Moisture Products Research Articles

Rapid retrieval of soil moisture using a novel portable L-band radiometer in the Hulunbeier Prairie, China

ABSTRACT The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

Applying Systems Thinking to Sustainable Beef Production Management: Modeling-Based Evidence for Enhancing Ecosystem Services

We used systems thinking (ST) to identify the critical components of beef cattle production through the lens of ecosystem services (ES), offering a holistic approach to address its adverse externalities. We identified eight critical feedback loops in beef production systems: (i) grazing and soil health, (ii) manure management and soil fertility, (iii) feed efficiency and meat production, (iv) water use and soil moisture, (v) cultural services and community engagement, (vi) energy use, (vii) carbon sequestration and climate regulation, and (viii) environmental impact. Our analysis reveals how these interconnected loops influence each other, demonstrating the complex nature of beef production systems. The dynamic hypothesis identified through the loops indicated that improved grazing and manure management practices enhance soil health, leading to better vegetation growth and cattle nutrition, which, in turn, have a positive impact on economic returns to producers and society, all of which encourage the continuation of interlinked beef and ecosystem stewardship practices. The management of beef production ES using ST might help cattle systems across the globe to contribute to 9 of the 17 different United Nations’ Sustainable Development Goals, including the “zero hunger” and “climate action” goals. We discussed the evaluation framework for agrifood systems developed by the economics of ecosystems and biodiversity to illustrate how ST in beef cattle systems could be harnessed to simultaneously achieve the intended environmental, economic, social, and health impacts of beef cattle systems. Our analysis of the literature for modeling and empirical case studies indicates that ST can reveal hidden feedback loops and interactions overlooked by traditional practices, leading to more sustainable beef cattle production outcomes. ST offers a robust framework for enhancing ES in beef cattle production by recognizing the interconnectedness of ecological and agricultural systems, enabling policymakers and managers to develop more effective and sustainable strategies that ensure the long-term health and resilience of humans and ES.

Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data

Abstract. Soil moisture is a crucial component of the global terrestrial ecosystem water vapor cycle, and higher spatial-temporal soil moisture product has significant impacts on research in agriculture, hydrology, and ecology. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology which can be used to retrieve soil moisture. Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA in 2016 to observe ocean surface hurricanes, has also collected a substantial amount of data of GNSS signals reflected over the land. This paper focuses on soil moisture retrieval using CYGNSS and SMAP dataset. In this study, three machine learning methods (GSVM, BP and RF) are used for spatial interpolation of SMAP soil moisture data. Result shows RF interpolation is the best with the RMSE of 0.044 cm3/cm3 and the CC of 0.959 compared with SMAP truly SM. Then soil moisture data after RF interpolation and conventional linear interpolation are respectively used as response variables to train the retrieval model by ELM, BP and RF machine learning methods respectively. Results show that, among the six regression models, the RF-RF regression model performs the best with the RMSE of 0.012 cm3/cm3 and the CC of 0.986. Using this model, we get a global soil moisture product and have higher temporal and spatial resolution.

АНАЛИЗ ВЛИЯНИЯ МЕТЕОРОЛОГИЧЕСКИХ УСЛОВИЙ НА ПРОДУКТИВНОСТЬ ЯРОВОЙ ПШЕНИЦЫ В 2023 ГОДУ НА ПРИМЕРЕ МЕТЕОРОЛОГИЧЕСКОЙ СТАНЦИИ ТАЙЫНША СЕВЕРО-КАЗАХСТАНСКОЙ ОБЛАСТИ

According to the results of the assessment about possible agricultural losses due to climate change, conducted as part of the preparation of the Seventh National Communication of the Republic of Kazakhstan on Climate Change, including for the North Kazakhstan region, where, according to the forecast, by 2050 a decrease in moisture supply during the growing season by 11...16 % and an increase in climate aridity by 10...15 % relative to current indicators are expected. As a result of such changes, this may lead to a decrease in the yield of spring wheat by 30...40 %. The aim of this study was to establish the relationship between the yield of Aviada spring wheat and meteorological conditions at the observation site of the Taiynsha meteorological station in the North Kazakhstan region for 2023. For this purpose, the actual meteorological parameters for the specified year were studied, such as temperature and precipitation, hydrothermal moisture coefficient (HTC), productive moisture reserves in the soil, sums of effective temperatures, as well as the growth and development characteristics of spring wheat of the Aviada variety during the vegetation period of 2023, its yield.It was found that insufficient precipitation during the critical phases of spring wheat development created stressful conditions during the grain ripening period, and, as a result, affected its yield.In addition, the shift of summer precipitation to August...September led to crop losses during and after harvesting. This article represents the results of the research internship that took place at Taiynsha meteorological station from May to August 2023.

