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This work proposes a complementary concentric open-ring microwave resonator sensor for accurate sensing of the electrical properties of soil and its moisture content. The device operation for microwave S-band at 3.1 GHz allows lower phase-dependent measurements for the accurate estimation of the dielectric constant. The device is fabricated on an RO48350B substrate of $ {\varepsilon _r} = 3.48 $ ε r = 3.48 and the corresponding results were found to close match the electromagnetic simulation data. Silt soil and red soil were tested and analysed up to 21% percentage of the moisture level. Transmission coefficient ( $ \mathrm{\Gamma } $ Γ )-based numerical technique was used to extract the dielectric constant of different soil samples. The proposed sensor design is also tested for on-field measurements of soil and moisture level from different soil surface depth has been extracted. [ABSTRACT FROM AUTHOR]

A new method for estimating cumulative evapotranspiration using superconducting gravimeter data is presented. Evapotranspiration is computed from the classical water balance equation using precipitation data, estimated runoff, and water storage assessed from temporal variations of gravity. The gravity-based methodology is applied to a study site located at the Argentinian-German Geodetic Observatory (AGGO) in the flatland area of Buenos Aires Province (Argentina), which houses the only superconducting gravimeter in South America. For the period from April 2017 to May 2018, the cumulative evapotranspiration is estimated at 710.7 ± 25.7 mm. Evapotranspiration values are successfully compared with estimates provided by three methods, based on the water balance equation using soil moisture data, the numerical modelling of water flow in the unsaturated zone and the MODIS product (MOD16A2). The analysis of evapotranspiration also allows us to identify a period of soil water stress which is validated by soil moisture measurements. [ABSTRACT FROM AUTHOR]

In past studies, several researchers took potential use of multi-temporal optical data and dual-polarized SAR data to assess drought by estimating soil moisture. In this study, Modified Dubois Model (MDM) semi-empirical model with Topp's model is used for retrieval of soil moisture. It involves retrieving the backscattering coefficient from RISAT-1 and SENTINEL-1 datasets to derive the surface roughness and soil moisture conditions. The estimated soil moisture retrieved from microwave SAR parameters is validated with field measurements provides soil moisture spatial variability over different land use classes and bare soil condition. The RISAT-1 derived soil moisture has R² = 0.53, whereas SENTINEL-1 shows R² = 0.84. It also confirms the possibility of two different polarization σ°HH and σ°VV backscatter involving MDM. It observes that SENTINEL-1 was found well correlated with ground-measured soil moisture. Also, the averaged NDVI sounds reliable with soil moisture ratio, which helps to understand the impact of agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

Accurate knowledge of soil moisture is critical for hydrological and agricultural applications such as agricultural irrigation management, drought characterization, and flood detection. Researchers have attempted to provide soil moisture using various methods and techniques. Traditionally, the amount of soil moisture was based on field measurements. On the other hand, remote sensing satellites have been widely used to provide continuous soil moisture measurements worldwide, encountering problems such as the lack of simultaneous spatial and temporal sampling rates and dependence on weather conditions. However, in recent decades, GNSS signals reflected from the Earth's surface (GNSS-R technique) have been increasingly used for soil moisture monitoring, due to the numerous advantages it offers. This paper aims to provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSS-R observations. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

The Terrestrial Water Resources Satellite (TWRS) campaign is a planned Chinese candidate satellite mission, and a one-dimensional synthetic aperture technology will be used, resulting in variant incidence angles for collecting synchronous active-passive observations at L-band, which would make brightness temperature (Tb) downscaling especially challenging when aiming to improve the spatial resolution of soil moisture measurements. In this study, two active-passive Tb downscaling algorithms, the time-series regression (TSR) and spectral analysis (SA) algorithms, are assessed comprehensively based on airborne experimental datasets. The results with data collected during the Soil Moisture Experiment 2002 (SMEX02) showed that both approaches could provide a reliable downscaled Tb at the same incidence angle. Based on the ground and airborne active-passive observations under variant incidence angles from the Soil Moisture Experiment in the Luan River (SMELR) it can be shown that the linear relationship between Tb and σ is still robust under the case of variant incidence angles, and Tb (both h- and v-pol) is better correlated to σvv for most cases than σhh. Both downscaling approaches can be applied to active-passive observations under varying incidence angles. Moreover, SA method performed better than the TSR method according to the lower RMSE values and higher correlation. [ABSTRACT FROM AUTHOR]

Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies. [ABSTRACT FROM AUTHOR]

