Browsing by Author "Manika Gupta"

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Agricultural Water Management: Theories and Practices
(Elsevier, 2020) Prashant K. Srivastava; Manika Gupta; George Tsakiris; Nevil Wyndham Quinn
Agricultural Water Management: Theories and Practices advances the scientific understanding, development and application of agricultural water management through an integrated approach. This book presents a collection of recent developments and applications of agricultural water management from advanced sources, such as satellite, mesoscale and climate models that are integrated with conceptual modeling systems. Users will find sections on drought, irrigation scheduling, weather forecasting, climate change, precipitation forecasting, and more. By linking these systems, this book provides the first resource to promote the synergistic and multidisciplinary activities of scientists in hydro-meteorological and agricultural sciences. As agricultural water management has gained considerable momentum in recent decades among the earth and environmental science communities as they seek solutions and an understanding of the concepts integral to agricultural water management, this book is an ideal resource for study and reference. © 2021 Elsevier Inc. All rights reserved.
Assessment of a Dynamic Physically Based Slope Stability Model to Evaluate Timing and Distribution of Rainfall-Induced Shallow Landslides
(MDPI, 2023) Juby Thomas; Manika Gupta; Prashant K. Srivastava; George P. Petropoulos
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. © 2023 by the authors.
Concepts and methodologies for agricultural water management
(Elsevier, 2020) Prashant K. Srivastava; Swati Suman; Varsha Pandey; Manika Gupta; Ayushi Gupta; Dileep Kumar Gupta; Sumit Kumar Chaudhary; Ujjwal Singh
Water resource management is of paramount importance for sustainable agricultural and socioeconomic development. Agriculture is also one of the prominent factors responsible for the deterioration in the water quality mostly due to poor water management practices and lack of proper knowledge about soil-plant-atmosphere relationship. As such, optimally designed techniques and careful selection of irrigation system can ensure high efficiency and uniform distribution of applied water. Advanced planning and proper management of water could lead us towards sustainable agricultural development with optimal crop production even under physical, environmental, financial and technological restrictions. Therefore, to discuss some of the irrigation-through-computer approaches as a tool for better agricultural water management in this report, we present a detailed description of some of these advanced techniques including decision support systems such as Hydra, Hydrus, DSSAT, CropSyst and MOPECO and irrigation practices such as drip, sprinkler and mulching systems. © 2021 Elsevier Inc. All rights reserved.
Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining
(Springer Science and Business Media Deutschland GmbH, 2019) Prashant K. Srivastava; George P. Petropoulos; Manika Gupta; Sudhir K. Singh; Tanvir Islam; Dimitra Loka
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices. © 2018, Springer Nature Switzerland AG.
Development of android application for visualisation of soil water demand
(Elsevier, 2020) Prashant K. Srivastava; Prachi Singh; Varsha Pandey; Manika Gupta
A real-time and accurate estimation of soil moisture content is a key factor for irrigation water management. For conventional and precision irrigation system, an irrigation demand tool is required that is economical, easy-to-use, has large-scale coverage, provides the users useful information on irrigation requirement and can be accessible on smartphone or wireless sensor platform. Owing to this, the current study aims to develop a user-friendly mobile app for monitoring and visualising irrigation water demand in terms of soil moisture deficit (or SMD). Irrigation Scheduler App is designed by using the Android Studio 3.1.4 software and Java RE 1.8.0 version. The database file of Irrigation Scheduler App contains ground measured soil moisture content and other soil physical properties such as field capacity and texture. Any android phone having code Kitkat, Lolipop, Marshmallow and Nougat support this mobile application. Future efforts will focus on expansion of this study area and updation of the application. © 2021 Elsevier Inc. All rights reserved.
Development of High-Resolution Soil Hydraulic Parameters with Use of Earth Observations for Enhancing Root Zone Soil Moisture Product
(MDPI, 2023) Juby Thomas; Manika Gupta; Prashant K. Srivastava; Dharmendra K. Pandey; Rajat Bindlish
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. © 2023 by the authors.
Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm
(MDPI, 2023) Neelam Agrawal; Himanshu Govil; Gaurav Mishra; Manika Gupta; Prashant K. Srivastava
Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework. © 2023 by the authors.
Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data
(John Wiley and Sons Ltd, 2016) Prashant K. Srivastava; Tanvir Islam; Sudhir K. Singh; George P. Petropoulos; Manika Gupta; Qiang Dai
Sea level rise is a threat to coastal habitation and is corroborating evidence for global warming. The present study investigated the combined use of quantitative forecasting methods for sea level rise using exponential smoothing state space models (ESMs) and an autoregressive integrated moving average (ARIMA) model fed with sea level data over 17 years (1994–2010). Two levels of ESMs were employed: double (model levels with trend) and triple (model levels, trend and seasonal decomposition). The overall data analysis revealed the better performance of ARIMA in terms of index of agreement (d = 0.79), root-mean-square error (RMSE = 32.8 mm) and mean absolute error (MAE = 25.55 mm) than the triple ESM (d = 0.76; RMSE = 39.86 mm; MAE = 35.02 mm) and double ESM (d = 0.14; RMSE = 52.71 mm; MAE = 45.99 mm) models. The present study results suggest that the rate of Arabian Sea level rise is high, and if this is not taken into consideration many coastal areas may become subject to climate-change-induced habitat loss in future. © 2016 Royal Meteorological Society
GIS-based analysis for soil moisture estimation via kriging with external drift
(Elsevier, 2020) Akash Anand; Prachi Singh; Prashant K. Srivastava; Manika Gupta
Spatial distribution analysis of in-situ measurements within a study area using geostatistical approach is always a complex thing to perform. Present study deals with a geostatistical method to map the distribution of soil moisture and soil temperature throughout the study area using Hydra probe in-situ data. As soil moisture plays an important role in short- and long-term meteorological modelling and also is a vital component for sustaining life supporting systems at micro- and mega-scale, it is required to monitor its spatial and temporal variation with high precision. Presently, a multivariate geostatistical approach, i.e., Kriging with External Drift (KED), is used to improve the accuracy of spatial distribution mapping of soil moisture within the study area. Semi-variogram analysis is done to estimate the semi-variance in the model and the stability of the interpolated results. The correlation is established between the observed and predicted soil moisture that has shown R2 of 0.989 and Root Mean Square Error of 0.32, which shows that the model performed very well. © 2021 Elsevier Inc. All rights reserved.
Integrating Soil Hydraulic Parameter and Microwave Precipitation with Morphometric Analysis for Watershed Prioritization
(Springer Netherlands, 2016) Swati Maurya; Prashant K. Srivastava; Manika Gupta; Tanvir Islam; Dawei Han
Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of basin. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric parameters and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998–2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed. © 2016, Springer Science+Business Media Dordrecht.
Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets
(Taylor and Francis Ltd., 2017) Sudhir Kumar Singh; Prashant K. Srivastava; Szilárd Szabó; George P. Petropoulos; Manika Gupta; Tanvir Islam
Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990–2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate measures to conserve this natural ecosystem. © 2016 Taylor & Francis.
Long-term trend analysis of precipitation and extreme events over kosi river basin in india
(MDPI AG, 2021) Prashant K. Srivastava; Rajani Kumar Pradhan; George P. Petropoulos; Varsha Pandey; Manika Gupta; Aradhana Yaduvanshi; Wan Zurina Wan Jaafar; Rajesh Kumar Mall; Atul Kumar Sahai
Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901-2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann-Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation
(Elsevier Ltd, 2022) Sumit Kumar Chaudhary; Prashant K. Srivastava; Dileep Kumar Gupta; Pradeep Kumar; Rajendra Prasad; Dharmendra Kumar Pandey; Anup Kumar Das; Manika Gupta
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. © 2021 COSPAR
Performance evaluation of WRF-Noah Land surface model estimated soil moisture for hydrological application: Synergistic evaluation using SMOS retrieved soil moisture
(Elsevier, 2015) Prashant K. Srivastava; Dawei Han; Miguel A. Rico-Ramirez; Peggy O'Neill; Tanvir Islam; Manika Gupta; Qiang Dai
This study explores the performance of soil moisture data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis datasets using the Weather Research and Forecasting (WRF) mesoscale numerical weather model coupled with the Noah Land surface model for hydrological applications. For evaluating the performance of WRF for soil moisture estimation, three domains are taken into account. The domain with best performance is used for estimating the soil moisture deficit (SMD). Further, several approaches are presented in this study to evaluate the efficiency of WRF simulated soil moisture for SMD estimation and compared against Soil Moisture and Ocean Salinity (SMOS) downscaled and non-downscaled soil moisture. In this study, the first approach is based on the empirical relationship between WRF soil moisture and the SMD on a continuous time series basis, while the second approach is focused on the vegetation cover impact on SMD retrieval, depicted in terms of growing and non-growing seasons. The linear growing and non-growing seasonal model in combination performs well with the NSE = 0.79, RMSE = 0.011 m; Bias = 0.24 m, in comparison to linear model (NSE = 0.70, RMSE = 0.013 m; Bias = 0.01 m). The performance obtained using WRF soil moisture is comparable to SMOS level 2 product but lower than the downscaled SMOS datasets. The results indicate that methodologies could be useful for modelers working in the field of soil moisture information system and SMD estimation at a catchment scale. The study could be useful for ungauged basins that pose a challenge to hydrological modeling due to unavailability of datasets for proper model calibration and validation. © 2015 Elsevier B.V.
Potassium Simulation Using HYDRUS-1D with Satellite-Derived Meteorological Data under Boro Rice Cultivation
(MDPI, 2023) Ayushi Gupta; Manika Gupta; Prashant K. Srivastava; George P. Petropoulos; Ram Kumar Singh
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. © 2023 by the authors.
Preface
(Elsevier, 2020) Prashant K. Srivastava; Manika Gupta; George Tsakiris; Nevil Wyndham Quinn
[No abstract available]
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data
(Springer Science and Business Media B.V., 2021) Prashant K. Srivastava; Manika Gupta; Ujjwal Singh; Rajendra Prasad; Prem Chandra Pandey; A.S. Raghubanshi; George P. Petropoulos
Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. © 2020, Springer Nature B.V.
Soil moisture retrieval from bistatic scatterometer measurements using fuzzy logic system
(CRC Press, 2016) Dileep Kumar Gupta; Rajendra Prasad; Prashant K. Srivastava; Tanvir Islam; Manika Gupta
Many hydrological processes are strongly dependent on the soil moisture content on top of earth surfaces. In the present study, the fuzzy logic algorithms are used for the retrieval of soil moisture using bistatic scatterometer data. For this purpose, the bistatic scatterometer measurements are performed for the rough soil surface at the incidence angles 20° to 70° steps of 5° for HH-and VV-polarization at di erent soil moisture contents. e linear regression analysis is performed between observed soil moisture contents and bistatic scattering coecients for selecting the suitable incidence angle for soil moisture retrieval. e bistatic scattering coecients at 25° incidence angle are selected as the input data sets for the calibration and validation of fuzzy logic models. e performances of fuzzy models are assessed interms of statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcli e Eciency (NSE). e analysis of the above mentioned indices indicates that the fuzzy model at VV-polarization is found better than HH-polarization. © 2017 by Taylor & Francis Group.
Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations
(Frontiers Media SA, 2024) Deepanshu Lakra; Shobhit Pipil; Prashant K. Srivastava; Suraj Kumar Singh; Manika Gupta; Rajendra Prasad
Soil moisture is a fundamental variable in the Earth’s hydrological cycle and vital for development of agricultural water management practices. The present study provided a comprehensive evaluation of a wide range of advanced machine learning algorithms for Soil Moisture (SM) estimation from microwave Synthetic Aperture Radar (SAR) backscatter observations over the wheat fields. From the wheat fields, samplings were performed to collect the in situ datasets on three different dates concurrent to the Sentinel-1 overpasses. The backscattering coefficients were taken as the input variables and SM as the output variable for the training and testing of different models. The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. Specifically, the RF model exhibited outstanding accuracy and robustness in both the training [RMSE (%): 3.44, R-squared: 0.88, correlation: 0.95] and validation phases [RMSE (%): 7.06, R-squared: 0.61, correlation: 0.8], marking it as the most effective model followed by the CNN model with [RMSE (%): 3.9, R-squared: 0.84, correlation: 0.92] during training and [RMSE (%): 8.44, R-squared: 0.43, correlation: 0.67] for validation, highlighting challenges in the model generalisation. Copyright © 2025 Lakra, Pipil, Srivastava, Singh, Gupta and Prasad.
Soil water content influence on pesticide persistence and mobility
(Elsevier, 2020) Manika Gupta; N.K. Garg; Prashant K. Srivastava
This chapter has been focused on to understanding the persistence and mobility of selected pesticides in the unsaturated soil zone under varying irrigation treatments. The movement of the pesticide can be determined in subsurface through open source software like HYDRUS-1D. The results of various studies in technical literature have shown that numerically simulated outputs through HYDRUS-1D have a good agreement with the experimental results. Through model simulations, regulation strategies can be suggested towards safe dosages of pesticides with respect to different irrigation treatments. This chapter will provide a step-by-step procedure towards numerical simulation of pesticide with the usage of HYDRUS-1D and can be utilised for safeguarding water requirements in the agricultural fields. © 2021 Elsevier Inc. All rights reserved.