Urea modified Cu-doped LaFeO3 nano-particles for humidity sensing with contactless moisture detection for medical and agricultural application

Nowadays, the role of humidity is a major factor in numerous applications such as medical purposes and soil moisture detection. In this work, we synthesized pristine LaFeO3 (LFO1, LFO2) nanoparticles via hydrothermal method with and without urea. Furthermore, the synthesis of LFO doped with Cr, Cu and Mn was also done in the presence of urea using hydrothermal technique and studied for humidity sensing. The XRD confirms the crystalline size of the developed nanoparticles ranges in 19–25 nm. The sensing properties of pristine and doped LFO sensors for humidity sensing have been investigated in the 11 % – 90 % relative humidity (RH) range. As compared with others, the Cu-doped LFO sensor exhibits better response and recovery time of 5.4 s and 3.8 s respectively. Additionally, the nanoparticles of Cu-doped LFO show a highly porous nature having nano-perforated dumbbell structure as identified from the FESEM images. Moreover, to check the functionality of the developed sensor, the device is fabricated, resulting in the linear increase in output current with increase in relative humidity at a bias voltage of 1 V. With rapid response and recovery time, superior sensitivity and long-time stability, the developed Cu-doped LFO sensor can be used for medical detection of asthma, apnea and cough, as well as for non-contact skin monitoring that indicates its potential use to identify the moisturizing products for skin care. Finally, the efficacy of the fabricated sensor to monitor real-time humidity is also observed via IoT on a laptop/smartphone to display its potential to detect soil moisture for agriculture application.

Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

Evaluation of Sentinel-1 Satellite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics

Evaluation of Sentinel-1 Satellite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics

Значение предшественников и минерального питания в формировании урожайности и качества ярового ячменя в Пермском крае

Abstract. The purpose is to evaluate the influence of previous culture and mineral nutrition on the yield and quality of grain and seeds of spring barley in the conditions of the Middle Urals. Methods. The research was conducted at the Perm Agricultural Research Institute (a branch of the Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences) over the period 2021–2023 on sod-podzolic heavy loam soil. A two-factor field experiment with different previous culture and mineral nutrition backgrounds was carried out. Data were processed using B. A. Dospekhov’s methods, and grain quality was determined using standard techniques. Results. The limiting factor affecting plant growth and productivity is soil moisture, which influences both the size and quality of crops. Studies have shown that the soil under vetch-oat mixtures had the highest level of productive moisture at the start of the growing season. However, by the end of the season, the highest level was found under clover. During the research period, the most significant decrease in soil moisture was observed after barley, possibly due to the smaller surface area covered by this crop. The study of barley seed germination showed that they met the required standards. Seeds grown after vetch-oat had the best germination rate (95 %), while those preceding barley had the worst (92 %). This suggests that choosing the right precursor and providing optimal nutrition can lead to increased yield and improved grain quality. The yield of barley can vary widely, ranging from 1.06 to 3.77 tons per hectare. The study results show that using vetch-oat as a precursor can provide the best indicators for barley yield and quality. The optimal combination of precursor and mineral nutrient background can significantly improve the efficiency of barley production in agriculture. Scientific novelty: The complex effect of fertilizers and precursor substances on the formation of barley productivity and quality is being studied in the Perm region for the first time. The methods of barley management and quality control studied allow for obtaining highly productive seeds and achieving the planned yield.

