BHU Institutional Repository :: Browsing by Author "Gupta, Dileep Kumar"

Browsing by Author "Gupta, Dileep Kumar"

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Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India
(MDPI, 2023) Awasthi, Nitesh; Tripathi, Jayant Nath; Petropoulos, George P.; Gupta, Dileep Kumar; Singh, Abhay Kumar; Kathwas, Amar Kumar; Srivastava, Prashant K.
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. � 2023 by the authors.
Assessment of climatic impact on growth and production of rice (Kharif) and wheat (Rabi) using geospatial technology over Haryana
(India Meteorological Department, 2023) Awasthi, Nitesh; Tripathi, Jayant Nath; Dakhore, K.K.; Gupta, Dileep Kumar; Kadam, Y.E.
Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI-weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI-weather and LAI-weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare and rice crop yield 0.029 tons per hectare. � 2023, India Meteorological Department. All rights reserved.
Atmospheric Aerosol and weather vulnerability on Maize production in India
(Institute of Electrical and Electronics Engineers Inc., 2022) Gupta, Dileep Kumar; Pramanick, Subhajit; Singh, Abhay Kumar
The gradual increment of anthropogenic aerosol pollutants, changes the properties of atmosphere and causes a significantly negative impacts on agriculture. The present study evaluated the long term aerosols and weather impact on the maize yield during the time period from the years 1998 to 2019 in India. The multiple linear regression analysis is carried out between the dependent variable (maize yield) and independent variables (AOD and weather). The performance of multiple linear regression analysis is found excellent between the dependent and independent variables in terms of the coefficient of determination (R2=0.945), root mean squared error (RMSE = 0.515 tons/hectare) and bias (0.0 tons/hectare). The atmospheric aerosols impact on the weather parameters is also evaluated because the changes in weather parameters due to aerosol is also affected the crop yield. The linear regression analysis is carried out to evaluate the aerosols impact on weather parameters and it is observed that the yearly increases in AOD impacts more on temperature variations than the fraction of absorbed photosynthetically active radiation (FAPAR) variation. The overall loss in maize yield is found approximately 8.8% per year due to variations in the weather variables with the increment of anthropogenic aerosol pollutants during 1998-2019 over India. In future, the climate warming and increment of aerosols may have differing impacts on crop yield and should be jointly considered in any assessment of Indian food security. � 2022 International Radio Science Union (URSI).
Challenges in Radar remote sensing
(Elsevier, 2022) Srivastava, Prashant K.; Prasad, Rajendra; Chaudhary Kumar, Sumit; Yadav, Suraj A.; Sharma, Jyoti; Suman, Swati; Pandey, Varsha; Singh, Rishabh; Gupta, Dileep Kumar
This chapter provides different challenges that are generally faced by the radar remote sensing community. The different types of challenges of radar remote sensing in biochemical and biophysical parameter retrieval, flood detection and monitoring, soil moisture, snow, droughts, sensor development, and instrumentation are briefly provided. � 2022 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) Chaudhary, Sumit Kumar; Gupta, Dileep Kumar; Srivastava, Prashant K.; Pandey, Dharmendra Kumar; Das, Anup Kumar; Prasad, Rajendra
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.
Fuzzy logic for the retrieval of kidney bean crop growth variables using ground-based scatterometer measurements
(Elsevier, 2022) Gupta, Dileep Kumar; Prasad, Rajendra; Kumar, Pradeep; Srivastava, Prashant K.
This study is designed to explore the potential of ?� and fuzzy logic algorithms for the accurate retrieval of kidney bean crop variables at innumerable growth stages using ground-based multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic radar system may be a better choice for retrieving crop growth variables than a monostatic radar system because of its great advantages The transmitter and receiver are set opposite each other or in different azimuthal configurations in the case of a bistatic scatterometer system. For this purpose, an outdoor kidney bean crop bed of 4�4m2 was specially prepared near the Department of Physics, Indian Institute of Technology (BHU), Varanasi, India. Bistatic scatterometer measurements are accomplished at innumerable growth stages of the kidney bean crop for incidence angle of 20 to 70 degrees at horizontal transmit�horizontal receive (HH) and vertical transmit�vertical receive (VV) polarization in the specular direction at an azimuthal angle (?=0). Kidney bean crop variables such as the vegetation water content, leaf area index, and plant height measurements are also taken at the time of each bistatic scatterometer measurement. The sensitivity of bistatic scattering coefficients at HH and VV polarization with the kidney bean crop variable is evaluated using Pearson correlation analysis to retrieve kidney bean crop variables. The higher sensitivities of ?� with the kidney bean crop growth variables are at 50 and 40 degrees incidence angles for HH and VV polarizations, respectively. The ?� does not exhibit severe saturation effects when the crops are mature and has better sensitivity to crop growth variables. The retrieved values of crop growth variables by fuzzy inference system are close to the in situ values of crop growth variables. Outcomes of this study are beneficial for the scientific community for making policy decisions regarding food security and environmental issues. � 2022 Elsevier Inc. All rights reserved.
Introduction to RADAR remote sensing
(Elsevier, 2022) Gupta, Dileep Kumar; Prashar, Shivendu; Singh, Sartajvir; Srivastava, Prashant K.; Prasad, Rajendra
For several decades, remote sensing has had a crucial role in various fields. With the increase in the quality of earth-observing sensors, the ability of observers has been revolutionized. There are two main types of remote sensing: optical-based and microwave. Optical-based remote sensing measures reflected solar illumination from the object but cannot penetrate clouds and has applicability only in the daytime, whereas microwave-based remote sensing can be imaged the earth's surface during both daytime and nighttime and in almost all weather conditions. This study focused on microwave remote sensing and its fundamentals. Microwave remote sensing can be categorized as (1) active remote sensing that provides its source of microwave radiation to illuminate the object on the land surface and detect the backscattered cross-section of a particular land type, and (2) passive remote sensing that detects naturally emitted microwave energy within its field of view. In this chapter, we provide a brief overview of microwave remote sensing and review its applications. This study helps researchers understand the basics of microwave remote sensing and various advances in the applicability of microwave remote sensing. � 2022 Elsevier Inc. All rights reserved.
Linkage between the vegetation indices and climate factors over Haryana
(Association of Agrometeorologists, 2022) Awasthi, Nitesh; Tripathi, Jayant Nath; Dakhore, K.K.; Gupta, Dileep Kumar; Kadam, Y.E.
Present study was an attempt to study the relationship of Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) with climatic parameters (maximum temperature, minimum temperature, relative humidity, rainfall, wind speed and aerosol optical depth) over the Indian state of Haryana using MODIS derived vegetation indices on monthly and yearly values for the time period from 2010 to 2020. The values of correlations coefficients of NDVI and LAI with climatic variables varied with the months, the nature of their variation was similar for two indices. During summer season the correlation values were maximum while these were minimum during rainy season. The overall correlation analysis revealed that the rainfall and relative humidity were positively correlated with NDVI and LAI, while the remaining climate variables had negative impact on the NDVI and LAI. � 2022, Association of Agrometeorologists. All rights reserved.
Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation
(Elsevier Ltd, 2022) Chaudhary, Sumit Kumar; Srivastava, Prashant K.; Gupta, Dileep Kumar; Kumar, Pradeep; Prasad, Rajendra; Pandey, Dharmendra Kumar; Das, Anup Kumar; Gupta, Manika
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
Microwave components and devices for RADAR systems
(Elsevier, 2022) Kumar, Vikram; Gupta, Dileep Kumar
This chapter discusses the essential components of microwave systems, which are relevant for designing a microwave radar measurement setup. The ways in which transmission lines are used in planar and waveguides lead to requirement for other components in planar or waveguide-based design. With a discussion on some primary antennas, the chapter reviews microwave absorbers and their characteristics. The power dissipation equation is established regarding the absorbing material. A few important microwave sources are discussed. The need for a mode converter in a microwave system leads to the necessary modes obtained from source-generated modes. A network analyzer is also discussed, which has the ability to measure the reflection loss and transmission loss of microwave power. � 2022 Elsevier Inc. 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) Gupta, Dileep Kumar; Srivastava, Prashant K.; Pandey, Dharmendra Kumar; Chaudhary, Sumit Kumar; Prasad, Rajendra; O'Neill, Peggy E.
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.
Performance Assessment of Global-EO-Based Precipitation Products against Gridded Rainfall from the Indian Meteorological Department
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Awasthi, Nitesh; Tripathi, Jayant Nath; Petropoulos, George P.; Gupta, Dileep Kumar; Singh, Abhay Kumar; Kathwas, Amar Kumar
Monitoring water resources globally is crucial for forecasting future geo-hydro disasters across the Earth. In the present study, an attempt was made to assess the functional dimensionality of multi-satellite precipitation products, retrieved from CHIRPS, NASA POWER, ERA-5, and PERSIANN-CDR with respect to the gridded India Meteorological Department (IMD) precipitation dataset over a period of 30+ years (1990�2021) on monthly and yearly time scales at regional, sub regional, and pixel levels. The study findings showed that the performance of the PERSIANN-CDR dataset was significantly better in Central India, Northeast India, and Northwest India, whereas the NASA-POWER precipitation product performed better in Central India and South Peninsular of India. The other two precipitation products (CHIRPS and ERA-5) showed the intermediate performance over various sub regions of India. The CHIRPS and NASA POWER precipitation products underperformed from the mean value (3.05 mm/day) of the IMD gridded precipitation product, while the other two products ERA-5 and PERSIANN-CDR are over performed across all India. In addition, PERSIANN-CDR performed better in Central India, Northeast India, Northwest India, and the South Peninsula, when the yearly mean rainfall was between 0 and 7 mm/day, while ERA-5 performed better in Central India and the South Peninsula region for a yearly mean rainfall above 0�7 mm/day. Moreover, a peculiar observation was made from the investigation that the respective datasets were able to characterize the precipitation amount during the monsoon in Western Ghats. However, those products needed a regular calibration with the gauge-based datasets in order to improve the future applications and predictions of upcoming hydro-disasters for longer time periods with the very dense rain gauge data. The present study findings are expected to offer a valuable contribution toward assisting in the selection of an appropriate and significant datasets for various studies at regional and zonal scales. � 2023 by the authors.
Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classification
(Elsevier, 2022) Suman, Swati; Srivastava, Prashant K.; Petropoulos, George P.; Avtar, Ram; Prasad, Rajendra; Singh, Sudhir Kumar; Mustak, S.K.; Faraslis, Ioannis N.; Gupta, Dileep Kumar
Owing to its large spatial and periodic temporal coverage, satellite remote sensing has emerged for formulating and implementing strategies for natural resources management. This study focuses on an appraisal of satellite sensors and artificial intelligence techniques such as kernels-based support vector machines (SVMs) and artificial neural networks (ANNs). These methods are used for land cover classification on multispectral and microwave satellite images acquired from Landsat-8 and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR) over Varanasi, India. The analysis shows comparable the performance of the microwave-classified image compared with the multispectral Landsat-8 image. ANNs and SVMs performed best with an overall accuracy of 97.3% and kappa coefficient of 0.97 for the Landsat-8 image, whereas SVM radial basis function was the best classifier for the ALOS PALSAR image with 94% overall accuracy. Other statistical indices such as kappa total disagreement and allocation disagreement scores revealed similar performances. The analysis demonstrated the ability of microwave data in land cover classification studies with satisfactory performance. These data can be used in nearly all weather and environmental conditions for broad image classification purposes when optical and infrared imagery such as Landsat are unavailable. � 2022 Elsevier Inc. All rights reserved.
Radar Remote Sensing: Applications and Challenges
(Elsevier, 2022) Srivastava, Prashant K.; Gupta, Dileep Kumar; Islam, Tanvir; Han, Dawei; Prasad, Rajendra
Radar Remote Sensing: Applications and Challenges advances the scientific understanding, development, and application of radar remote sensing using monostatic, bistatic and multi-static radar geometry. This multidisciplinary reference pulls together a collection of the recent developments and applications of radar remote sensing using different radar geometry and platforms at local, regional and global levels. Radar Remote Sensing is for researchers and practitioners with earth and environmental and meteorological sciences, who are interested in radar remote sensing in ground based scatterometer and SAR systems; air borne scatterometer and SAR systems; space borne scatterometer and SAR systems. � 2022 Elsevier Inc. All rights reserved.
Smap soil moisture product assessment over wales, u.K., using observations from the wsmn ground monitoring network
(MDPI, 2021) Gupta, Dileep Kumar; Srivastava, Prashant K.; Singh, Ankita; Petropoulos, George P.; Stathopoulos, Nikolaos; Prasad, Rajendra
Soil moisture (SM) is the primary variable regulating the soil temperature (ST) differences between daytime and night-time, providing protection to crop rooting systems against sharp and sudden changes. It also has a number of practical applications in a range of disciplines. This study presents an approach to incorporating the effect of ST for the accurate estimation of SM using Earth Observation (EO) data from NASA�s SMAP sensor, one of the most sophisticated satellites currently in orbit. Linear regression analysis was carried out between the SMAP-retrieved SM and ground-measured SM. Subsequently, SMAP-derived ST was incorporated with SMAP-derived SM in multiple regression analysis to improve the SM retrieval accuracy. The ability of the proposed method to estimate SM under different seasonal conditions for the year 2016 was evaluated using ground observations from the Wales Soil Moisture Network (WSMN), located in Wales, United Kingdom, as a reference. Results showed reduced retrieval accuracy of SM between the SMAP and ground measurements. The R2 between the SMAP SM and ground-observed data from WSMN was found to be 0.247, 0.183, and 0.490 for annual, growing and non-growing seasons, respectively. The values of RMSE between SMAP SM and WSMN observed SM are reported as 0.080 m3m?3, 0.078 m3m?3 and 0.010 m3m?3, with almost zero bias values for annual, growing and non-growing seasons, respectively. Implementation of the proposed scheme resulted in a noticeable improve-ment in SSM prediction in both R2 (0.558, 0.440 and 0.613) and RMSE (0.045 m3m?3, 0.041 m3m?3 and 0.007 m3m?3 ), with almost zero bias values for annual, growing and non-growing seasons, respectively. The proposed algorithm retrieval accuracy was closely matched with the SMAP target accuracy 0.04 m3m?3 . In overall, use of the new methodology was found to help reducing the SM difference between SMAP and ground-measured SM, using only satellite data. This can provide important assistance in improving cases where the SMAP product can be used in practical and research applications. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Snow Cover Response to Climatological Factors at the Beas River Basin of W. Himalayas from MODIS and ERA5 Datasets
(2023) Sunita; Gupta, Pardeep Kumar; Petropoulos, George P.; Gusain, Hemendra Singh; Sood, Vishakha; Gupta, Dileep Kumar; Singh, Sartajvir; Singh, Abhay Kumar
Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.
Spatiotemporal Vegetation Variability and Linkage with Snow-Hydroclimatic Factors in Western Himalaya Using Remote Sensing and Google Earth Engine (GEE)
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Singh, Dhiraj Kumar; Singh, Kamal Kant; Petropoulos, George P.; Boaz, Priestly Shan; Jain, Prince; Singh, Sartajvir; Gupta, Dileep Kumar; Sood, Vishakha
The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001�2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = ?0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya. � 2023 by the authors.
Theory of monostatic and bistatic radar systems
(Elsevier, 2022) Yadav, Suraj A.; Gupta, Dileep Kumar; Prasad, Rajendra; Sharma, Jyoti; Srivastava, Prashant K.
This chapter discusses radar bands and radar system configurations used for various applications of earth exploration. Because theoretical modeling in radar sensing deals with the modeling of radar cross-section or scattering coefficients, fundamental mathematical concepts for radar cross-section or scattering coefficients measurements of point targets and distributive targets using monostatic (i.e., backward scatter alignment convention) and bistatic (i.e., forward scatter alignment convention) radar system configurations are discussed in detail. In defining the radar cross-section or scattering coefficients of target, the amplitude calibration and characterization of radar system are necessary to obtain a meaningful radar cross-section. Four well-known and most common amplitude calibrations are discussed in this chapter. The subsystem characterization of radar system parameters is also discussed. Finally, we discuss the basic procedures of the measurement system and important precautions to take for meaningful radar data acquisition. � 2022 Elsevier Inc. All rights reserved.

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