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A delay differential equation model of an infectious disease is considered and analyzed. In this model, the impact of information due to the presence of infection is considered explicitly. As information propagation is dependent on the prevalence of the disease, the delay in reporting the prevalence is an important factor. Further, the time lag in waning immunity related to protective measures (such as vaccination, self-protection, responsive behaviour etc.) is also accounted. Qualitative analysis of the equilibrium points of the model is executed and it is observed that when the basic reproduction number is less unity, the local stability of the disease free equilibrium (DFE) depends on the rate of immunity loss as well as on the time delay for the waning of immunity. If the delay in immunity loss is less than a threshold quantity, the DFE is stable, whereas, it loses its stability when the delay parameter crosses the threshold value. When, the basic reproduction number is greater than unity, the unique endemic equilibrium point is found locally stable irrespective of the delay effect under certain parametric conditions. Further, we have analyzed the model system for different scenarios of both delays (i.e., no delay, only one delay, and both delay present). Due to these delays, oscillatory nature of the population is obtained with the help of Hopf bifurcation analysis in each scenario. Moreover, at two different time delays (delay in information's propagation), the emergence of multiple stability switches is investigated for the model system which is termed as Hopf-Hopf (double) bifurcation. Also, the global stability of the endemic equilibrium point is established under some parametric conditions by constructing a suitable Lyapunov function irrespective of time lags. In order to support and explore qualitative results, exhaustive numerical experimentations are carried out which lead to important biological insights and also, these results are compared with existing results.

Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics.

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel’s (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m3/m3) and bias (in m3/m3). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation.

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

In this work, we proposed a simple SEIHR compartmental model to study and analyse the third wave of COVID-19 in India. In addition to the other features of the disease, we also consider the reinfection of recovered individuals in the model. For the purpose of parameter estimation we separate the infective and deaths classes and plot them against the cumulative counts of infective and deaths from data, respectively. The estimated parameters from these two are used for prediction and further numerical simulations.We note that the infective will keep on growing and only slow down after around three months. We have studied impact of various parameters on our model and observe that the parameters associated with mask usage, screening and the care giving toCOVID-19 patients have significant impact on the prevalence and time taken to slow down the infection.We conclude that better use of mask, effective screening and timely care to infective will reduce infective and can help in disease control. Our numerical simulations can explicitly provide a short term prediction for such time line. Also we note that providing better care facilities will help reducing peak as well as the disease burden of predicted infected cases.

The crops' biophysical parameters play an important role in balancing the land surface energy fluxes and are needed in crop simulation modeling, evapotranspiration, etc. The vegetation parameters' retrieval using microwave scattering model, mainly affected by the heterogeneous distribution of land targets, hampers an accurate retrieval of soil-vegetation parameters in microwave remote-sensing algorithms. To minimize the errors in biophysical parameters' retrieval, the synergetic approach of modified water cloud model (MWCM) and modified soil scattering model (MSSM) was attempted to retrieve the leaf area index (LAI) of wheat and barley crops. Due to the spatiotemporal resolution of Sentinel-1A synthetic aperture radar (SAR) mission, it could be more sensitive to vegetation condition and the retrieval accuracy than optical/IR satellites. The nonlinear least square optimization algorithms were used for the parameterization of modified scattering model. The lookup table (LUT)-based inversion algorithm was applied to compute the LAI values through the modified scattering models. The statistical analysis was performed to assess the model efficiency. In case of forward modeling, the highest R² = 0.96 and low RMSE = 0.20 dB were computed between the modeled σ⁰ (dB) and SAR-derived σ⁰ (dB) using vegetation descriptor (V) = LAI. On the other hand, for inverse modeling, the LAI values obtained were more accurate at VV polarization (R² = 0.94 and RMSE = 0.124 m²/m²), when compared with the in situ data. The overall product was also compared with the Project for On-Board Autonomy-Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS)-LAI to check the robustness of the approach.

