Browsing by Author "Sumit Kumar Chaudhary"

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Artificial neural network for the estimation of soil moisture using earth observation datasets
(Elsevier, 2020) Sumit Kumar Chaudhary; Jyoti Sharma; Dileep Kumar Gupta; Prashant K. Srivastava; Rajendra Prasad; Dharmendra Kumar Pandey
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. © 2021 Elsevier Inc. All rights reserved.
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.
Evaluation of Radar/Optical Based Vegetation Descriptors in Water Cloud Model for Soil Moisture Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2021) Sumit Kumar Chaudhary; Dileep Kumar Gupta; Prashant K. Srivastava; Dharmendra Kumar Pandey; Anup Kumar Das; Rajendra Prasad
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 m3m-3 followed by RVI, cross-polarized and LAI with 0.100 m3m-3 , 0.124 m3m-3 and 0.124 m3m-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. © 2001-2012 IEEE.
Future challenges in agricultural water management
(Elsevier, 2020) Sumit Kumar Chaudhary; Prashant K. Srivastava
Agriculture is the prime industry essential for the survival of human beings. It requires enormous water resources to improve productivity. The huge and unorganised use of water resources poses a threat of scarcity of water in many regions of the Earth. The scarcity of water resources also plays a very significant role in climate change. Thus, the management of water in agriculture sector is very vital and needs attention immediately. In this paper, possible future challenges in agriculture water management are emphasised. © 2021 Elsevier Inc. All rights reserved.
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
Passive Only Microwave Soil Moisture Retrieval in Indian Cropping Conditions: Model Parameterization and Validation
(Institute of Electrical and Electronics Engineers Inc., 2023) Dileep Kumar Gupta; Prashant K. Srivastava; Dharmendra Kumar Pandey; Sumit Kumar Chaudhary; Rajendra Prasad; Peggy E. O'Neill
The present study carried out to parameterize the single channel soil moisture active passive (SMAP) passive soil moisture (SM) retrieval algorithm, over Indian conditions. The moderate resolution imaging spectroradiometer (MODIS) data products and soil texture data were used for an improved parameterization of the algorithm. The bias correction was applied to the MODIS leaf area index (LAI) for accurate computation of vegetation optical depth. The necessary vegetation and roughness parameter were calibrated through minimization of the error between model retrieved and ground measured SM. The value of root mean square error (RMSE) for retrieved SM was found as 0.059m3m-3 with bias and correlation coefficients of 0.036m3m-3 and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE = 0.059m3m-3, bias = 0.024m3m-3, and correlation coefficients = 0.752) for descending overpass. The same method is also implemented on two other test sites in different regions of India to check the model robustness, which indicates that the current parameterization provides a better estimate of SM over croplands in India. The overall performance of new parameterized model is found as (RMSE = 0.052 and bias = 0.034) for ascending and descending (RMSE = 0.048 and bias = 0.026) satellite overpasses for all the three test sites. Additionally, the intercomparing of various operational SM products SMAP SM (L2_SM_P), Soil Moisture and Ocean Salinity (SMOS) SM (SMOS_L3_SM), and SMOS-IC data products was carried out with the SAC-ISRO PAN India SM network, which showed a significant RMSE, dry and wet biases over all three test sites as compared to the developed improved parameterized algorithm. © 1980-2012 IEEE.
Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system
(Taylor and Francis Ltd., 2022) Akash Anand; Anand Singh Dinesh; Prashant K. Srivastava; Sumit Kumar Chaudhary; Atul Kumar Varma; Pavan Kumar
Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation over Cropland
(Institute of Electrical and Electronics Engineers Inc., 2020) Ujjwal Singh; Prashant K. Srivastava; Dharmendra Kumar Pandey; Sasmita Chaurasia; Dileep Kumar Gupta; Sumit Kumar Chaudhary; Rajendra Prasad; A.S. Raghubanshi
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 (R2), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al., the values of R2, 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. © 2004-2012 IEEE.