A physics‐constrained hybrid residual neural network for the prediction of moisture content in a closed‐cycle drying system

AbstractClosed‐cycle drying technology has the advantages of safety, energy saving, and environmental protection, and has a wide application prospect. Due to the multi‐physics and multi‐scale nature of heat/mass transfer in the closed‐cycle drying process, as well as the characteristics of drying medium circulation and energy integration, it is difficult to obtain the product moisture content prediction model in closed‐cycle process based on rigorous closed‐cycle drying process mechanism model. However, accurate monitoring of product moisture content in the closed‐cycle drying process is the key to improving drying quality and process optimization. Aiming at the challenges in predicting moisture content, this paper proposes a physics‐constrained hybrid residual neural network (PC‐HRNN) for soft sensor modelling within a designed closed‐cycle drying system. The characteristic of this proposed method lies in its effective fusion of physics, knowledge, and data. First, a hybrid residual neural network (HRNN) is constructed based on the integration of a physics model and a data‐driven model. The HRNN takes the knowledge from the physics model as its auxiliary features and learns the prediction residuals of the physics model through a neural network model. Then, a new loss function that takes into account physical laws is introduced into HPNN to standardize model training and improve the generalization performance of the model. The experimental results show that the PC‐HRNN model improves prediction accuracy and model robustness, reduces the demand for data, and demonstrates stronger extrapolation capabilities under limited data.

Real-time quality analysis of baked goods using advanced technologies

Some critical quality variables of baked goods baked in tunnel ovens cannot be monitored in real-time and quickly after baking. Generally, these variables are measured individually and manually. Analysis of some variables can be done in laboratory environments. It is very difficult to continuously and accurately read the quality variables of products, especially at the exits of industrial tunnel ovens operating at high speeds. With current systems, important quality variables of the products are assessed through widely spaced sampling, and most of the time the measurements made do not represent the entire batch. Additionally, interventions in the process may be delayed due to the time required for analysis, allowing defective products to reach consumers. In this study, experiments were carried out to enable real-time and high-speed identification of important quality variables such as product geometry, surface color, image features, and moisture. The developed system aims to monitor products such as cookies, cakes, biscuits, crackers and bread. Through this system, the size of the products that can represent the lot or even the entire batch can be analyzed when desired. Thus, quality oscillations can be kept within very narrow ranges. The developed hybrid system is applied to the exit of tunnel ovens, and the data read through sensors and cameras are transferred to computers using specially developed algorithms through interfaces. It has been observed that process control signals can also be generated by considering the lower and upper control limits of quality variables. With the developed software, changes can be instantly seen on the screens in the form of graphics, audio, and visual warnings can be created, historical data can be recorded, and reports can be obtained. At the end of the study, it was found that important quality variables such as circularity, diameter, thickness, color, image area, and moisture could be read with a sample size of up to 100%. As a result of the experiments, it was seen that the change in product diameters could be defined within the range of 40.30±0.5 mm. The circularity ratio between horizontal and vertical diameters can be monitored within 1.008 ± 0.018%. The change in moisture within the range of 2.741±0.164 g/100g. The light-dark image area ratios within the range of 0.486±0.033%, and the product thickness within the range of 11.56±0.178 mm. Resultly, all critical quality parameters taken into account can be detected within a range that will not affect product and process quality.

Investigation of the Formation of Productive Moisture Reserves in the Thickness of Chernozem under the Forest-Steppe Conditions

Investigation of the Formation of Productive Moisture Reserves in the Thickness of Chernozem under the Forest-Steppe Conditions

A soil moisture experiment for validating high-resolution satellite products and monitoring irrigation at agricultural field scale

Validating the satellite soil moisture products is always an active research topic for the application of the products and improvement of the retrieval algorithms, attracting extensive attention. Nevertheless, seldom existing validation activities focus on the validation of high-resolution soil moisture products at the fine scale. To this end, an experiment was conducted in the middle stream of the Heihe River Basin in northwestern China in August to October of 2021, aiming to validate high-resolution satellite remote sensing products of soil moisture. The paper introduces the design, composite, and preliminary results of the experiment. A soil moisture observation network was established with two kinds of sensors (CS616 and Stevens Hydra Probe) validated against soil core measurements. Several synchronized campaigns were performed, and data were collected to validate the SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 and 1 km EASE-Grid Soil Moisture (SPL2SMAP_S) products. Besides, an optical trapezoid model (OPTRAM) and collected Sentinel-2 data were applied to estimate soil moisture and to map irrigated area. Preliminary analyses show that: 1) Steven probes perform best, with an RMSE = 0.040 m3m−3 and ubRMSE=0.034 m3m−3; 2) Both the SPL2SMAP_S products at 3 km and 1 km show large RMSE (0.128 m3m−3 for 3 km and 0.158 m3m−3 for 1 km) and ubRMSE (0.115 m3m−3 for 3 km and 0.158 m3m−3 for 1 km); 3) The OPTRAM retrievals over bare surface present relatively smaller RMSE (0.06 m3m−3) and ubRMSE (0.057 m3m−3), while retrievals over vegetated croplands present a relatively large RMSE/ubRMSE (0.083/0.083 m3m−3), and the retrievals can identify the irrigated area at field scale. Overall, the experiment provides fruitful methodologies and datasets for the validation of high-resolution remote sensing products, benefiting the development and improvement of soil moisture retrieval algorithms and products to support irrigation scheduling and management at a precision agricultural scale in the future.