Regional quantification of energy and water balance fluxes depends inevitably on the estimation of surface and rootzone soil moisture. The simulation of soil moisture depends on the soil retention characteristics, which are difficult to estimate at a regional scale. Thus, the present study proposes a new method to estimate high-resolution Soil Hydraulic Parameters (SHPs) which in turn help to provide high-resolution (spatial and temporal) rootzone soil moisture (RZSM) products. The study is divided into three phases—(I) involves the estimation of finer surface soil moisture (1 km) from the coarse resolution satellite soil moisture. The algorithm utilizes MODIS 1 km Land Surface Temperature (LST) and 1 km Normalized difference vegetation Index (NDVI) for downscaling 25 km C-band derived soil moisture from AMSR-2 to 1 km surface soil moisture product. At one of the test sites, soil moisture is continuously monitored at 5, 20, and 50 cm depth, while at 44 test sites data were collected randomly for validation. The temporal and spatial correlation for the downscaled product was 70% and 83%, respectively. (II) In the second phase, downscaled soil moisture product is utilized to inversely estimate the SHPs for the van Genuchten model (1980) at 1 km resolution. The numerical experiments were conducted to understand the impact of homogeneous SHPs as compared to the three-layered parameterization of the soil profile. It was seen that the SHPs estimated using the downscaled soil moisture (I-d experiment) performed with similar efficiency as compared to SHPs estimated from the in-situ soil moisture data (I-b experiment) in simulating the soil moisture. The normalized root mean square error (nRMSE) for the two treatments was 0.37 and 0.34, respectively. It was also noted that nRMSE for the treatment with the utilization of default SHPs (I-a) and AMSR-2 soil moisture (I-c) were found to be 0.50 and 0.43, respectively. (III) Finally, the derived SHPs were used to simulate both surface soil moisture and RZSM. The final product, RZSM which is the daily 1 km product also showed a nearly 80% correlation at the test site. The estimated SHPs are seen to improve the mean NSE from 0.10 (I-a experiment) to 0.50 (I-d experiment) for the surface soil moisture simulation. The mean nRMSE for the same was found to improve from 0.50 to 0.31.

Potassium (K) is a critical nutrient for crops, as it is a major constituent in fertilizer formulations. With increasing concentrations of K in agricultural soil, it is necessary to understand its movement and retention in the soil. Sub-surface modeling is an alternative method to overcome the exhausting and uneconomical methods to study and determine the actual concentration of K in soil. HYDRUS-1D is considered an effective finite-element model which is suitable for sub-surface modeling. This model requires the input of ground-station meteorological (GM) data taken at a daily timestep for the simulation period. It can be a limiting factor in the absence of ground stations. The study compares K predictions in surface and sub-surface soil layers under Boro rice cultivation obtained with the usage of different meteorological datasets. Thus, the main hypothesis of the study was to validate that, in the absence of GM data, satellite-based meteorological data could be utilized for simulating the K concentration in soil. The two meteorological datasets that are considered in the study included the GM and satellite-derived NASA-Power (NP) meteorological datasets. The usage of a satellite meteorological product at a field scale may help in applying the method to other regions where GM data is not available. The numerical model results were validated with field experiments from four experimental fields which included varied K doses. The concentration in soil was assessed at the regular depths (0–5, 5–10, 10–15, 15–30, 30–45 and 45–60 cm), and at various stages of crop growth, from bare soil and sowing, to the tillering stages. The concentration of K was measured in the laboratory and also simulated through the optimized model. The modeled values were compared with measured values statistically using relative root mean square error (RMSER) and Nash–Sutcliffe modeling efficiency (E) for simulating K concentration in the soil for the Boro rice cropping pattern with both GM data and NP data. The model was found most suitable for the 0–30 cm depth on all days and for all treatment variations.

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

The need for information on soil moisture at large scale to facilitate a sustainable intensification of agricultural land and to ensure food security due to increasing populations cannot be oversta...

The accurate consideration of vegetation descriptors in water cloud model (WCM) is necessary for precise SM retrieval. Most of the vegetation descriptors are sourced from optical remote sensors. The acquisitions from optical sensors are largely hampered by bad weather conditions. For all-weather monitoring, Synthetic Aperture Radar (SAR) based vegetation descriptors are needed to identify and evaluate their performance for SM retrieval. The present study evaluates the various sources/combinations of SAR based vegetation descriptors in WCM to identify the better alternatives of optical-based vegetation descriptors. The performance of three radar-based vegetation descriptors, namely VH polarized backscattering coefficients, depolarization ratio and radar vegetation index (RVI) along with the one optical-based vegetation descriptor, namely leaf area index (LAI) from MODIS were utilized in WCM. The WCM for each vegetation descriptor has been performed using Sentinel-1 VV polarized backscattering coefficients and in-situ SM. The in-situ SM measurements were carried out in the fields around Varanasi District in India during the winter season sown with the wheat crop. The correlations coefficient (r), root mean square error (RMSE) and bias were used to evaluate the performances of vegetation descriptors in WCM for SM retrieval. The study showed that the depolarization ratio is the best for SM retrieval with accuracy of 0.096 ${m}^{3}{m}^{-3}$ followed by RVI, cross-polarized and LAI with 0.100 ${m}^{3}{m}^{-3}$ , 0.124 ${m}^{3}{m}^{-3}$ and 0.124 ${m}^{3}{m}^{-3}$ , respectively. Thus, the depolarization ratio can be used for the retrieval of SM using Sentinel-1 VV polarized backscattering coefficients over the wheat crop.

Translating genome-wide association loci to therapies requires knowledge of the causal genes, their directionality of effect and the cell-types in which they act. To infer these relationships in the human brain, we implemented Mendelian randomisation using single cell-type expression quantitative trait loci (eQTLs) as genetic anchors. Expression QTLs were mapped across 8 major cell-types in brain tissue exclusively ascertained from donors with no history of brain disease. We report evidence for a causal association between the change in expression of 118 genes and one or more of 16 brain phenotypes, revealing candidate targets for risk mitigation and opportunities for shared preventative therapeutic strategies. We highlight key causal genes for neurodegenerative and neuropsychiatric disease and for each, we report its cellular context and the therapeutic directionality required for risk mitigation. Our use of control samples establishes a new resource for the causal interpretation of GWAS risk alleles for human brain phenotypes.

Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies.

For the purpose of deriving spatiotemporal estimates of soil moisture, the triangle method is one of the most widely used approaches today utilizing remote sensing data. Generally, those techniques are based on the physical relationships that exist when a satellite-derived land surface temperature (Ts) is plotted against a spectral vegetation index (VI). The present study proposes an improvement in the triangle method in retrieving soil moisture over heterogeneous areas. In particular, it proposes a new approach in robustly identifying the extreme points required for the technique’s implementation. Those extreme points are then used in calculating fractional vegetation cover (Fr) and scaled Ts. Furthermore, the study proposes a new approach for calculating the coefficients required to develop the relationships between surface soil moisture (SSM) and Fr/Ts, which is implemented using a model and field data. As a case study, an agricultural field in the Varanasi district in India has been used, on which the triangle method is implemented using ECOSTRESS and Sentinel-2 data. The much-improved spatial resolution satellite data of ~70 m from ECOSTRESS allowed deriving more vivid results of SSM spatial variability for the study area. Comparisons between field soil moisture calculated using the proposed method returned an RMSE of 0.03 and R2 value of 0.84, which are considered very satisfactory. The methodology proposed herein and the results obtained are of significant value with regards to the triangle method, contributing to ongoing efforts at present examining its use for operational product development at a global scale.

Human activity recognition has become one of the most active areas of research in computer vision, due to its increasing demand in many automated monitoring applications such as visual surveillance, human-computer interaction, health care, security systems, and many more. This work aims to introduce an integrated feature descriptor which combines texture feature and shape feature, at multiple orientations, to construct the efficient and robust feature vector for activity recognition in realistic scenarios. This feature descriptor is an integration of Discrete Wavelet Transform (DWT), multiscale Local Binary Pattern, and Histogram of Oriented Gradients (HOG). HOG descriptor extracts local-oriented histograms of the frame sequences, multiscale LBP gives the complex structural information of the frames and DWT gives the directional information at multiple scales. By exploiting these properties, we have constructed an integrated feature descriptor to construct the feature vector and achieves promising results of activity recognition in realistic videos. Multiclass Support Vector Machine (SVM) classifier with one-vs-one architecture has been used for activity recognition. The experiments are performed on five benchmark publicly available video datasets, namely Weizmann, IXMAS, UT Interaction, HMDB51, and UCF101. The experimental results are compared with the results of other state-of-art methods based on conventional machine learning and deep learning-based methods to show the effectiveness and usefulness of the proposed work. The experimental results have demonstrated that the proposed method performs better than the other state-of-art methods.

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithm...

Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe.

The dual polarimetric study including degree of polarization (mL) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel − 1 A and ALOS − 2 satell...

A nonlinear SEIR model is formulated and analyzed. This model accounts for three important interventions — the saturated treatment on infective individuals, the screening on the exposed individuals and the information induced self-protection on susceptible individuals. Existence and stability of equilibria are discussed. A sensitivity analysis for the model parameters is performed and we identified the parameters which are more sensitive to the model system. The sensitivity analysis is further followed up with the two parameters heat plot that determines the regions for the parametric values in which the system is either stable or unstable. Further, an optimal control problem is formulated by considering screening and treatment as control variables and corresponding cost functional is constructed. Using Pontryagin’s Maximum Principle, paths of optimal controls are obtained analytically. A comparative study is conducted numerically to explore and analyze analytical results. We note that in absence of treatment, screening policy may be a cost-effective choice to keep a tab on the disease. However, comprehensive effect of both screening and treatment has a huge impact, which is highly effective and least expensive. It is also noted that treatment is effective for mild epidemic whereas screening has a significant effect on the disease burden while epidemic is severe. For a range of basic reproduction number, effect of self-protection and saturation in treatment is also explored numerically.

Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about folia...

Globally, changes in hydroclimate extremes such as extreme precipitation events influence water resources, natural environments, and human health and safety. During recent decades, India has observed an enormous increase in rainfall extremes during the summer monsoon (June to September) seasons. However, future extreme rainfall events have significant uncertainty at the regional scale. Consequently, a comprehensive study is needed to evaluate the extreme rainfall events at a regional river basin level in order to understand the geomorphological characteristics and pattern of rainfall events. In the above purview, the current research focuses on changes in extreme rainfall events obtained through observed gridded datasets and future scenarios of climate models derived through the Coupled Model Intercomparison Project (CMIP). The results highlight a significant rise in the extremes of precipitation events during the first half of the 21st century. In addition, our study concludes that accumulated precipitation will increase by five days in the future, while the precipitation maxima will increase from 200 to 300 mm/day at the 2-year, 50-year, and 100-year return periods. Finally, it is found that during the middle of the 21st century the 23.37% number of events will increase over the TRB at the 90th percentile.

Shallow landslides due to hydro-meteorological factors are one of the most common destructive geological processes, which have become more frequent in recent years due to changes in rainfall frequency and intensity. The present study assessed a dynamic, physically based slope stability model, Transient Rainfall Infiltration and Grid-Based Slope Stability Model (TRIGRS), in Idukki district, Kerala, Western Ghats. The study compared the impact of hydrogeomechanical parameters derived from two different data sets, FAO soil texture and regionally available soil texture, on the simulation of the distribution and timing of shallow landslides. For assessing the landslide distribution, 1913 landslides were compared and true positive rates (TPRs) of 68% and 60% were obtained with a nine-day rainfall period for the FAO- and regional-based data sets, respectively. However, a false positive rate (FPR) of 36% and 31% was also seen, respectively. The timing of occurrence of nine landslide events was assessed, which were triggered in the second week of June 2018. Even though the distribution of eight landslides was accurately simulated, the timing of only three events was found to be accurate. The study concludes that the model simulations using parameters derived from either of the soil texture data sets are able to identify the location of the event. However, there is a need for including a high-spatial-resolution hydrogeomechanical parameter data set to improve the timing of landslide event modeling.

The global Earth Observations, remote sensing, geoscience, environmental and applied science communities celebrated the successful launch of LANDSAT 9 on Sept. 27, 2021. LANDSAT 1 (originally named...

Boro rice, an emerging low-risk crop variety of rice, cultivated using residual or stored water after Kharif season. To enhance the quality and production of rice, potassium (K) and phosphorus (P) are the common constituents of agricultural fertilizers. However, excess application of fertilizers causes leaching of nutrients and contaminates the groundwater system. Therefore, assessment and optimization of fertilizer dose are needed for better management of fertilizers. Towards this, the present study determines the path, persistence, and mobility of K and P under the Boro rice cropping system. The experimental site consisted of four plots having Boro rice with four different fertilizer doses of nitrogen (N), P, K viz. 100%, 75%, 50%, and 25% of the recommended dose. Disturbed soil samples were analysed for K and P from pre-sown land to tillering stage at 0–5, 5–10, 10–15, 15–30, 30–45, and 45–60 cm depths. Simultaneously, K and available P were also simulated in the subsurface soil layers through the HYDRUS-1D model. The statistical comparisons were made with RMSER, E, and PBIAS between the modelled values and laboratory-measured values. Although, the results showed that all the treatments considered had agreeable simulations for both K and P, the K simulations were found to be better as compared to P simulations except for 25% where P simulations outperformed K. The simulated concentration at all doses was found most appropriate when measured for the subsurface layers (up to 45 cm), while showed an underestimation in the bottom layers (45–60 cm) of soil.

In this article, we propose and analyze an infectious disease model with reinfection and investigate disease dynamics by incorporating saturated treatment and information effect. In the model, we consider the case where an individual's immunity deteriorates and they become infected again after recovering. According to our findings, multiple steady states and backward bifurcation may occur as a result of treatment saturation. Further, if treatment is available for all, the disease will be eradicated provided

The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

Large-scale soil moisture monitoring is a critical element of sustainable intensification of agricultural land. Remote sensing provides the way forward required for nationwide soil moisture monitoring coverage. On the other, utilising cosmic-ray neutron probes is a relatively new approach for obtaining larger area soil moisture and various relevant operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. This study compares retrievals of soil moisture between the COSMOS-UK cosmic-ray soil moisture observation network and the Synthetic-Aperture-Radar Soil Water Index (SCAT-SAR SWI) product across selected COSMOS-UK sites. A further objective has been to investigate the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed objectively examining the agreement between the truth data of the TDT soil moisture sensors and the COSMOS-UK product for soil moisture. It was found that the true footprint of this COSMOS-UK station was representative for an area smaller than the general assumed footprint of 600m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the Time Domain Transmissometry (TDT) sensor probes installed in the area. Results of our study contribute towards efforts to assess the COSMOS-UK soil moisture measurement footprint demonstrating the added value of geospatial analysis techniques for this purpose. Results showed a strong correlation between the true data of the Time Domain Transmissometry soil moisture sensors and the COSMOS and SCAT-SAR products for soil moisture. In addition, the true footprint of this COSMOS-UK station was discovered to be reflective of a smaller area than the usually accepted footprint of 600m diameter, as proposed in many relevant studies. Results of our study contribute towards efforts to assess the COSMOS-UK soil moisture measurement footprint demonstrating the added value of geospatial analysis techniques for this purpose. Further scrutiny of the technique is required to establish its applicability to different areas and ecosystems. Such an investigation would require exploring the prediction accuracy of the technique for other sites would have other contributing features such as slopes, land cover differentiation and penetrating vegetation such as hedgerows which could drastically affect the footprint of the probes. All the above are topics of key importance to be taken up by future studies exploiting neutron probe data in the context of soil moisture retrievals. Keywords: COSMOS UK; SCAT-SAR SWI; Soil Moisture Monitoring; Spatial Analysis; Remote Sensing

Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors' biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions.