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Background: The mandate of journal metrics is to measure the performance of journals indexed in the concerned databases based on their citation impact. Such measurements are essential to understand the quality of research output and assess the quality of the journals in which such research gets reported. There are several such journal metrics in use and include Journal Impact Factors (JIF), CiteScore, Article Influence Factor, Eigenfactor (EF), Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), h-index, i-10 index, Altmetric Attention Score, and PlumX metrics. Journal metrics help decide which journal to choose to publish one's manuscript in. Objective: The purpose of this paper is to highlight the Scopus journal metrics of Indian Earth and Planetary Sciences journals and to identify the top Indian Earth and Planetary Sciences journals based on their journal indices in Scopus for the period 2011 to 2020. Method: Data on the Scopus-indexed Indian Earth and Planetary Sciences journals from SCImago Journal and Country Rank databases available at https://www.scimagojr.com/journalrank.php was collected. These journals' metrics were collected from Scopus Source List available at https://www.scopus.com/sources.uri. Results: Indian Earth and Planetary Sciences Journals in Scopus are ranked based on their journal metrics in Scopus for the period 2011 to 2020. Conclusion: Based on the bibliometric parameters used, the Journal of Geological Society of India and the Journal of Earth System Sciences are the most impactful journals in Earth Science published in India. Journal of the Indian Society of Remote Sensing follows. Both journals are published on behalf of the most influential scientific societies in Earth Science in India by Springer. Several Indian Earth and Planetray Science journals have over four decades of publishing records but have impact factors <1.5. When the journal's prestige is considered, these three journals are joined by the Journal of Astrophysics & Astronomy also. [ABSTRACT FROM AUTHOR]

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing "crop management", "soil management", and "water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. [ABSTRACT FROM AUTHOR]

With advances in social development and economic growth, remote sensing technology has been attracted greater attention in monitoring the earth data using radar and optical sensors on satellite platforms for a wide range of applications in different fields such as coastal, hazard and natural resources. Satellite images could play a greater role in improving classification accuracy with high spatial resolution and rich spectral information for land cover classification. However, existing image fusion methods achieves low accuracy due to large-scale feature space. To focus on these issues, a deep learning network structure needs to classify different classes with high spatial resolution and rich spectral information to obtain higher accuracy. In this paper, a feature-based classification approach is proposed namely Multi-Fusion based Dense Transpose Convolutional layer in Fully Convolutional Network with Feature Alignment framework (MF-DTCFCN) to label and categorizes the label region in Remote Sensing Images (RSI). Initially, a multi-fusion feature framework is designed by adding a point-wise addition structure to handle large-scale feature space for high-resolution images. Secondly, the optimized features are pre-trained to classify the labels comprised of the most discriminative features in the pre-training network. The density of output label maps are improved by introducing dense transpose convolution in the network. Then combine the output to the feature alignment with point-wise addition is employed to balance the different features and similarities to achieve additional performance for classification. Here, the Land Use/land Cover (LULC) satellite image dataset namely, Sentinel-2 were used to classify the urban areas of Hyderabad city, India. Experimental results depict that the MF-DTCFCN approach outperforms an accurate improvement in classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

A better understanding of aerosol–cloud interaction processes is important to quantify the role of clouds and aerosols on the climate system. There have been significant efforts to explain the ways aerosols modulate cloud properties. However, from the observational point of view, it is indeed challenging to observe and/or verify some of these processes because no single instrument or platform has been proven to be sufficient. Discrimination between aerosol and cloud is vital for the quantification of aerosol–cloud interaction. With this motivation, a set of observational field campaigns named balloon-borne aerosol–cloud interaction studies (BACIS) is proposed and conducted using balloon-borne in situ measurements in addition to the ground-based (lidar; mesosphere, stratosphere and troposphere (MST) radar; lower atmospheric wind profiler; microwave radiometer; ceilometer) and space-borne (CALIPSO) remote sensing instruments from Gadanki (13.45 ∘ N, 79.2 ∘ E), India. So far, 15 campaigns have been conducted as a part of BACIS campaigns from 2017 to 2020. This paper presents the concept of the observational approach, lists the major objectives of the campaigns, describes the instruments deployed, and discusses results from selected campaigns. Balloon-borne measurements of aerosol and cloud backscatter ratio and cloud particle count are qualitatively assessed using the range-corrected data from simultaneous observations of ground-based and space-borne lidars. Aerosol and cloud vertical profiles obtained in multi-instrumental observations are found to reasonably agree. Apart from this, balloon-borne profiling is found to provide information on clouds missed by ground-based and/or space-borne lidar. A combination of the Compact Optical Backscatter AerosoL Detector (COBALD) and Cloud Particle Sensor (CPS) sonde is employed for the first time in this study to discriminate cloud and aerosol in an in situ profile. A threshold value of the COBALD colour index (CI) for ice clouds is found to be between 18 and 20, and CI values for coarse-mode aerosol particles range between 11 and 15. Using the data from balloon measurements, the relationship between cloud and aerosol is quantified for the liquid clouds. A statistically significant slope (aerosol–cloud interaction index) of 0.77 found between aerosol backscatter and cloud particle count reveals the role of aerosol in the cloud activation process. In a nutshell, the results presented here demonstrate the observational approach to quantifying aerosol–cloud interactions. [ABSTRACT FROM AUTHOR]

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman's rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters. [ABSTRACT FROM AUTHOR]

The rapid growth in urban population in India is seen to create an essential for the development of more urban infrastructures. Land surface temperature (LST) is a significant factor in many areas like climate change, urban land use/land cover (LULC), heat balance studies and a key input for climate models. The main objective of this paper is to examine multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Gaziabad district in Uttar Pradesh, India using LANDSAT satellite data in GIS platform. To compute the changes and relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), Landsat LST data for the months of September of year 2000, 2011 and 2018 were used in this study. The LST has been estimated with respect to Normalized Difference Vegetation Index (NDVI) values determined from the Red and Near Infrared bands. The Land Surface Emissivity (LSE) is retrieved directly from the Thermal Infrared bands. The present study focuses on ArcGIS Raster functions and Raster calculation using the LANDSAT in September, thermal Bands (10, 11 & 6). The output of this paper shows that the surface temperature was high in the barren and built up area whereas it is comparatively low in the thick vegetation and agriculture land. It is also recommended that in order to reduce the land surface temperature of urban areas, sustainable urban planning strategies that include increasing the vegetated areas and embracing other green initiatives such as urban forestry should be adopted. [ABSTRACT FROM AUTHOR]

Urban areas have witnessed an inexorable expansion in population density, urban sprawl, industrialization and agricultural activities in the past few decades. Withdrawal of groundwater to meet water demand has resulted in over-drafting of aquifers and, commonly, lowered water tables in many urban centres of India, like Delhi, Lucknow, Patna, Kolkata and others. Overdraft has also deteriorated the chemical and biogeochemical quality of surface water and groundwater. This paper highlights some of the research approaches, namely hydrogeochemical investigation, groundwater modeling, and use of geographic information systems and remote sensing, used worldwide to resolve issues specific to urban groundwater. The paper also highlights approaches that are practiced internationally (combined hydrogeologic and multi-isotopic studies, integrated modeling techniques, etc.) but still need to be developed on a large scale in India. A detailed overview of the literature indicates the need for a more holistic approach incorporating various types of conceptual and management models to strengthen the research level in India and elsewhere. For instance, a computational tool that can incorporate different models, such as for urban water and contaminant balance and pipeline leakage, should be developed to generate predefined scenarios of potential groundwater contamination and help in the socio-economic analysis of different scenarios. In hydrogeochemical approaches, multi-isotope ratios should be coupled with hydrogeologic approaches for better understanding of water quality problems. [ABSTRACT FROM AUTHOR]

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

India, being an abundant source of minerals, Mineral exploration, on a large scale, is promising to provide good impact for the future of the country. India has a rich source of coal, bauxite, limestone etc. With the advent of remote sensing technologies capturing broader area, exploration of minerals has now become an appreciable problem. Satellite Cluster Images spanning over wider areas can effectively serve as a tool for mapping minerals, qualitatively and quantitatively. In this paper, an algorithm, based on decision tree classification, is developed to map minerals, specifically, coal and limestone for a specific region. Multi-temporal cluster images are employed to map dynamic change detection resulting in greater accuracy. In this paper, multi-temporal cluster images (Landsat 8 OLI/TIRS) are analyzed to map coal, limestone and 'no-mineral' regions with the help of the algorithm developed using decision tree classification. Classification results are compared with ground truth data for assessing its accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we report geomorphic signatures of neotectonic activity identified from the area around Nahan Salient along Himalayan Frontal Thrust (HFT), using remote sensing and field investigations. We document two newly discovered exposures of HFT fault zone and active fault traces along the mountain front at Khetpurali, Nahan Salient, NW Himalaya, India. We demonstrate that HFT fault zone at Khetpurali is not blind, instead it is exposed at the surface along with highly deformed, folded strata in the hanging wall and its geometry is quite variable with the possibility of along strike-segmentation. The geometry of fold and various deformational features preserved in the hanging wall indicate a process of fault-propagation-fold. This is in contrast to the previous observation of fault-bent-folding within the Nahan Salient. Two-three levels of river terraces are preserved and truncated in the hanging wall, along with the active fault scarps which are linked to uplift along HFT fault zone. The Khetpurali Anticline (KA), is a fault-propagating-fold formed due to displacement and shortening along HFT which dips 30° towards north and strike WNW-ESE. The uplift (U) along the KA is calculated to be ~73 m and accommodated 7.3% shortening of the original length, before the fault tip truncated the fold. Various deformational structures have been identified within the hanging wall around-tip damage zone of ~80 m perpendicular to HFT fault zone and in the core of KA. Northwest of Khetpurali, a south-facing hogback structure ~100 m high is formed due to erosion of forelimb of the anticline. At Khetpurali, tectonic control due to folding of strata and uplift of river terraces is evident as the Dudhgarh River forms V-shaped profile near the thrust, compared to the wide floodplain before and after crossing the HFT fault zone. Along strike segmentation of HFT and associated anticlines is observed as the surface trace of HFT is offset at Khetpurali, Trilokpur, and Sabilpur due to the strike-slip faults. The study reveals that HFT has been truncated by tear faults and has different subsurface geometries in study area which may have implication on seismic hazard in the Nahan Salient where three thrusts; Himalayan Frrontal Thrust (HFT), Nahan Thrust (NT) and Main Boundary Fault (MBF) are located at relatively shorter distance of ~6 km as compared to the surrounding recesses. [ABSTRACT FROM AUTHOR]

Land Surface Temperature refers back to the calculation of ground temperature that is made with extraordinary factors. Due to changes in the land surface temperature of the area, the climate and vegetation are also affected. The Normalized Difference Vegetation Index (NDVI) serves as an indicator of vegetation abundance to estimate the land surface temperature (LST) vegetation relationship. Landsat 7 ETM+ and Landsat 8 OLI TIRS images for four years (2005, 2010, 2015, and 2020) are used to study the LST changes in the research area (Jammu & Kashmir). Correlations between NDVI and LST are calculated. Through the study, it is clear that the Normalized Difference Vegetation Index plays a major role in the calculation of land surface temperature. In this study, the change in the higher temperature noted in the year 2005 was 20oC, and in the year 2020, the highest temperature was 25oC. If we talk about the lower temperature, the change is 1oC. Both Jammu and Kashmir have semideciduous vegetation that includes subtropical pine forests. [ABSTRACT FROM AUTHOR]

The higher rate of sedimentation in reservoirs is one of the main concerns in sustainable reservoir management, which impairs the functional capacity of reservoirs that may lead to various environmental risks. Estimation of sedimentation rate and useful life of a reservoir using appropriate methods is imperative for sustainable management. This paper deals with the computation of sedimentation rate and useful life of Tehri reservoir of Bhagirathi river, in India, using hydrographic survey analysis, Hydrologic Engineering Center's River Analysis System (HEC-RAS) modelling, satellite remote sensing (SRS) technique, and trap efficiency based empirical curve methods. The rate of sedimentation was found as 5.33 million cubic metres (MCM)/year based on hydrographic survey analysis from the data obtained for the years 2005, 2008 and 2013. Likewise, the mean annual rate of sedimentation was estimated to be 5.07 MCM/year and 5.75 MCM/year based on the HEC-RAS model and SRS techniques respectively. Brune's method and Churchill's method of trap efficiency were found to be inconsistent with the hydrographic survey results. The reservoir can be classified as Type III reservoir with respect to the sediment vertical distribution analysis. Changes in bathymetry obtained in the simulation studies showed that the Bhagirathi river's 28-30 km reach would be most vulnerable to sedimentation problems. The estimated useful life of Tehri reservoir was found to be in the range of 160-180 years. The SRS technique and hydrodynamic model provided a better fit with the observed data. [ABSTRACT FROM AUTHOR]

This study focuses on exploring changes in Land Use and Land Cover (LULC) over Baramulla district of Kashmir using remote sensing and Geographic Information System (GIS). Linkage of LULC with evapotranspiration and precipitation is also explored in this paper. LULC were categorised into dense forest, sparse forest, water body and other classes by using geospatial satellite data from Landsat for the years 1979, 2001 to 2018. The dense forest and water body show decrease in extent from 1979 to 2018. A decrease of about 27 % has been reported from 1979 to 2018 in dense forest. Furthermore, water body shows a reduction of about 36% during the same time span. Significant changes in dense forest and water body affected the water cycle of the region. Reduction in dense forest and water body resulted in significant decrease in evapotranspiration which caused reduction in precipitation over the study area. Reduced precipitation over study area caused conversion from agriculture to horticulture. Increase of about 55% in horticulture has been reported with decrease of about 54% in agriculture over study area. Evapotranspiration from reanalysis shows coherent variation with precipitation from India Meteorological Department (IMD) gridded product. A decadal decrease of about 0.5% in evapotranspiration resulting in decrease of about 0.38% in precipitation over study area is reported. Increased horticulture causes increase in black carbon due to increased biomass which results in increased warming over study area. Changes reported in this study may cause a significant socioeconomic and environmental impact over Baramulla. [ABSTRACT FROM AUTHOR]

Rain gauges are considered the most accurate method to estimate rainfall and are used as the "ground truth" for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the "averaging error factor" has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty. [ABSTRACT FROM AUTHOR]

The aim of this study is to utilize the remotely sensed data and GIS techniques for instability assessment of terrain and prediction in the chosen study area, Rohtang to Baralacha, Lahaul Spiti District, Himanchal Pradesh, India. Several parameter maps were generated by the availability of different types of multi-temporal and various scales of remote sensing data. Inventory map was developed though the field work, past records and with the help of remote sensing data. Geological map, soil map, land use map, terrain map, slope map, aspect map, drainage density and snow melt parameter maps were generated or extracted from the existing ancillary data. In speeding up processing for hazard zonation and prediction assessment, GIS can be useful. To determine the degree of susceptibility to the landslides, information value method is used. The landslide hazard map is classified on the basis of its probability values into low, medium, high and very high risk zones. The results showed that over 3.09% of the area is liable to low landslide risk, 26.28% of the area is liable to medium landslide risk, 32.81% of the area is of high landslide risk and within this area about 37.81% has very high to severe risk. The landslide susceptibility map delineated in this paper can be used by various stakeholders, like state governments, researchers and citizens to develop and manage the developmental activities in the area in particular and in other similar areas in general. [ABSTRACT FROM AUTHOR]

India is one of the few developing countries with a growing technological capability in the space industry. This capability manifests itself in designing, manufacturing and launching satellites of various sizes and sophistication. The state has played a significant role in evolving and nurturing this technological capability, as the space sector has received considerable public investment. The space economy has grown and can be essential to the country's high-tech manufacturing and services sectors. A necessary feature of this technological capability building is entirely built through indigenous research and development by the Indian Space Research Organisation. Employing an eclectic framework, the paper maps out the size and structure of India's space sector and analyses the state's role in shaping it. It also provides us with some estimates of the productivity of those public investments in creating a space economy. [ABSTRACT FROM AUTHOR]

ABSTRACT Muthusankar, G.; Proisy, C.; Balasubramanian, D.; Bautès, N.; Bhalla, R S.; Mathevet, R.; Ricout, A.; Senthil Babu, D., and Vasudevan, S., 2018. When socio-economic plans exacerbate vulnerability to physical coastal processes on the south east coast of India. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1446–1450. Coconut Creek (Florida), ISSN 0749-0208. Industrial and urban development along the coast may exacerbate the changes induced by oceano-climatic processes to such an extent that the coast becomes uninhabitable. This paper presents a baseline study carried out on the 360 km-long Coromandel coast of Tamil Nadu's (TN) Bay of Bengal coast, Southeast India. About 30% of the 72 million inhabitants of the state of TN live in low-lying coastal areas within a variety of ecosystems such as sandy beaches and dunes, wetlands, mudflats and mangroves. Exponential demographic growth combines with dense urban agglomerations, ever-growing industrial and harbour areas, aquaculture and luxury tourism expansion increasing vulnerabilities to coastal erosion and extreme meteorological events. We report here how very high spatial resolution satellite (VHSRS) images, freely provided by Google Earth® engine or from the public database offered by GIS software, may be used to impartially and significantly survey coastal land cover (LC) transformations at fine scales over a number of years using preliminary visual interpretation. The study also gathered demographic data, historical records on strength and trajectories of the cyclones that have hit the TN coast since 1900, and decadal series of daily ERA-Interim/ECMWF waves and Mercator-Ocean oceanic currents. Our results demonstrate a tangle of environmental and human activities and situations subject to specific coastal hazards, particularly severe during the Northeast monsoon season. The potential risks for coastal ecosystems and local communities are finally discussed. [ABSTRACT FROM AUTHOR]

The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated to oceanography. The mission objectives are primarily the observation of the oceanic mesoscales but also include coastal oceanography, global and regional sea level monitoring, data assimilation, and operational oceanography. The mission ended its nominal phase after 3 years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to provide a state of the art of the achievements of the SARAL/AltiKa mission in terms of quality assessment and unique characteristics of AltiKa data. It shows that the AltiKa data have similar accuracy at the centimeter level in term of absolute water level whatever the method (from local to global) and the type of water surfaces (ocean and lakes). It shows also that beyond the fact that AltiKa data quality meets the expectations and initial mission requirements, the unique characteristics of the altimeter and the Ka-band offer unique contributions in fields that were previously not fully foreseen. [ABSTRACT FROM AUTHOR]

Soil erosion is a most severe environmental problem in humid sub-tropical hilly state Tripura. The present study is carried out on Muhuri river basin of Tripura state, North east India having an area of 614.54 Sq.km. In this paper, Universal Soil Loss Equation (USLE) model, with Geographic Information System (GIS) and Remote Sensing (RS) have been used to quantify the soil loss in the Muhuri river basin. Five essential parameters such as Runoff-rainfall erosivity factor (R), soil erodibility Factor (K), slope length and steepness (LS), cropping management factor (C), and support practice factor (P) have been used to estimate soil loss amount in the study area. All of these layers have been prepared in GIS and RS platform (Mainly Arc GIS 10.1) using various data sources and data preparation methods. In these study DEM and LISS satellite data have been used. The daily rainfall data (2001-2010) of 6 rain gauge stations have been used to predict the R factor. Soil erodibility (K) factor in Basin area ranged from 0.15 to 0.36. The spatial distribution map of soil loss of Muhuri river basin has been generated and classified into six categories according to intensity level of soil loss. The average annual predicted soil loss ranges between 0 to and 650 t/ha/y. Low soil loss areas (<25 t/ha/y) have been recorded under very densely forested areas and intensely plantation (mainly Rubber plantation) area. The high rate (>70 t/ha/y) of soil erosion was found along the main course of Muhuri River. [ABSTRACT FROM AUTHOR]

Agriculture is a major occupation for people who inhabit the state of West Bengal in India. In order to boost irrigation, 570 tube wells per year were installed during 2002-2008, and 12,000 wells per year were installed during 2009-2013, contributing to higher groundwater (GW) withdrawal. However, the impact of tube wells on GW storage levels has not been well-studied, both spatially and temporally. Hence, this study used remote sensing data from NASA's Gravity Recovery and Climate Experiment and the Global Land Data Assimilation Systems to assess change in GW storage. Results showed that GW is being depleted at 8, 5.3, and 14.7 cm (Billion Cubic Meters)/year during the study period. After tube well intensification, the state-wide average net GW recharge was 15.33 BCM/year, while the net GW discharge was at 19 BCM/year. The spatiotemporal GW storage data presented in this paper will benefit managers and policymakers in identifying suitable mitigation plans for future management of GW resources. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson's chi statistics and Shannon's entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts-Central and New Delhi. Relative entropy, which scales Shannon's entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density. [ABSTRACT FROM AUTHOR]

The present paper maps the district-wise potential for concentrating solar power (CSP) and centralized solar photovoltaic (SPV) technology based power plants in India. The evaluation is based on remotely-sensed annual average global horizontal irradiance (GHI) and direct normal irradiance (DNI) provided by National Aeronautics and Space Administration (NASA) surface meteorology and solar energy program. The solar irradiation data (GHI and DNI), land-use data and Digital Elevation Model (DEM) were used in GIS environment while employing land-use criteria and topography to exclude unsuitable sites for harnessing solar energy. Furthermore, land-cover factor, number of sunshine hours and conversion efficiencies were taken into account to calculate technical potential in suitable land areas for solar power development. The result shows potential sites for installation of CSP and centralized large scale SPV plants in India. [ABSTRACT FROM AUTHOR]

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India's total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers' reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS). [ABSTRACT FROM AUTHOR]

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m 2 to tens of km 2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it's data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR). [ABSTRACT FROM AUTHOR]

Diffuse attenuation coefficient (K d (λ)), a measure of water quality and an indicator to predict the euphotic zone, is one of the key optical variables in ocean studies that can be determined remotely from satellite sensors. Here, we examined the seasonal plume pattern and K d (490) trends (2002–2021) in the coastal waters of Krishna-Godavari basin, India, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Additionally, we discussed the dynamics of sediment plumes by analyzing multiple hydro-geophysical variables such as sea surface temperature (SST), wind speed, sea surface height anomaly (SSHA), river discharge, and precipitation dataset. Our result presents the first K d (490) seasonal climatology and long-term trends of sediment plumes over the study region. We noticed that the plume is at its maximum during the summer, when there is an abundance of precipitation and river discharge, and the K d (490) value can reach over 1.72 m-1. Contrarily, the sediment plume is nearly nonexistent during the rest of the season. During summer, the decrease of SST coincides with the increase of K d (490) and vice versa. Furthermore, we observed that the surface waters had a homogenous SST during the winter, whereas stratified SSTs dominate at the surface during the summer. Although the temporal variability of K d (490) is strongly dominated by the seasonal component, a long-term decreasing trend of about −0.00011 m-1y-1 in K d (490) concentration is noted, and is associated with the decrease of the Godavari river discharge and sediment load during summer. • Sediment plumes were reported in the summer as compared to other seasons. • River discharges and rainfall play a dominant factor in sediment plume variations. • Decreased trend was observed in 2002–2021 for turbidity concentrations. • Resuspension of sediments due to high winds also increases the turbidity. [ABSTRACT FROM AUTHOR]