Climate change and greenhouse gas emissions from soils under the winter wheat agroecosystem in Ukraine

The issue of global climate change is one of the most important challenges for modern environmental science. In this regard, assessment of greenhouse gas emissions from soils under agroecosystems is an extremely important task. The main purpose of the study was to assess the potential emissions of greenhouse gases (CO2 and N2O) from the soils under the winter wheat agroecosystem during its spring-summer growing season under the climate change in Ukraine. A comprehensive model of greenhouse gas emissions from agroecosystem soils was used as a theoretical basis. The study considered the main agroclimatic regions of Ukraine, such as Polissia, Forest-Steppe, Northern Steppe, and Southern Steppe. The modelling was performed for average long-term conditions of 1981–2020 and under scenario conditions (using the RCP4.5 climate scenario) for the decades of 2021–2030, 2031–2040, and 2041–2050. Estimates of CO2 emissions were obtained for the agroclimatic regions of Ukraine. According to the results, the largest CO2 emissions (0.886-0.940 t C-CO2/ha) will be expected when the average productive moisture supplies in the arable soil layer for the spring-summer period of winter wheat vegetation are 20–40 mm and the air temperature is 12–15 °C. Rise in the level of moisture reserves, averaged for the period, for more than 40 mm and the air temperature of 11–12 °C is accompanied by decrease in the level of CO2 emission (0.645–0.658 t C-CO2/ha). In arid conditions, with a decrease in the period-average moisture supplies in the arable soil layer to less than 20 mm and heightened average air temperatures (14.5–15.2 °C), a decrease in CO2 emissions is expected (0.755–0.770 t C-CO2/ha). The study showed that the highest N2O emission (1.087–1.703 kg N-N2O/ha) is expected at a high level of moistening (Md being equal to 0.474–0.713 relative units and air temperature – 11.3–13.0 °C) with a high intensity of nitrification and denitrification processes. Conditions characterizing the average level of N2O emission (0.657–0.890 kg N-N2O/ha) are formed under the moistening at a level of Md equaling 0.296–0.447 relative units and air temperature of 13.4–14.7 °C. There is a slowdown in denitrification processes and an increase in the share of emissions owing to nitrification. It has been found that the lowest N2O emission (0.508–0.537 kg N–N2O/ha) is expected under conditions of severe and very severe drought (according to the Md criterion), with a heightened temperature averaged at the level of 14.5–15.2 °C during the spring-summer period of winter wheat vegetation.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

Understanding Terrestrial Water and Carbon Cycles and Their Interactions Using Integrated SMAP Soil Moisture and OCO‐2 SIF Observations and Land Surface Models

AbstractRecently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

The puzzle of soil Moisture: Unravelling its dynamics in a watershed with complex physiographic features

Periodic in-situ soil moisture observations are essential for managing agriculture, water resources and validating satellite-based soil moisture products. To facilitate this, a comprehensive soil moisture monitoring network is required that provides an optimal balance between the accuracy of measurement and the associated costs. Therefore, the present study attempts to investigate the spatio-temporal variability of soil moisture over a period of 57 weeks in a watershed spanning 422 km2 with elevation ranging from 739 m to 2914 m. The watershed presents complex physiographic features such as extreme seasonal variability, differential elevations and diverse land use patterns (agricultural, forest and grasslands). The study follows a systematic sampling strategy, using a 2 km x 2 km spatial resolution across 104 grids over the entire watershed area. The soil moisture data is then used to estimate the number of optimal locations from a spatial and temporal perspective. The number of spatial optimal locations and temporal optimality are quantified using the student’s t-distribution test and the random combination method, respectively. Moreover, empirical orthogonal functions (EOFs) coupled with Spearman rank correlation method are used to evaluate the dominant physiographic factors affecting soil moisture variability within the watershed. From a comprehensive set of 71,136 field measurements, this study identified 30 optimal sampling locations necessary to capture the spatial variability of soil moisture within a ± 2 % error band. Within these 30 locations, 8 randomly selected locations can adequately represent the temporal variability of the watershed mean soil moisture within a ± 1 % error band. Furthermore, EOFs coupled with Spearman rank correlation identified elevation and sand content as the dominant physical factors influencing soil moisture variability across the watershed. The insights derived from this study provide a robust framework for carrying out soil moisture studies, especially in an ungauged watershed.

