Study of contaminated snow cover using remote sensing in the Eastern Himalayas of Arunachal Pradesh, India.

Snow is considered contaminated when any foreign materials are deposited/mixed with it, which can accelerate melting and significantly impact the snow cover's radiative balance. Such an enhanced melting rate results in a reduction in freshwater sources at the catchment level. In optical remote sensing, snow contamination is widely studied using a normalizing difference index called the snow contamination index.

Particulate matter estimation using satellite datasets: a machine learning approach.

In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. INSAT-3D and Moderate Resolution Imaging Spectroradiometer (MODIS) for the period of 7 years (2014 to 2020).

Sunflower mapping using machine learning algorithm in Google Earth Engine platform.

The sunflower crop is one of the most pro sources of vegetable oil globally. It is cultivated all around the world including Haryana, in India. However, its mapping is limited due to the requirement of huge computation power, large data storage capacity, small farm holdings, and information gap on appropriate algorithms and spectral band combinations.

Vegetation disturbance and regrowth dynamics in shifting cultivation landscapes.

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool.

CO variability over a tropical coastal station in India: Synergy of observation and model.

Carbon dioxide (CO), being the prime greenhouse gas, has largest contribution in radiative forcing and global warming due to increased anthropogenic emissions leading to regional and global climate change. We performed CO measurements using a continuous monitoring instrument at a tropical coastal station at the southern tip of India (Thumba; 8.54° N, 76.

Assessment and estimation of coal dust impact on vegetation using VIs difference model and PRISMA hyperspectral data in mining sites.

This work focuses on dust detection, and estimation of vegetation in coal mining sites using the vegetation indices (VIs) differences model and PRISMA hyperspectral imagery. The results were validated by ground survey spectral and foliar dust data. The findings indicate that the highest Separability (S), Coefficient of discrimination (R), and lowest Probability (P) values were found for the narrow-banded Narrow-banded Normalized Difference Vegetation Index (NDVI), Transformed Soil Adjusted Vegetation Index (TSAVI), and Tasselled Cap Transformation Greenness (TC-greenness) indices.

Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India.

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas.

ToTE: A global database on trees of the treeline ecotone.

Globally, treelines form a transition zone between tree-dominated forest downslope and treeless alpine vegetation upslope. Treelines represent the highest boundary of "tree" life form in high-elevation mountains and at high latitudes. Recently, treelines have been shifting upslope in response to climate warming, so it has become important to understand global tree diversity and treeline distributions.

Urban ozone variability using automated machine learning: inference from different feature importance schemes.

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world.

Modern pollen dispersal in relation to present vegetation distribution and land use in the Baspa valley, Kinnaur, western Himalayas.

Interpretation of a fossil pollen data for the vegetation and climate reconstruction of any region needs a modern pollen-vegetation analogue for its calibration. We analyzed the surface sediments and moss polsters for the pollen and microcharcoal records to understand the modern pollen-vegetation relationship and human activities in the Baspa Valley, Kinnaur, Himachal Pradesh. Presently, valley is occupied by the arboreal and non-arboreal vegetation of temperate to subalpine habitats and land use activities.

Flood susceptibility mapping using Sentinel 1 and frequency ratio technique in Jinjiram River watershed, India.

Assam is one of the most flood-prone states in India, and the state frequently experiences catastrophic floods that cause significant damage in terms of loss of life and property. Flood susceptibility is considered the most essential and crucial input for managing floodplains and fostering local and regional development. This study focuses on the generation of flood susceptibility maps using the Frequency Ratio (FR) technique and microwave remote sensing inputs in the Jinjiram watershed which experienced disastrous flooding in 2020.

Multiple ocean parameter-based potential fishing zone (PFZ) location generation and validation in the Western Bay of Bengal.

A new conceptual framework based on satellite data, including chlorophyll (CHL), sea surface temperature (SST) fronts, relative winds, current vectors, Ekman transport, and eddies, has been developed to identify potential fishing zones (PFZ) in the Bay of Bengal (BoB). The framework aims to provide persistent forecasts, even under cloudy conditions, based on feature propagation. The validation of the PFZ was carried out using fish catch data collected by the Fishery Survey of India (FSI) between 2016 and 2018.

