Browsing by Author "Dharmendra Kumar Pandey"

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Active-Passive Approach for NISAR High Resolution Soil Moisture Products: Retrieval and Accuracy Assessment over Indian Cropland
(Institute of Electrical and Electronics Engineers Inc., 2021) Dharmendra Kumar Pandey; Srinivasa Teja Noothi; M. Shashi; Prashant K. Srivastava; Anup Das; Om Pal; Kapil Rohilla; Ravindra Prawasi; Nijbul H. Sekh; Sushma Bisht; Deepak Putrevu; Arundhati Misra; Raj Kumar
Soil moisture is an essential variable in agricultural applications for irrigation scheduling, crop water requirements and pest management etc. However, currently available global satellite microwave radiometer derived soil moisture products are inadequate due to coarse spatial resolution for such applications. In order to improve spatial resolution, SMAP (Soil Moisture Active Passive) mission has first time demonstrated the potential of combining microwave radiometer and radar data based on Active-Passive approach to produce 3-9 km soil moisture globally. However, this approach has not been well tested quantitatively at sub km (<1km) grid resolution using spaceborne observations. In this work, we adopted and tested active-passive approach to integrate radiometer derived coarse resolution soil moisture (SMAP L-band radiometer) with fine resolution radar backscatter (Sentinel-1 C-band SAR) to downscale soil moisture at multiple grid resolution (100 m, 500m and 1000 m). The impact of scale on the robustness of the algorithm is analyzed by assessing the soil moisture retrieval accuracy. Detailed validation was attempted using Multi-Scale Field Sampling Framework (MFSF) over selected agricultural cropland study site. The results obtained are very encouraging, showing the potential of Active-Passive approach for high spatial soil moisture with correlation of 0.75, 0.76 & 0.68 and ubRMSE of 4.94%, 6.07% & 7.21% for 100m, 500m and 1km respectively. Based on the above assessment as pre-cursor study, Active-Passive approach will be the potential candidate for utilizing NISAR data to deliver high spatial resolution soil moisture operational products along with radiometer. © 2021 IEEE.
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.
Assessment of SCATSAT-1 Backscattering by Using the State-of-the-Art Water Cloud Model
(Springer, 2020) Ujjwal Singh; Prashant K. Srivastava; Dharmendra Kumar Pandey; Sasmita Chaurasia
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. © Springer Nature Singapore Pte Ltd. 2020.
Crop phenology and soil moisture applications of SCATSAT-1
(Indian Academy of Sciences, 2019) Nilima R. Chaube; Sasmita Chaurasia; Rojalin Tripathy; Dharmendra Kumar Pandey; Arundhati Misra; B.K. Bhattacharya; Prakash Chauhan; Kiran Yarakulla; G.D. Bairagi; Prashant Kumar Srivastava; Preeti Teheliani; S.S. Ray
SCATSAT-1 measures the backscattering coefficient over land surfaces, which is a function of vegetation structure, surface roughness, soil moisture content, incidence angle and dielectric properties of vegetation. Scatterometer image reconstruction techniques provide fine resolution data to explore the emerging applications over land by using ambiguous backscatter from land. In this paper, 2 km resolution products of ISRO's scatterometer SCATSAT-1 are exploited for land target detection, rice crop phenology stages detection for kharif and rabi seasons and estimation of relative soil moisture over parts of India. Temporal and spatial backscatter changes are due to seasonal and changes in Land Use Land Cover. Crop phenology stages such as transplanting, maximum tillering, panicle emergence and physiological maturity stages are identified by analysing SCATSAT-1 time series along with NDVI and findings are supported by appropriate ground truth observations and crop cutting experiment (CCE) data. The relative soil moisture change detection is validated with in situ ground truth measurements using Hydraprobe, carried for SCATSAT-1 ascending and descending passes. © 2019 Current Science Association, Bengaluru.
Development and characterization of micelles for nucleolin-targeted co-delivery of docetaxel and upconversion nanoparticles for theranostic applications in brain cancer therapy
(Editions de Sante, 2023) Mahima Chauhan; Rahul Pratap Singh; Sonali; Bhavna Yadav; Saurabh Shekhar; Abhitinder Kumar; Abhishesh Kumar Mehata; Amit Kumar Nayak; Rohit Dutt; Vandana Garg; Vikas Kailashiya; Madaswamy S. Muthu; Biplob Koch; Dharmendra Kumar Pandey
Despite the existence of several treatment modalities and advancements in cancer research, brain cancer is still incurable. Over-expression of nucleolin receptors on cancer cells has been explored in several studies. The study aimed to develop and characterize nucleolar -targeted theranostic pluronic F127-TPGS micelles for brain cancer therapy. The theranostic agents i.e., Docetaxel; DTX as a therapeutic agent, and the upconversion nanoparticles; UCNP as a diagnostic agent, were loaded into micelles by a slightly-modified solvent casting method. Micelles were further decorated with synthesized TPGS-AS1411 aptamer conjugate for targeting brain cancer cells. The prepared micelles were found between 90 and 165 nm, with a uniform homogeneous and narrow distribution in formulations. DTX and UCNP encapsulation efficiencies of micelles were found 74–88% and 38–40%, respectively. Micelles have depicted sustained release of DTX for as long as 72 h. Hemolytic assay confirmed that DUTP-AS1411 aptamer micelles were found more biocompatible than Taxotere®. The cytotoxicity results revealed that DTP, DUTP, and DUTP-AS1411 aptamer micelles achieved 4.20, 11.70, and 17.54-fold more effectiveness than Taxotere®, after 24 h of therapy, respectively. In addition, DUTP-AS1411 aptamer micelles achieved higher tmax and Cmax of DTX up to 8- and 1.5-fold, respectively, compared to Taxotere® treated group. A similar trend was observed for the brain-distribution study as DUTP-AS1411 aptamer micelles were found more efficacious than Taxotere®. The histopathology studies showed no toxicity and cellular damage even after the 14th and 28th day post i.v. administration of normal saline, DTP, DUTP, and DUTP-AS1411 aptamer micelles formulations whereas Taxotere® has reported to cause toxicity in brain tissues. The study revealed that DUTP-AS1411 aptamer micelles inherit promising and improved therapeutic efficacy, reduced toxicity, dosing frequency, and sustained drug release behavior which can be further exploited as a potential therapeutic approach for brain cancer. © 2023 Elsevier B.V.
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.
Improved radar vegetation water content integration for SMAP soil moisture retrieval
(Elsevier B.V., 2025) J. Sharma; Rajendra B. Prasad; Prashant Kumar Srivastava; Shubham Kumar Singh; Suraj A. Yadav; Dharmendra Kumar Pandey
The Vegetation Water Content (VWC) serves as a crucial parameter within the framework of the Soil Moisture Active Passive (SMAP) satellite mission, particularly in its utilization for vegetation optical depth estimation in the Single Channel Algorithm (SCA) to determine soil moisture content. This study attempts to enhance the soil moisture estimation by estimating microwave VWC utilizing the Single Look Complex (SLC) format of dual-polarized Sentinel-1 data. This approach aims to refine the efficacy of the Single Channel Algorithm (SCA), thereby elevating the precision and reliability of soil moisture estimations. The Sentinel-1 datasets have been utilized to compute radar indices, particularly the Dual Polarimetric Radar Vegetation Index (DPRVI), Radar Vegetation Index (RVI), and Cross- and Co-Polarized Ratio (CCR). DPRVI reflects vegetation's growth and moisture properties, while RVI and CCR indicate vegetation water content and health status. The radar indices were employed within regression approaches such as random forest (RF), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and linear regression to estimate VWC. The performance of DPRVI was found better to capture aspects of vegetation dynamics and effectively estimates VWC values with a high correlation (R2) of 0.59. Furthermore, the DPRVI-estimated VWC values are integrated into the SCA, a renowned method for soil moisture retrieval. The results of SCA are compared to the ground-measured soil moisture along with the already available SMAP L2-enhanced passive soil moisture product. The soil moisture estimation via SCA integrated with the DPRVI-estimated VWC enhances the soil moisture estimations with an accuracy of (RMSE = 0.042 m3/m3 and ubRMSE = 0.039 m3/m3) compared to the SMAP L2 soil moisture. This integration allows for a more comprehensive understanding of soil-vegetation-atmosphere interactions and improves the accuracy of soil moisture assessments, critical for hydrological modeling, agricultural management, and environmental monitoring efforts. © 2024 Elsevier B.V.
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
MACHINE LEARNING BASED SOIL MOISTURE RETRIEVAL ALGORITHM AND VALIDATION AT SELECTED AGRICULTURAL SITES OVER INDIA USING CYGNSS DATA
(Institute of Electrical and Electronics Engineers Inc., 2021) Shivani Tyagi; Dharmendra Kumar Pandey; Deepak Putrevu; Prashant K. Srivastava; Arundhati Misra
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 0:057m3=m3 during pre-monsoon and 0:65 and 0:053m3=m3 in post monsoon periods, respectively. © 2021 IEEE.
Multipath phase based vegetation correction scheme for improved field-scale soil moisture retrieval over agricultural cropland using GNSS-IR technique
(Elsevier Ltd, 2024) Sushant Shekhar; Rishi Prakash; Dharmendra Kumar Pandey; Anurag Vidyarthi; Prashant K. Srivastava; Deepak Putrevu; Nilesh M. Desai
Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.). © 2024 COSPAR
Operational 500 m surface soil moisture product using EOS-04 C-band SAR over Indian agricultural croplands
(Indian Academy of Sciences, 2024) Dharmendra Kumar Pandey; Prashant Kumar Srivastava; Rucha Dave; Raj K. Setia; Ompal; Rajiv Sinha; Muddu Sekhar; Manish Parmar; Shubham Gupta; Deepak Putrevu; Raghav Mehra; V. Ramanujam; Bimal Kumar Bhattacharya; Raj Kumar
Surface soil moisture (SSM) at high spatial resolution is an essential land parameter for agricultural applications like irrigation mapping, scheduling, crop water stress assessment, etc. However, available satellite derived soil moisture products are inadequate for meeting the requirements of agricultural applications due to coarse scale soil moisture (~10–40 km). In this article, we developed an operational framework for first of its kind sub-km (~500 m) operational soil moisture product over India by utilizing ISRO’s EOS-04 C-band synthetic aperture radar (SAR) data based on active-passive approach. The potential of EOS-04 SAR for sub-km scale is demonstrated and tested over major cropland sites covering highly heterogeneous and dynamic crop conditions in different agro-climatic regions over India which shows a good agreement with in situ datasets with mean ubRMSE, ranging from 0.051 to 0.078 m3/m3. © (2024), (Indian Academy of Sciences). All rights reserved.
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.
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.