As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional vegetation cover (Fc). Theoretical solutions of the dry and wet boundaries were derived by annual-scale optimization and microwave SM calibration. The two limits of SM were obtained by linear fit function between SEE and microwave-based SM. The proposed method was validated at the Liaoning Province of China in the year 2011 by using MODerate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture and Ocean Salinity (SMOS) satellite images as input. Results indicated that the new method has not only bypassed the complex parametric scheme in the calculation of boundaries within the LST-Fc feature space but also performed superior in the estimation of soil moisture status at all-sky days. Besides, the optimal method has reproduced the spatial and temporal patterns of soil moisture reasonably well, with a root mean square error of 0.07 m3 m−3. Therefore, the proposed method can be regarded as a suitable tool to provide accurate and continuous monitoring of soil moisture. [ABSTRACT FROM AUTHOR]

This study investigated, soil salinity and moisture content under the exotic Tamarix in the Olifants River, South Africa, where they predominantly occur. Soil electro-conductivity (EC) was mapped using the electromagnetic induction (EMI) device (EM38 sensor), in three transects laid along the river from as close to the water source outward towards the bank of the River at 50 m apart. This was supported by three soil EC and moisture measurements from each of the three transects at a soil depth of 0–100 cm at intervals of 10 cm using soil EC meter and Amplitude Domain Reflectometry (ADR) sensor, respectively. The highest salt concertation (3,000 mS/m or 19,500 ppm) was found at a depth of 30–40 cm under the dense Tamarix species. The highest soil moisture (20–40%) was also found at the same depth under the Tamarix, suggesting a hydraulic lift of water to the top 30–40 cm, where the Tamarix fine roots for water absorption occur. It also confirms that the distance from water point and Tamarix plant density affect salt leaching depth and amount of litter decomposition, respectively, which is the main source of salt deposition in soil. [ABSTRACT FROM AUTHOR]

Sea Surface Salinity (SSS) is a pre-eminent parameter in oceanology causing extreme climate and weather events such as floods and droughts. Therefore, knowledge discovery of SSS is increasingly becoming a fundamental problem in recent years. However, not only the inadequacy of in-situ SSS data in large ocean basins are hampering conduction of detailed analyses of patterning SSS variations but also conventional data-gathering techniques for SSS estimation are often too expensive and time-consuming to meet the amount of data required in SSS estimation studies. Conversely, the brand-new Soil Moisture Active-Passive (SMAP) mission could provide validated SSS data along with its main objective soil moisture retrieval. As a result, collecting a candidate data set of surface's parameters as inputs to SSS with the aid of Pearson correlation and Boruta feature selection techniques, this paper aims to study the predictive skills of machine learning approaches to estimate SMAP radiometer SSS in the Persian Gulf region from April 2015 to April 2017. Thus, four machine learning methods including Support Vector Regression (SVR), artificial neural network (ANN), random forest (RF) and gradient boosting machine (GBM) were adopted to model the SSS. Two approaches of GBM and RF provided scarcely equivalent predictions for both the calibration and validation data sets that were distinguishably substantiated by experimental results and simulations, nonetheless, slightly superior results were attained with the GBM model by correlation coefficient (r) = 0.734, root mean squared error (RMSE) = 0.906 and mean absolute error (MAE) = 0.627. The findings demonstrate promising SSS estimation from SMAP, which could provide a baseline to perceive the large-scale changes in SSS. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over fertilization has resulted in serious soil compaction and acidification, as well as nonpoint pollution. A strategy for variable fertilization according to Site-Specific Management Zones (SSMZ) is urgently needed. We developed an object-oriented method using image segmentation for SSMZ delineation for a farming cooperative founded in 2013 in northeastern China. The method is based on Normalized Difference Vegetation Index (NDVI) during the crop growth period from 2011 to 2016. We analyzed and validated SSMZ results with plant sampling data, NDVI, soil temperature and moisture. The results indicated that (i) SSMZs derived from NDVI and image segmentation enhanced the difference between SSMZs and the homogeneity within SSMZs; (ii) crop dry biomass was consistent with NDVI, and NDVI showed a clear change in the regular SSMZ pattern with terrain variation after 2013; and (iii) SSMZ patterns were mainly affected by different farming practices or by topographic factors before and after the cooperative was founded. This new approach for SSMZ delineation will accelerate the application of precision agriculture in relief areas. If the study results are applied to other areas, the main factors of SSMZ need to be determined because of climate differences as well as specific soil and terrain conditions. [ABSTRACT FROM AUTHOR]

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast. [ABSTRACT FROM AUTHOR]

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042. [ABSTRACT FROM AUTHOR]

The main objective was to identify representative parameter sets for two mesoscale catchments. Discharge, groundwater level and satellite-based soil moisture were included in the calibration process to increase the robustness and the representativeness of the parameters. The integration of these data led to an innovative, detailed soil water description, which was implemented in a hydrological model with its corresponding parameters as an appropriate interface to optimization algorithms. A new lexicographic calibration strategy was applied. A comparison with results obtained by the SCE-UA (shuffled complex evolution algorithm) optimization showed that the lexicographic approach delivered more similar parameter sets, had more similar effects on the hydrological system and was much faster. The similarity of the parameter values and the cross-validation scores indicates the good representativeness of the model and furthermore emphasizes the application of a lexicographic calibration procedure. The consideration of groundwater level was a clear-cut benefit for the calibration, while the consideration of satellite-based soil moisture showed no significant effects in the two catchments. [ABSTRACT FROM AUTHOR]

The article offers information on agrometeorological services provided by hydrometeorological service of Republic of Macedonia. Topics discussed include history of Hydrometeorological activities; meteorological activities regarding agriculture are carried out in the Department of Meteorology; and Climate change prediction in Macedonia was carried out with MAGIC/SCENGEN v5.3 software package.

Soil Temperature (ST) data, obtained from either field works or satellite imagery, has frequently been studied for Soil Moisture (SM) estimation. However, a combination of ST data at different depths and soil surface temperature, i.e., Surface Radiometric Temperature (SRT) or Land Surface Temperature (LST), has not yet been well investigated for accurate SM prediction. In this study, an empirical model was first developed to estimate SM at 5 cm Depth (SM5D) over areas with no or sparse vegetation cover using the field SRT and field ST data at 5 cm Depth (ST5D). A Root Mean Square Error (RMSE) and a correlation coefficient (r) of 0.037 m3 m−3 and 0.8 were obtained using this model, respectively. Then, the SRT was substituted by the LST obtained from Landsat thermal bands and ST5D was estimated using the ST data collected at the nearest weather station to the study area by developing a regression equation. The second model demonstrated an RMSE and r of 0.035 m3 m−3 and 0.71, respectively. Overall, it was concluded that the proposed models had high potential for SM estimation using the ST data at different depths collected in the field or acquired by optical satellites. [ABSTRACT FROM AUTHOR]

Conflicting reports of the responsiveness of carrots (Daucus carota L.) to nitrogen (N) fertiliser have been rationalised on the basis of the climatic zones in which the experiments were conducted. We tested this explanation with two experiments under temperate conditions, where yield increases were predicted to be unlikely, yet growers frequently apply N fertiliser. Our results show that in temperate regions carrot yields may indeed respond to N fertiliser when the yield potentials are c. 140 t ha−1 of fresh roots, if soil mineral N to 0.6 m depth and readily mineralisable N add to less than c. 190 kg N ha−1. With normal experimental precision, such responses may be undetected unless soil water deficit is kept small and variations in plant population are accounted for. Carrots have strong potential for luxury uptake of N. This has implications for marketing and the possible use of carrots as catch crops. [ABSTRACT FROM AUTHOR]

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter αs. In addition to the parameter αs from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR'2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9-12 and 9-15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters αs and χ for εs > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season. [ABSTRACT FROM AUTHOR]

Expansive clays are widely prevalent all over the world as one of the most problematic soils. These soils undergo significant volume change with a change in the moisture regime, thereby posing problems to the stability of the structures founded on such strata. Efforts have been made to model the erratic behaviour of these soils at the macro, micro, and, to a lesser extent, nano levels. Micro and nano level fabrics, believed to have a central role in the overall behaviour of expansive clays, are only partially considered in the modelling concepts; natural clay fabrics with multiple clay minerals, silt and sand inclusions, micro fissures, cementation, overconsolidation, induration and other such features have never been considered. This paper covers a review of deficiencies in the existing constitutive models for the expansive characteristics of the natural clayey soils at macro, micro and nano levels. These shortcomings are discussed in the light of the understanding of the fundamentals including fabric and structure controlling the swelling mechanism of the expansive clayey soils at the molecular level. Finally, a framework based on authors' work to incorporate molecular level behaviour in the constitutive modelling of expansive clays is presented. [ABSTRACT FROM AUTHOR]

Unsaturated expansive soils subjected to wetting and drying cycles result in huge differential settlements of structures built on these materials. The existed models for these materials present large number of parameters that lead to time-consuming procedure to characterise their mechanical behaviour during wetting--drying cycles. In this context, Zarka shakedown theory previously applied to the mechanical loading of granular materials has been used for expansive soils subjected to suction cycles. The parameters of this shakedown-based model were calibrated for two different expansive soils. The comparisons between the experimental results and the calculations for the different tests, demonstrate the capacity of Zarka shakedown theory to simulate the mechanical behaviour of unsaturated expansive soils. [ABSTRACT FROM AUTHOR]

The soil--water retention (SWR) characteristics of the clays play an important role in controlling their engineering behaviour, particularly, in the unsaturated state. Although, researchers have attempted to understand the water retention characteristics of the clays in their reconstituted or remoulded state, such studies are rare for the clays in their intact state. In this context, it becomes important to understand the influence of initial state of compaction, which would create different pore and fabric structure (viz., microstructure), on the water retention characteristics of the clays. With this in view, SWR behaviour was experimentally determined for the swelling clays (dried from different compaction states, viz., intact, reconstituted and remoulded) by employing Dewpoint PotentiaMeter (WP4C®). The changes in the pore size distribution of the clays at different stages of drying cycle were also studied by employing the Mercury Intrusion Porosimetry. The study reveals that the SWR curves for the intact and reconstituted specimens of the clays converge beyond a certain stage of drying. Also, a critical analysis of changes in the pore structure of the swelling clay specimens, during drying, indicates that the progressively deforming pore structure plays an important role in controlling its water retention characteristics to a great extent. [ABSTRACT FROM AUTHOR]

Seasonal signals of stable isotopes in precipitation, combined with measurements of isotope ratios in soil water, can be used for quantitative estimation of groundwater recharge rates. This study investigates the applicability of using the piston flow principle and the peak shift displacement method to estimate actual groundwater recharge rates in a humid Nordic region located in the province of Quebec, Canada. Two different sites with and without vegetation (C1 and C2) in an unconfined aquifer were tested by measuring soil water isotope ratios (18O/16O and 2H/1H) and volumetric pore water content. Core samples were obtained along the vadose zone down to the groundwater table at the two sites (2.45 m for Site C1 and 4.15 m for Site C2). The peak shift method to estimate groundwater recharge rates was shown to be accurate only in certain specific conditions inherent to the soil properties and the topographical situation of the investigated sites. Indeed, at Site C2, recharge from the snowmelt could not be estimated because of heterogeneity in the lower part of the vadose zone. At this same site the later recharge after the snowmelt (in the period from late spring to early autumn) could be estimated accurately because the upper part of the vadose zone was homogeneous. Furthermore, at site C1, runoff/runon phenomena hampered calculations of actual infiltration and thus produced inaccurate results for recharge. These two different site effects (heterogeneity in the first site and runoff/runon in the other site) were identified as being limiting factors in the accurate assessment of actual recharge. This study therefore recommends the use of the peak shift method for (1) humid Nordic regions, (2) homogeneous and thick vadose zones, and (3) areas with few or limited site effects (runoff/runon). [ABSTRACT FROM AUTHOR]

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R2) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess, under laboratory and field conditions, the performance of the Decagon 5TE and GS3 soil water sensors in estimating the soil water content of saline-alkali reclaimed land in South Korea. The error due to increased bulk soil EC was greater for the 5TE sensor than for the GS3 sensor and was greater in soil with higher bulk soil EC. Therefore, it is recommended that the soil electrical conductivity be considered in the calibration process for the 5TE. The calibration equations developed for the 5TE and GS3 sensors during laboratory experiments were highly accurate. Testing of the 5TE and GS3 sensors showed very good agreement between actual VWC and VWC calculations resulting from the field-derived calibration. These results suggest that either sensor can be used to acquire accurate soil water content data in the field. [ABSTRACT FROM PUBLISHER]

Soil water content (SWC) is an important factor in transfer processes between soil and air, contributing to water and energy balances, and quantifying it remains a challenge. This study uses artificial neural networks (ANNs) to analyse spatial and temporal variation of SWC in a Brazilian watershed, based on climate information, soil physical properties and topographic variables. Thirty eight input variables were tested in 200 models. The outputs were compared with 650 gravimetric moisture measurements collected at 26 points (25 field studies). The results show that it is possible to estimate SWC efficiently (Nash-Sutcliffe statistic, NS = 0.77) using topographic data, soil physical properties and rainfall. If only climate information is considered, modelling is less efficient (NS = 0.28). Using many variables does not necessarily improve performance. Alternatively, SWC can be estimated by simplified models using rainfall and topographic maps information, although the performance is less good (NS = 0.65). [ABSTRACT FROM PUBLISHER]

To facilitate precise and cost-effective watershed management, a simple yet spatially and temporally distributed hydrological model (DHM-WM) was developed. The DHM-WM is based on the Mishra-Singh version of the curve number method, with several modifications: The spatial distribution of soil moisture was considered in moisture updating; the travel time of surface runoff was calculated on a grid cell basis for routing; a simple tile flow module was included as an option. The DHM-WM was tested on a tile-drained agricultural watershed in Indiana, USA. The model with the tile flow module performed well in the study area, providing a balanced water budget and reasonable flow partitioning. The daily coefficient of determination and Nash-Sutcliffe coefficient were 0.58 and 0.56, for the calibration period, and 0.63 and 0.62 for the validation period. The DHM-WM also provides detailed information about the source areas of flow components, the travel time and pathways of surface runoff.EDITOR A. Castellarin; ASSOCIATE EDITOR F.-J. Chang [ABSTRACT FROM AUTHOR]

Copyright of Ecoscience (Ecoscience) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST. [ABSTRACT FROM AUTHOR]

The capacity of six water stress factors (ε′i) to track daily light use efficiency (ε) of water-limited ecosystems was evaluated. These factors are computed with remote sensing operational products and a limited amount of ground data:ε′1uses ground precipitation and air temperature, and satellite incoming global solar radiation;ε′2uses ground air temperature, and satellite actual evapotranspiration and incoming global solar radiation;ε′3uses satellite actual and potential evapotranspiration;ε′4uses satellite soil moisture;ε′5uses satellite-derived photochemical reflectance index; andε′6uses ground vapor pressure deficit. These factors were implemented in a production efficiency model based on Monteith’s approach in order to assess their performance for modeling gross primary production (GPP). Estimated GPP was compared to reference GPP from eddy covariance (EC) measurements (GPPEC) in three sites placed in the Iberian Peninsula (two open shrublands and one savanna).ε′iwere correlated toε, which was calculated by dividing GPPECby ground measured photosynthetically active radiation (PAR) and satellite-derived fraction of absorbed PAR. Best results were achieved byε′1,ε′2,ε′3andε′4explaining around 40% and 50% ofεvariance in open shurblands and savanna, respectively. In terms of GPP,R2 ≈ 0.70 were obtained in these cases. [ABSTRACT FROM AUTHOR]

Two winter wheat (Triticum aestivumL.) cultivars, namely Jimai22 (JM22) and Zhouyuan9369 (ZY9369), were used to study the effects of a new irrigation policy, supplemental irrigation (SI) based on soil moisture levels, photosynthesis, dry matter accumulation, and remobilization from 2009 to 2011 in Northern China. Two SI treatments were designed based on relative soil moisture contents in the 0–140 cm soil layer: (1) the target soil relative water contents were 75% of field capacity (FC) at jointing and 65% of FC at anthesis (W1), 75% and 70% (W2) in 2009–2010, and (2) the target soil relative water contents were 75% at jointing and 75% at anthesis (W1′), 75% and 80% (W2′) in 2010–2011. Rain-fed treatment (W0) was used as control. Results showed that SI significantly improved the biomass, grain yield and water use efficiency (WUE) of both wheat cultivars. The biomass and grain yield of W1 and W1’ treatments were higher than those of others. The net photosynthetic rate, the actual photochemical efficiency of flag leaf, the accumulation of dry matter, and its remobilization from the vegetative parts to the grains after anthesis in W1 and W1’ treatments were significantly higher than in the other treatments. By contrast, the WUE and irrigation efficiency of W2 and W2’ were significantly lower than those of W1 and W1’. Under the experimental conditions, ‘JM22’ showed higher photosynthetic rate in the last stage of grain filling, more spike number per ha, more kernels per spike, higher 1000-kernels weight and eventually higher WUE than ‘ZY9369’. [ABSTRACT FROM AUTHOR]

Techniques to formulate accurate and timely prescriptions for variable-rate irrigation systems lag behind the hardware capabilities. Canopy temperature measurements could serve as an indicator of water stress, but current methods that exploit the data can be cumbersome. The objectives of this study were 1) to determine the relationship between synchronous measurements of maize (Zea maysL.) canopy temperature and in-field soil moisture tension, and 2) to understand the influence of discretionary crop canopy temperature stress thresholds on the relationship between soil moisture tension and crop canopy temperature. A variable-rate irrigation pivot was used to form six water-treatment zones. Each zone was equipped with tensiometers installed in the center of the plots and an infrared thermometer pointed into the crop canopy to individually monitor conditions in each zone. Results of this study indicate that synchronous canopy temperature was able to quantify soil moisture tension, particularly during the reproductive crop growth stages. Using inexpensive infrared thermometers with this method could provide a cost-effective solution for gauging in-field soil moisture variability. [ABSTRACT FROM PUBLISHER]

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan. [ABSTRACT FROM PUBLISHER]

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSMmeasurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. Thesemodel coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSMmeasurements demonstrated that SSMvariation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419-0.379, root mean square error: 0.046-0.051m³m-3, Bias: -0.020 to -0.025m³m-3) was found for the mid-morningmodel and the daytimemodel with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614-0.675), a better accuracy (Bias: 0.032-0.044 m³m-3, RMSE: 0.043-0.050 m³m-3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research. [ABSTRACT FROM AUTHOR]

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in theTs–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI,Ts,Fvegetation,Fsoil, temperature (T), precipitation at timet(Pt, Pt –1,Pt –2), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between −2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations. [ABSTRACT FROM PUBLISHER]

The proper management of irrigation requires an accurate measuring of soil moisture. One of the commonly applied methods for measuring soil moisture is the use of gypsum blocks – a method that is simple and quick to apply. However, measuring moisture in saline soils using this method is prone to errors due to the effect of soil salinity on the block. In this study, the effect of different salinities (1, 2, 6, 10, and 18 deci Siemens per meter (dS m−1) on the measurements of a gypsum block type 5910 A was investigated with two repetitions in random blocks in sandy loam, loam, and clay loam soils, and corrective functions were obtained using multivariate regression for all soils with different salinity levels. The results showed different trends for measuring the soil moistures for salinities 1–6 dS m−1compared with salinities 10–18 dS m−1, and the corrective functions in salinities 1–6 dS m−1, which had higher accuracies than those with salinities 10–18 dS m−1. [ABSTRACT FROM AUTHOR]

The main cause of loss of soil structural stability is vehicle operation on unpaved wet surfaces. Unfortunately, there is a lack of continuous soil moisture data in predicting trafficable conditions. To measure changes in soil moisture conditions in real time, Percostation (Adek) sensors were installed in sandy loam Stagnosol soil at different depths. Problems with soil trafficability can be expected at the plastic limit, and the soil is unable to support vehicle operations at the liquid limit in such soils. The maximum water-holding capacity of the soil is 32%, the field capacity is 25%, the plastic limit is 22%, and the liquid limit is 30%. With rainfall of more than 10 mm d−1, the moisture content reached the plastic limit in the upper 25 cm of soil. The average increase in the soil moisture content after more than 10 mm of rain was 1–2.5% in a time frame of 2–3 hours. After rain, the previous soil moisture level was obtained within 2 to 3 days in the vegetation period. Measurements also allowed soil water balance and evapotranspiration modelling data to predict soil moisture conditions with an accuracy of one day but failed to predict in a shorter period. [ABSTRACT FROM PUBLISHER]

Land surface emissivity is known to be affected by land cover, surface roughness, and soil moisture. In this study we used land surface emissivity data from National Aeronautics and Space Administration's Advanced Microwave Scanning Radiometer for Earth Observing Systems for 2003 to 2007 to detect areas of significant emissivity change and identify its potential causes. Data distribution of annual emissivity averages on a global scale revealed that the western coastlines of the continents, deserts, and the polar areas showed the lowest emissivity values, whereas the higher emissivity values were concentrated in large cities, heavily forested areas, and large mountain ranges. A two-tailed Z test was then used to identify regions that showed statistically significant changes at the 99 percent confidence interval. At the regional scale, three regions that showed statistically significant annual change values at the 99 percent confidence interval--the Iranian Plateau, the La Plata basin, and the Indus basin--were then isolated. An analysis of variance was then performed to determine whether the variability was due to phenomena across the region or within each region. In the La Plata River basin, areas of significant negative emissivity change (≤-0.0156) were noted, which are associated with deforestation, whereas the areas of significant emissivity gain (≥0.0165) were concentrated in floodplains. In the Indus basin and Iranian Plateau, the significant negative emissivity change was associated with variations in surface wetness. Using emissivity as an indicator of land surface properties can assist in more detailed analysis of changes on the surface of the earth. [ABSTRACT FROM AUTHOR]

The widespread transition into intensive agroecosystems has led to a considerable decline in plant biodiversity especially for organisms in the field boundaries. The aim of this study was to survey the vegetation of fields and field boundary types located in an agricultural landscape. We provided the structural map of the landscape and classified it into field boundary types nested in three main groups of natural and seminatural elements. Species recorded in all habitat types were categorized into two emergent groups based on their response to land use intensification: 1) agrotolerant species (AT) and 2) high nature-value species (NV). We analyzed the effect of landscape structure and soil physicochemical properties on species richness of these groups. We found both landscape structure and soil properties significant in explaining variation among AT and NV species richness both at 2-m-×-2-m and landscape scales. The most overall species richness was recorded for agricultural fields (43 species) followed by non-crop field edges (37). Woody green veins and permanent ditches were the elements providing high NV species richness clearly, while fields and other field boundary types were mostly supporters of AT species richness. Diversity of AT species increased along nitrogen and clay content gradients of the soil, while NV species benefited where phosphorus and soil moisture content were high. We recommend agricultural landscapes to be more specified with (semi-)natural habitats which embed high a proportion of rare weeds as nature-value plant species. [ABSTRACT FROM PUBLISHER]

Soil evaporation transfer coefficient (ha) is an effective means of estimating surface soil moisture from moderate-resolution imaging spectroradiometer (MODIS) imagery. This coefficient is a function of three variables: air temperature, land surface temperature and dry soil temperature. The first two variables can easily be obtained from different sources, whereas dry soil temperature cannot be determined as easily as the other ones, particularly over partially vegetated areas. In this paper, to enhance the capability ofhain estimating soil moisture, we propose to use the combination of land surface temperature (LST) and normalized difference vegetation index (NDVI) to estimate dry soil temperature over partially vegetated areas as well as bare soil areas; this combination is known as LST-NDVI feature space in the literature. The underlying assumption of the proposed method is that at any given pixel over partially vegetated or bare soil areas, dry soil temperature is approximately equivalent to the maximum LST derived from LST-NDVI triangle space. The results showed that calculatinghausing dry soil temperature derived from the triangle space can result in more reliable estimation of soil moisture over bare soil and partially vegetated areas. [ABSTRACT FROM AUTHOR]