Influence of the Weather Conditions on the Efficiency of Absorbents in the Vegetable Crop Rotation System and on the Stock of Productive Soil Moisture

Influence of the Weather Conditions on the Efficiency of Absorbents in the Vegetable Crop Rotation System and on the Stock of Productive Soil Moisture

Computational Fluid Dynamics Modelling of a Laboratory Spray Dry Scrubber for SO2 Removal in Flue Gas Desulphurisation—Effect of Drying Models

Spray dry scrubbing is widely used for SO2 abatement, but high removal efficiencies are required for economical operation. Whereas SO2 removal dependence on the drying rate has been investigated, available modelling work has not addressed the impact of selected drying models on the removal efficiency; instead, a single drying model is often assumed. In the present work, computational fluid dynamics is used to numerically model the SO2 removal in a laboratory-scale spray dry scrubber. The Euler–Lagrangian framework is used to simulate the multiphase interaction and two drying models are used: the widely used classical D2-law model and the mechanistic model. In addressing shortcomings from previous works, this study also provides a comprehensive model development and robust model validation with quantifiable metrics for goodness-of-fit, including R2. Also presented are key parameters associated with SO2-removal efficiency, including the exit product moisture content and droplet dynamics. The mechanistic model gave a better representation of the SO2-removal efficiency. The latter was found to be dependent on the inlet temperature, the calcium-to-sulphur and liquid-to-gas (L/G) ratios, with a high L/G ratio having the most significant impact on the removal efficiency, although resulting in a higher product outlet moisture content.

Variability in addictive and carcinogenic potential of smokeless tobacco products marketed in Mumbai, India: a surveillance study

India has the highest incidence worldwide of smokeless tobacco (SLT)-associated oral cancer, accounting for nearly 70% of all SLT users globally. Nicotine and tobacco-specific N-nitrosamines (TSNA) play critical roles in the addictive and carcinogenic potential, respectively, of SLT products. Our group has previously reported substantial variability in nicotine and TSNA levels across a small SLT product sample in India, calling for systematic surveillance. However, there is no information available on the current levels of these constituents in Indian SLT. We analysed 321 samples representing 57 brands of eight popular types of manufactured SLT products purchased from five local markets in Mumbai, India between August, and September 2019. The sampling locations were Mumbai Central, Kurla, Thane, Vashi, and Airoli. Product pH, moisture content, total and unprotonated (biologically available) nicotine, and TSNA levels were measured at the Advanced Centre for Treatment, Research, and Education in Cancer (ACTREC) in Mumbai. Total nicotine content ranged from 0.45 to 35.1mg/g across products. The unprotonated nicotine fraction contributed 0.1-100% of the total nicotine content. The carcinogenic TSNA levels ranged 0.06-76 ug/g for N'-nitrosonornicotine (NNN), 0.02-19.2 ug/g for 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), and 0.01-6.51 ug/g for 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). Consistent with our previous study, we observed substantial variations across different brands of the same product type. This is the most extensive and the first within-country study to report brand-specific nicotine and TSNA levels in SLT products marketed in Mumbai, India. Our results show that levels of these constituents remain extremely variable across Indian SLT and are strikingly high in many products. Enhanced public education and continued efforts to reduce SLT use prevalence in India are critical for reducing the global burden of SLT-associated morbidity and mortality. Regulation of nicotine and TSNA levels in SLT products should be considered. This work was supported by the National Institutes of Health (USA) grant R01-TW010651 and, in part, by grants R01-CA180880 and R50-CA211256. The LC-MS/MS analysis was supported in part by XII Plan project funding from the Department of Atomic Energy, Government of India.

Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with in situ measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m3/m3 and 0.99, respectively versus the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m3/m3vs. 0.04 m3/m3). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.