dongtai City, Jiangsu Province, China. Hyperspectral images with a spectral range of 397 to 1018 nm were obtained from 21 to 25 May 2015; a total of 140 soil samples were collected. Five spectral preprocessing methods, Daubechies wavelet (Db), LOG10(1/Db), Savitzky‐Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) were applied, and partial least squares regression (PLSR) and least squares support vector machine (LS‐SVM) models were developed. Results showed that the LS‐SVM model predicted the SSC more accurately than the PLSR model, and the highest prediction accuracy was obtained with LOG10(1/Db) preprocessed spectra with R2p, RMSEp, RPIQ, and RPD values of 0.87, 0.58 g kg–1, 2.60 and 2.77, respectively. Based on the optimum prediction model, the fine distribution of SSC in soil profiles over 5 d were successfully obtained. This study indicated hyperspectral imaging is an efficient and nondestructive method for mapping SSC distribution in soil profiles and characterizing the vertical transportation of soil salt under field conditions with moderate soil moisture range.