This study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index,R2, and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.Editor D. Koutsoyiannis; Associate editor not assigned [ABSTRACT FROM PUBLISHER]

Before and after a prescribed burning, the upper 2.5 cm of soil in patches of forage, nonforage, and shrub vegetation were sampled. Then, bulk density, gravimetric moisture, pH in water, available phosphorus, exchangeable cations, cationic exchange capacity, total nitrogen, total, particulate, and mineral organic carbon contents were determined. Maximum temperature ranges reached over the ground during burning were measured and the highest values were registered in shrubs. Results showed that total, particulate, and mineral organic carbon and total nitrogen contents and percent base saturation increased in all soils after burning, without detecting differential effects between vegetation patches. After burning, available phosphorus content increased both for forage and shrub patches. A Na+content decrease was observed for all soils in the different patches while Mg2+content increased. No changes in the other analyzed variables were observed. Prescribed burning, as studied in the present work, might improve the Caldenal soils’ chemical fertility, mainly due to an increase in labile organic matter and some nutrients essential for plant growth. [ABSTRACT FROM AUTHOR]

The main objective of this research was to develop, test, and validate a soil moisture retrieval method for agricultural fields using C-band synthetic aperture radar (SAR) data in preparation for the RADARSAT Constellation Mission (RCM). The integral equation model (IEM) was used to estimate soil moisture for 12 quad-polarimetric RADARSAT-2 images acquired in 2011 and 2012 over a test site in eastern Canada. A new hybrid inversion scheme is introduced whereby field-level soil moisture retrieval was accomplished through a look-up table (LUT) optimization using backscatter from pairs of RADARSAT-2 data acquired at two incidence angles and two polarizations. The accuracy of this new hybrid approach was compared to that of the more conventional multipolarization inversion. For validation purposes, in situ soil moisture was monitored using a network of four stations designed to capture the maximum soil variability within the study area. Results showed that the hybrid approach provided the best estimates with a mean absolute error of 4.13%. Unlike the multipolarization inversion, the hybrid approach does not require a roughness calibration term. This model/inversion configuration was implemented to produce soil moisture maps from image pairs acquired at different incidence angles. Spatiotemporal changes conveyed by retrieved maps were assessed using an index of absolute normalized moisture difference. The resulting maps provided meaningful information, showing the potential of combining a soil moisture index statistic, IEM, and RADARSAT data in estimating soil moisture conditions. Based on the results presented here, the hybrid approach is well suited for the planned RADARSAT Constellation Mission considering the more frequent relook expected with three satellites. Agriculture and Agri-Food Canada is preparing to implement the hybrid approach to monitor soil moisture across Canada's agricultural landscapes using RCM. [ABSTRACT FROM AUTHOR]

Field-scale water balance is difficult to characterize because controls exerted by soils and vegetation are mostly inferred from local-scale measurements with relatively small support volumes. Eddy covariance flux and lysimeters have been used to infer and evaluate field-scale water balances because they have larger footprint areas than local soil moisture measurements. This study quantifies heterogeneity of soil deep drainage (D) in four 12.5-m2repacked lysimeters, compares evapotranspiration from eddy covariance (ETEC) and mass balance residuals of lysimeters (ETwbLys), and modelsDto estimate groundwater recharge. Variation in measuredDwas attributed to redirection of snowmelt infiltration and differences in lysimeter hydraulic properties caused by surface soil treatment. During the growing seasons of 2010, 2011 and 2012, ETwbLys(278, 289 and 269 mm, respectively) was in good agreement with ETEC(298, 301 and 335 mm). Annual recharge estimated from modelledDwas 486, 624 and 613 mm for three calendar years 2010, 2011 and 2012, respectively. In summary, lysimeterDand ETECcan be integrated to estimate and model groundwater recharge.Editor D. Koutsoyiannis [ABSTRACT FROM AUTHOR]

This study examined the characteristics of heat and water transfer in structurally modified urban soils. To satisfy our goals, we measured the temperature and moisture content of anthropogenic soils to a depth of 50 cm. Field observations was carried out for three sets (each of two pedons) of soils in the Tokyo Metropolitan area. Each pedon had the same turf coverage but different profile modifications in the green areas. Soil temperature, soil moisture, and precipitation data were collected during the summer (July–Sept) and winter (Oct–Feb) every 10 min. From the results, we calculated the thermal diffusivity and thermal conductivity in each pedon. Soil temperature showed a clear daily variation down to 30 cm depth. Temperature transmission to deeper layers was faster in pedon having stronger soil compaction and more artificial fragments than in pedons with weaker soil compaction and fewer concrete fragments. This finding suggests that strongly compacted soil has a relatively high thermal conductivity, and easily transfers heat to deeper soil. In pedons composed of soft, organic-rich, and clay-rich soil, water retention impedes the increase in soil temperature during daytime, whereas nighttime cooling is prevented by the lower heat transmission due to the larger porosity. Throughout the observations, the water content ranged from 0.1 to 0.45 m3 m−3. The thermal diffusivity was obtained as 1.2–5.0 × 10−3 cm2 s−1in pedons without artifacts, but was higher (2.5–7.3 × 10−3 cm2 s−1) in all pedons containing larger volumes of concrete artifacts. Although the directions of heat flow by time within the profiles having lithological disturbance was not much different with that of natural soils, the observation data revealed that heat flow per time differed by structural properties of the profiles. Furthermore, thermal properties such as thermal diffusivity and thermal conductivity of the soils characterized with lithological disturbance were significantly higher than those of natural soils and it was notable that they were not influenced by either volume fraction of water or air in the soil. The fact suggested that the anthropogenic soils containing a large amount of modifiers and concrete artifacts have small capacity of water retention due to specific macro pores. Artificial materials and compaction regulate the drainage and water retention of the soil, although the water transfer behavior in the studied pedons could be rather complicated. [ABSTRACT FROM PUBLISHER]

The Soil Moisture Active Passive Validation Experiment 2012 was conducted as a pre-launch validation campaign for the Soil Moisture Active Passive mission over 6 weeks in June and July 2012. During this campaign, the Passive Active L-Band System (PALS) was flown at a low altitude, providing radar and radiometer measurements that were contained within a single agricultural field. The campaign domain consisted of 55 agricultural fields, where soil moisture was measured coincident to the PALS flight times and measurements of vegetation volumetric water content (VWC) and leaf area index (LAI) were measured weekly. The low-altitude flights allowed for the comparison between measured VWC and LAI for 11 fields to radar parameters derived from the radar backscatter. Only the correlation between the HV backscatter and the soybean VWC was considered strong (|r| > 0.7). All other correlations between the radar parameters and the VWC (or LAI) were moderate (0.3 < |r| < 0.7) or weak (|r|< 0.3). The established relationships between radar parameters and VWC were used in a forward radiation transfer model to estimate H-pol brightness temperature. It was found that the RMSE between the brightness temperatures estimated using the measured VWC was lowest when using the relationship between VWC and LAI (3.9 K for soybeans, 6.8 K for spring wheat, and 9.3 K when all crop data are combined). Despite a lower correlation, the RMSE associated with using the radar vegetation index relationship with VWC was less than when HV was used (7.9 K) for soybeans, which would result in an error in soil moisture estimation of just over 4%. The RMSEs for all other VWC and radar parameter relationships were greater than 10 K. [ABSTRACT FROM AUTHOR]

Tropical estuary wetlands are important for aquaculture and wildlife. However, many of them receive large amounts of anthropogenic heavy metals annually. Here, the transformation of spiked heavy metals, namely, Cd, Cr, Cu, Ni, Pb, and Zn, and the effects of salinity on their transformation in wetland soils after an eight-month-long incubation under moisture-saturation conditions were studied in the Pearl River estuary in China. Cd exhibited high mobility and bioavailability, with 12.2% to 25% Cd existing in the exchangeable fraction. Other heavy metals primarily existed in the reducible and oxidisable forms, and less than 2% were bound to the exchangeable fraction. Compared with the controls, contents of none of the metals associated with residual forms were significantly altered. These results imply that most exchangeable metals, except for Cd, transformed into other stable fractions through an eight-month-long ageing process, but not into the residual fraction. Thus, transformation from non-residual to residual forms was very slow in the tropical estuary wetland environment. Addition of NaCl increased the exchangeable fractions of Cd, Pb, and Zn, suggesting that increased soil salinity induced by flood tides during the dry season may enhance their mobility. [ABSTRACT FROM PUBLISHER]

Knowledge of the variability of soil water content (SWC) in space and time plays a key role in hydrological and climatic modelling. However, limited attention has been given to arid regions. The focus of this study was to investigate the spatio-temporal variability of surface soil (0–6 cm) water content and to identify its controlling factors in a region of the Gobi Desert (40 km2). The standard deviation of SWC decreased logarithmically as mean water content decreased, and the coefficient of variation of SWC exhibited a convex upward pattern. The spatial variability of SWC also increased with the size of the investigated area. The spatial dependence of SWC changed over time, with stronger patterns of spatial organization in drier and wetter conditions of soil wetness and stochastic patterns in moderate soil water conditions. The dominant factors regulating the variability of SWC changed from combinations of soil and topographical properties (bulk density, clay content and relative elevation) in wet conditions to combinations of soil and vegetation properties (bulk density, clay content and shrub coverage) in dry conditions. This study has important implications for the assessment of soil quality and the sustainability of land management in arid regions. [ABSTRACT FROM AUTHOR]

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m3 m−3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model. [ABSTRACT FROM AUTHOR]