Climate change-derived environmental and physical factors influencing the socioeconomic development in the Himalayan region.

Spatio-temporal fluctuation of climatic variables with the terrain characteristics and their inter-relationship is a priority for predicting flash-flood-induced landslide hazards over the fragile Himalayas. The present study addressed this anxiety by assimilating satellite data products and auxiliary datasets in the Bhagirathi River basin of the Indian Himalayas. Snow Covered Area (SCA) is a critical indicator of the ecosystem that influenced the flash flood along different terrain features such as Altitude, Hill-Gradient, and Aspect.

Factors influencing nearshore hypoxia in the southeastern Arabian Sea: A sensor-based study.

Seasonal upwelling and the associated incursion of hypoxic waters into the coastal zone is a widely studied topic over different upwelling zones. However, its persistence or variations over short time scales are poorly addressed. The present study, therefore, brings out a first report on hourly variations in the temperature, salinity and dissolved oxygen recorded by an environmental data buoy equipped with sensors, deployed in the nearshore waters of Alappuzha (southeastern Arabian Sea) from April to August 2022.

Influence assessment of urban expansion on groundwater level fluctuations in Gandhinagar, Gujarat, India.

Being the state capital of Gujarat, Gandhinagar is snowballing urban population, resulting in overexploitation of groundwater resources and consequent decline in local groundwater level. The key objective of the current research is to understand the impact of urban expansion on the groundwater level of Gandhinagar district for the last 3 decades. Long-term land use/land cover (LULC) alterations using Landsat images (1991-2021) reveal a 234% increase in overall built-up area and it is more prominent in western and southern parts than the eastern part of study area till 2021 due to urban sprawl of adjacent Ahmedabad City.

Evaluation of multifrequency SAR data for estimating tropical above-ground biomass by employing radiative transfer modeling.

The retrieval of the biophysical parameters and subsequent estimation of the above-ground biomass (AGB) of vegetation stands are made possible by the simulation of the extinction and scattering components from the canopy layer using vector radiative transfer (VRT) theory-based scattering models. With the use of such a model, this study aims to evaluate and compare the potential of dual-pol, multi-frequency SAR data for estimating above-ground biomass. The data selected for this work are L-band dual polarized (HH/HV) ALOS-2 data, S-band dual polarized (HH/HV) NovaSAR data, and C-band dual polarized (VV/VH) Sentinel-1 data.

Characterizing the regional XCO variability and its association with ENSO over India inferred from GOSAT and OCO-2 satellite observations.

India is primarily concerned with comprehending regional carbon source-sink response in the context of changes in atmospheric CO concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite's fine-scale XCO measurements provide an opportunity to understand unprecedented details of source-sink activity on a regional scale. In this study, we investigated the long-term variations of XCO concentration and growth rates as well as its covarying relationship with ENSO and regional climate parameters (temperature, precipitation, soil moisture, and NDVI) over India from 2010 to 2021 using GOSAT and OCO-2 retrievals.

Manifestation of spatially varying demarcations in Indian rainfall trends through change-point analysis (1901-2020).

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods.

The role of continental evapotranspiration on water vapour isotopic variability in the troposphere.

Rainforests play an important role in hydrological and carbon cycles, both at regional and global scales. They pump large quantities of moisture from the soil to the atmosphere and are major rainfall hotspots of the world. Satellite-observed stable water isotope ratios have played an essential role in determining sources of moisture in the atmosphere.

Exploring biophysical links of catch rate from mid-water trawl fishery in the north-west coast of India, Eastern Arabian Sea: A remote sensing approach.

Fish frequently shift their distribution ranges as a result of changes in preferred environmental factors. Knowledge on distribution of fish in relation to their environmental optima is crucial for improving the understanding of fishing grounds and planning sustainable exploitation. This study investigated the monthly variability in environmental factors impacting the catch rate and the spatio-temporal distribution patterns of fish along northwest coast of India (NWCI) from 2017 to 2019.

Fingerprinting of rainfall over semi-arid region, Western India, using MATLAB and GIS.

The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981-2020 using computational and spatial analysis tools. Daily gridded rainfall data from India Meteorological Department (IMD) at 0.25 × 0.

Nested DWT-Based CNN Architecture for Monocular Depth Estimation.

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth.