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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.

A new dielectric constant estimation approach for the lunar surface using Mini-RF synthetic aperture radar (SAR) data and Chandrayaan-2 SAR data is presented in this work. Both the SAR systems are based on a hybrid-polarimetry (hybrid-pol) configuration that transmits a circularly polarized wave and coherently measures the backscattered wave by dual orthogonal linearly polarized channels. From the three-component hybrid-pol SAR decomposition technique, the ratio of the Fresnel reflection coefficients for horizontal and vertical polarization transmission can be estimated. This ratio, which is referred to as the co-polarization ratio, is used by the proposed methodology to find the real value of the dielectric constant. For performance validation, the proposed method is implemented on the Mini-RF hybrid-pol SAR data acquired over Apollo 17 landing sites. The values of the real part of dielectric constant are estimated for six different regions covering the collection sites of six Apollo 17 samples: 72,441, 73,241, 74,241, 75,081, 76,001, and 79,135. The results obtained using the proposed method are found to be in good agreement with the laboratory-measured results of the corresponding samples. Furthermore, the proposed methodology is also implemented on the Chandrayaan-2 hybrid-pol SAR data acquired over the Biot crater region. Various small areas possessing different possible surface characteristics, situated inside and outside the Biot crater, are being analyzed for validation.

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.

The physico-chemical properties of binary mixture of 2,3-dichloroaniline (2,3-DCA) and carbon tetrachloride (CCl4) have been studied in the present work by the measurement of density, viscosity, ultrasonic velocity and estimation of thermo-physical parameters at 300 K. The density, compressibility, free length, relative association and free volume were found to increase while viscosity, ultrasonic velocity, acoustic impedance and internal pressure were received to decay with an increase in mole fraction of solvent in the binary mixture. The thermal relaxation time and Gibb’s free energy were found to decay with a slow rate up to 50% mole fraction of the solvent, after that these quantities decay very fast in chosen binary mixture. The ultrasonic absorption was also found to increase slowly up to 50% mole fraction of solvent and after that a fast decay was observed in the present binary mixture. The molecular interaction, structural ordering, stability and related features of the prepared binary mixture were also analyzed based on measured and estimated parameters.

The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination ( $R^{2}$ ), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al. , the values of $R^{2}$ , RMSE and bias were obtained as 0.87, 0.57 m2m−2, and 0.05 m2m−2 respectively, whereas for WCM model, the values were found as 0.82, 0.67 m2m−2, and 0.32 m2m−2 respectively. This investigation showed that the modifications in Oveisgharan et al. model provide marginally better results in the retrieval of LAI using ScatSat-1 data than the WCM model. The models’ limitation may be less serious for crop management studies because the majority of crops attains its maturity at LAI values less than 6 m2/m2.

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

Surface Soil Moisture (SSM) is an important variable in agricultural water management, required for irrigation water demand, scheduling, etc. In this chapter, the estimation of SSM is carried out using Artificial Neural Network (ANN) model trained by MODIS land surface temperature (LST) and normalised difference vegetation index (NDVI) feature spaces and validated using the in-situ data. The ANN model is trained, validated and tested using the three different combinations of input-output datasets. The first combination of datasets is considered as MODIS LST (input) and in-situ SSM (output) datasets for ANN-I model. The second combination of datasets is considered as MODIS NDVI (input) and in-situ SSM (output) datasets for ANN-II model. The third combination of datasets is considered as MODIS LST and NDVI (input) and in-situ SSM (output) datasets for ANN-III model. The performance of ANN-I, ANN-II and ANN-III models are evaluated in terms of correlation coefficient (r), bias and root mean squared error. In overall, the performance of ANN-II model was found good for SSM estimation.

This study brings out work carried out for large-scale soil moisture mapping based on a time-series-based methodology over India using SMAP L-band radiometer data and its validation at selected agriculture sites over India. A methodology has been developed to derive soil wetness index from the time-series of SMAP L-band brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This study also focuses on analysis of in-situ soil moisture data from different agricultural fields as validation sites to utilise for the validation of Soil Moisture (SAC-ISRO) data products, developed and operationalized by SAC (ISRO) since 2015. The in-situ data over a two-year period was analysed and the validation of the soil moisture (SAC-ISRO) with this in-situ data was conducted using statistical performance metrics, i.e., unbiased root mean square error (ubRMSE), Bias, root mean square error (RMSE) and coefficient of determination (R2). Overall, the results presented in this work demonstrate that very significant improvement was observed in soil moisture (SAC-ISRO) data products over different validation sites by applying scaling approach, which finally meets SMAP mission requirement and the performance of soil moisture (SAC-ISRO) data products derived from SMAP L-band radiometer data are closely matching with in-situ observations and have a very good agreement. This validation exercise and results are very useful to support the continuous development and improvement of the soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially over Indian regions.

The synthesis of Cox1, the conserved catalytic-core subunit of Complex IV, a multisubunit machinery of the mitochondrial oxidative phosphorylation (OXPHOS) system under environmental stress, has not been sufficiently addressed. In this study, we show that the putative YihA superfamily GTPase, Mrx8, is a bona fide mitochondrial protein required for Cox1 translation initiation and elongation during suboptimal growth condition at 16°C. Mrx8 was found in a complex with mitochondrial ribosomes, consistent with a role in protein synthesis. Cells expressing mutant Mrx8 predicted to be defective in guanine nucleotide binding and hydrolysis were compromised for robust cellular respiration. We show that the requirement of Pet309 and Mss51 for cellular respiration is not bypassed by overexpression of Mrx8 and vice versa. Consistently the ribosomal association of Mss51 is independent of Mrx8. Significantly, we find that GTPBP8, the human orthologue, complements the loss of cellular respiration in Δmrx8 cells and GTPBP8 localizes to the mitochondria in mammalian cells. This strongly suggests a universal role of the MRX8 family of proteins in regulating mitochondrial function.

Soil moisture is required to understand the land surface processes, land-atmosphere interaction, drought forecasting, crop growth patterns, etc. It is a dynamic variable that changes significantly on different spatial and temporal scales even in a smaller area. Remote sensing (RS) techniques provide an alternative way to estimate the high spatial and temporal variability of soil moisture. This chapter includes the state of art and techniques used to retrieve soil moisture from satellite RS in different parts of the world. Several techniques have been developed to retrieve soil moisture either from optical/thermal/microwave sensors or fusion of these sensors, but microwave sensors either with a fine spatial resolution (and coarse temporal resolution) or with a coarse spatial resolution (and fine temporal resolution) seem promising than optical and thermal sensors depending on applications. However, microwave sensors have shown its high potential and capability for deriving global soil moisture information due to all weather capability and longer penetration depth. Many operational products of soil moisture have been developed using passive microwave sensors; however, coarse spatial resolution and penetration depth over vegetation-covered surfaces are the major factors that limit the utility of these soil moisture products for agricultural purposes. The major initiatives have been taken by various space agencies across the globe to develop the microwave sensors with L and/or S bands for its potential use in soil moisture besides other applications. Sentinel-1 synthetic aperture radar (SAR, active microwave sensor) data has opened up a new research area to develop high-spatial-resolution soil moisture products for agricultural applications. Several studies have shown the potentials of Sentinel-1 SAR data and high-resolution optical data along with operational products (like SMAP) to downscale the coarse soil moisture data to retrieve high spatial soil moisture at a regular interval over vegetation-covered surfaces. However, future sensors are required to estimate soil moisture from depth up to 0.7–1 m (microwave sensors with P band) over sparse vegetation areas and varying surface roughness using active and passive microwave sensors.

Estimation of soil moisture is significant in various fields like agriculture, climatic studies, hydrology etc. The feasibility of India’s NavIC multipath signal for estimating soil moisture is presented in this paper. The analysis is carried out for carrier to noise ratio (C/N 0 ) of the multipath NavIC signal which depends on reflectivity of signal from the soil surface. Multipath data is collected over a period of 36 days in the month of November and December 2019 and analyzed for its sensitivity towards volumetric soil moisture content. The receiver set up for experimental data collection was placed in agricultural land situated in Dehradun, India. Results show that there is a significant correlation between the phase of the multipath signal and volumetric moisture content of soil.

Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km x 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0−5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE =0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

This paper demonstrates the relationship of surface radar reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) measurements with the Soil Moisture (SM) derived from Soil Moisture Active Passive (SMAP) over India. CYGNSS is an operational eight-satellite constellation, each with a Delay Doppler Mapping Instrument (DDMI) for deriving wind speed in tropical cyclones over oceans. However, these reflections from the land surface, acquired by the DDMI instrument, can also be used to retrieve Soil Moisture (SM). Surface radar reflectivity was derived from Delay Doppler Map (DDM) at specular points generated by CYGNSS measurements as coherent specular reflections over land using radar range equations. From the analysis carried out for selected data sets, a strong linear relationship between CYGNSS derived radar reflectivity and SMAP derived SM has been demonstrated.

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R2) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 m3/m3), followed by Wang & Schmugge model (RMSE = 0.08 m3/m3), Hallikainen model (RMSE = 0.09 m3/m3), Dobson model (RMSE = 0.10 m3/m3) and original SMAP radiometer SM (RMSE = 0.12 m3/m3). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

The SCATSAT-1 satellite data can be used for various applications in the field of agriculture. The main aim of the study is to investigate the water cloud model (WCM) for backscattering simulation by using the field-measured soil moisture in order to validate the SCATSAT-1 measured backscattering. WCM requires various input datasets for simulation of backscattering such as vegetation parameters A and B and soil parameters C and D, which can be estimated by Non-linear least square fitting method by using with experimental dataset. The results showed that the simulated WCM values are well correlated with the backscattering of SCATSAT-1 satellite data. However, it can be further improved when each parameter of WCM is generated by using the ground-based measurements. In this study, some progress has been made toward backscattering simulations using the SCATSAT-1; however, it can be further refined with the advancement in the retrieval algorithms and sensor sensitivity.

Numerical modelling and simulation for Ground-Penetrating Radar (GPR) were done to characterize and quantify the effect of subsurface roughness on GPR performance at 500 MHz operating frequency. An open-source electromagnetic solver was utilized for modelling multiple scenarios with various subsurface roughness and its numerical simulations. The detailed quantitative analysis for GPR signal losses was attempted in different subsurface roughness scenarios by using Normalized Energy (NE) and Signal-to-Clutter Ratio (SCR). This work also included the different fractal window sizes, i.e. variation of height of roughness with mean surface which define dynamic range of roughness. Quantitative results and analysis showed continuous decreasing trend in SCR with respect to increase in subsurface roughness. There was also a significant drop observed in SCR with increasing window size. The overall loss of signal has been quantified with respect to smooth interface. Hypothesis test has also been carried out to ensure and validate the subsurface roughness effect on GPR performance via signal-to-clutter ratio.