Journal of Geography, Environment and Earth Science International

Quantifying the Influence of Agrometeorological Parameters on Growth and Yield of Brinjal: A Comprehensive Review

Prerna Sihag — 2026-02-07

Brinjal (Solanum melongena L.) is an important warm-season vegetable crop whose growth, phenology, yield, and fruit quality are strongly influenced by prevailing weather conditions. This review synthesizes existing research on the effects of major weather parameters temperature, rainfall and soil moisture availability, relative humidity, solar radiation, sunshine duration, wind speed, and extreme weather events on the growth and yield of brinjal. Temperature emerges as a dominant factor regulating physiological processes, phenological development, and reproductive success, with both heat and cold stress during sensitive stages such as flowering and fruiting leading to significant yield reductions. Rainfall distribution and soil moisture availability critically affect nutrient uptake, biomass accumulation, and disease incidence, while extremes of relative humidity modify transpiration, pollen viability, and pest–disease dynamics. Solar radiation and sunshine duration govern photosynthetic efficiency, radiation use efficiency, and assimilate partitioning, thereby directly influencing fruit set, size, and yield. Wind speed and evaporative demand further alter crop microclimate, water use, and reproductive stability. The review also highlights the role of sowing time in aligning crop phenology with favourable agrometeorological windows, emphasizing the usefulness of thermal indices and weather-based yield prediction models in understanding yield variability. Additionally, the impacts of weather parameters on fruit quality traits and their interactions with crop phenology are discussed. Finally, adaptation and management strategies and key research gaps are identified, underscoring the need for integrated, climate-resilient approaches and predictive modelling to sustain brinjal productivity under increasing climate variability.

Renewable Energy in Shaping the Upcoming Energy Demands in India: A Review on Status, Policy Analysis and Future Pathways

Sidhartha Mishra — 2026-02-07

Energy is the key catalyst in the overall economic growth of any nation. Energy in many forms drives the nation. In the current scenario, India's energy utilization ranks fourth globally in total energy consumption, with the majority of it derived from non-renewable sources. The need of the hour stands in the exploitation of renewable sources of energy to mitigate the arising environmental issues, as well as the fear of being driven out of the conventional energy sources. As the population grows, so does the energy demand, raising concerns about sustainable resource utilization and reducing carbon footprints. As of 2025, India’s renewable capacity has reached a significant milestone of 220.10 GW, driven largely by a 106 GW solar contribution. The findings also suggest that while India is on a trajectory to meet its 500 GW target by 2030, success depends on modernized grid infrastructure and enhanced public-private partnerships. These problems are not just limited to India, but the whole world is working towards a common goal to achieve sustainability in the energy sector. This review paper explores the current status and scope of renewable energy in India, including solar, wind, hydro, biomass, and geothermal offering strategic framework for policymakers and researchers to navigate the challenges of scaling clean energy in an emerging economy.

Remote Sensing and GIS Based Terrain Analysis: A Case Study of Bhera River Watershed, Ranchi- Ramgarh Districts, Jharkhand, India

Mable Martha Toppo — 2026-01-30

Remote sensing (RS) and geographic information system (GIS) based terrain analysis is essential for characterizing watershed dynamics and aids in sustainable land and water resource management, particularly in hard rock terrain where primary porosity is minimal, and topography and lithology pose challenges for groundwater and erosion control. Despite advancement in geospatial techniques, integrated terrain- hydrological evaluations remain limited for watersheds in hard rock terrain, particularly in Jharkhand because of its diverse physiography and effects of multiple tectonism. This study aims to highlights the potential of geomatic techniques in assessing seven key terrain parameters, lithology, geomorphology, lineaments, soil, slope, drainage, and hydrological indices (NDWI, NDMI, EWI) for sustainable management of Bhera River watershed, Ranchi- Ramgarh districts, Jharkhand. The Bhera watershed, having an area of 269 sq. km, lies within the Chotanagpur Plateau and is characterised by dissected plateaus, undulating hills, and diverse lithology ranging from Precambrian granites and gneisses to Gondwana sediments. The thematic maps were generated using Visual and Digital Image interpretation, using the sources from existing thematic maps, satellite imageries- Landsat 8 and 9 (Date of acquisition- 20^thand 22nd November 2023), SRTM (Date of acquisition- 1^st November 2023), and survey of India toposheets at 1:50,000 scale. The study reveals geologically complex landscape with dominant pediplains (70% coverage) indicating moderate groundwater potential, moderate structural control, luvisols as the dominant soil type with good nutrient retention, dendritic to sub-dendritic drainage pattern indicating uniform lithology and varied slopes heightening runoff and erosion risks. Hydrological indices highlight sparse water bodies (NDWI: -0.43 to 0.11), moderate vegetation moisture (NDMI: -0.09 to 0.26), and predominantly dry to sparsely vegetated soils (EWI: -0.60 to -0.35). These findings underscore the watershed's vulnerability to erosion and groundwater overexploitation despite adequate rainfall (1100-1400 mm annually), emphasizing the need for integrated RS-GIS approaches for terrain evaluation to guide conservation strategies and sustainable management in similar hard rock regions.

A Comparative Analysis of Remote Sensing Derived Spectral Indices of Valanchery Micro Watershed, Kerala, India

ARAVIND P — 2026-02-02

Spectral indices derived from multispectral satellite data provide a useful means to assess the temporal dynamics of land surface features. The study on spectral indices is important to detect the Land use change and the trend of urbanization and vegetation pattern of the watershed. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), three important remote sensing-based spectral indices, were compared for the Valanchery micro-watershed of Kerala for the study period (2015-2023). This study area was specifically carried out in this study area, since the study area is majorly covered with vegetation and plantation land use, and the study area is undergoing rapid urbanization. Multi-temporal spectral analysis (2015–2023) revealed an increase in the vegetation, with NDVI range of 0.45 to 0.52. NDWI ranged between (-0.47 to 0.13), and there was an increase in NDWI of 0.13 in the year 2020, indicating temporal moisture variability. NDBI values ranging between (−0.27 to 0.22), maximum NDBI values of 0.22 were observed during the year 2023 and showed an increase in built-up, with urban land. The findings of the study show that over the research period, there was a visible increase in built-up areas combined with a steady increase in vegetation greenness, especially within plantation and thick vegetation classifications. Holistic Integrated analysis of the spectral indices, such as NDVI, NDWI and NDBI revealed an improvement of vegetation with an increase in urban expansion and a decline in surface moisture conditions. The study is an important for multi-temporal spectral indices analysis and cloud-based processing usage to monitor landscape changes in micro-watersheds that are undergoing rapid urbanization.

Periodic Market Typology and Spatial Dynamics in Menoua Division, Cameroon

Maryline Nganga Yisa — 2026-02-03

Global literature has consistently emphasised the complex challenges and opportunities associated with periodic market systems and spatial organisations. Despite this board attention, empirical evidence at the regional level is limited, particularly in African contexts where periodic markets play a central role in shaping local economies. This study uses the case of Menoua Division to: (i) to identify the typology of periodic markets (ii) to determine the spatial variations in periodic market and (iii) to analyse the determinants of spatial variations in periodic markets. This study employed a random sample of 350 households involved in periodic marketing to reflect variations in market participation, spatial accessibility and socio-economic characteristics from six sub-divisions –Dschang, Santchou, Fokoue, Fongo-Tongo, Penka-Michel and Nkong-ni. This was complemented by focus group discussions and interviews. Descriptive and inferential analyses led to the following results: First, periodic markets exhibit distinct typologies (semi-daily and weekly markets) shaped by frequency, scale and functional roles. Second, significant spatial variations are evident across the division, reflecting differences in population characteristics, transport development, and agricultural outputs. The spatial distribution of periodic markets and transport development (r=.620, p< .001), spatial market location and agricultural output (r= -.186, p=.004). The typological diversity of periodic markets, expressed in their varying frequency, scale of operation and functional roles complicates efforts to establish coherent classification. Also, the spatial disparities in distribution and accessibility create disparities in participation, connectivity and uneven opportunities for communities. This undermines the capacity of periodic markets to serve as inclusive and efficient platforms for trade and development. Sustainable improvement in market and transport infrastructures could reduce spatial disparities, robustly strengthening market governance and security which could mitigate the challenges posed by typological diversity such as inconsistencies in classification and variations in functional roles while also addressing spatial disparities through improved accessibility and integration.

A Reclassification-Based Framework for Vegetation Greenness Assessment Across Mixed Landscapes and Spatial Extents

Adedoyin S. Ajeyomi — 2026-02-04

Vegetation dynamics are fundamental indicators of environmental health, yet evaluating them in complex, mixed-use landscapes remains methodologically challenging. Traditional statistics are not the real representative of the pixel-level distribution of greenness in a particular area of study, which results in critical disparities in the spatial extent and density of vegetation. To overcome these limitations, this study introduces the Greenness Score (GS), a spatially explicit, area-weighted framework that reclassifies NDVI into ecologically distinct categories to quantify peak greenness intensity and coverage.

To test this framework, this study utilizes the NDVI of ten Nigerian cities across diverse bioclimatic zones for the years 2015 and 2025. The analysis shows that traditional summary statistics frequently fail to represent ecological reality. In 2015, Asaba and Ibadan exhibited comparable maximum NDVI values (0.67 and 0.83) and mean NDVI values (0.41 and 0.57), suggesting similar greenness potential. However, the GS analysis exposed a stark contrast in actual biomass: Ibadan supported 175,411 hectares of dense vegetation (56.44% of its area), while Asaba contained only 17.46 hectares (0.06%).

The GS framework also shows high performance in detecting temporal shifts invisible to central-tendency measures. Between 2015 and 2025, Asaba’s Average NDVI remained deceptively stable (0.41 to 0.42), masking a significant ecological recovery where dense vegetation expanded from 0.06% to 21.76% of the city. Additionally, Abuja’s Average NDVI declined (0.39 to 0.25), and Abuja lost approximately 112,000 hectares of moderate vegetation.

This study reveals that GS provides a more accurate and ecologically meaningful assessment of urban vegetation than traditional summary statistics, highlighting its value for monitoring and managing urban greenness.

Assessing Erosion Vulnerability and Soil Loss Dynamics in the Eastern Himalayas: A USLE-based Approach in the Kiile Watershed, Arunachal Pradesh, India

Ligang Aniya — 2026-02-04

Quantifying erosion vulnerability at the watershed scale is imperative for understanding sediment dynamics and formulating effective soil conservation strategies. In this study, the erosion vulnerability of the Kiile watershed was evaluated using the Universal Soil Loss Equation (USLE) model to quantify soil loss dynamics across ten distinct sub-watersheds. The model integrated multi-source geospatial datasets, including high-resolution Digital Elevation Model (DEM), satellite-derived land use/land cover (LULC) maps, and long- term rainfall records. These datasets were used to compute the standard USLE parameters: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P). The analysis revealed that the average annual soil loss in the Kiile watershed ranges between 0 and 138 t ha^-1 yr^-1, with a mean annual loss of 2.4 t ha^-1 yr^-1. Based on erosion severity, the study area was categorised into five classes: very high, high, moderate, low, and very low. The SW-8 recorded the highest soil loss of 4.52 t ha^-1 yr^-1, highlighting an urgent need for site-specific conservation strategies. The findings reveal that intense rainfall, erodible sandy clay loam textures and increasing urban development are the principal determinants of soil loss in this sub-watershed. This study highlights the necessity of integrating climatic and anthropogenic drivers into watershed planning to mitigate soil loss in vulnerable sub-watersheds. The study provides a foundation for decision-makers to formulate effective resource management and conservation strategies, ensuring long-term sustainability of the watershed.

Status of Water Quality of Naikal Pond in Northeastern Karnataka, India

Basavaraja D — 2026-02-06

Ponds are shallow, water filled depressions inhabited by aquatic plants and animals. They are an important natural resource and provide habitat for native species. These ponds remain neglected with no proper attempt of nurturing the potential ecosystem services and are under evaluated. The study was conducted to determine the water quality of Naikal pond in northeastern Karnataka. Water sample was collected near deepest part of pond using grab sampling technique during march 2022 and 2023. The collected water sample was subjected to characterization of physico-chemical parameters. viz., temperature, pH, electrical conductivity, total dissolved solids, total alkalinity, dissolved oxygen, biological oxygen demand, total hardness, calcium, magnesium, chloride, sulphate, nitrate and phosphate. The results were compared with BIS drinking water quality standards. The most of the water quality parameters were within the permissible level except total alkalinity. From the data obtained, the water quality index was calculated to quantify overall pond water quality status of the area. It is found that based on the water quality index results, the samples are falling under good category. Pond water is suitable for propagation of wild life and fisheries as water pH is in between 6.5 to 8.5 and dissolved oxygen concentration more than 4.0 mg/L.

Rainfall Variability and Trends in the Lower Dhansiri River Catchment, Assam, India

Anindita Saikia — 2026-02-06

The Lower Dhansiri River catchment in Assam, India, is one of the highly sensitive hydroclimatic zones in northeastern India, where rainfall patterns critically influence river discharge, agriculture and socio-economic systems. This study investigates long-term rainfall variability and trends from 2001 to 2024 using daily rainfall data from meteorological stations in the catchment. The analysis incorporates annual, seasonal (monsoon and post-monsoon), event-based and storm-based rainfall characteristics, applying statistical parameters such as mean, standard deviation, coefficient of variation (CV), skewness, kurtosis and rainfall intensity. Results reveal substantial inter-annual variability with a mean annual rainfall of 1,222.4 mm and a CV of 120.9%, indicating high year-to-year fluctuations. The monsoon season (May–September) contributes approximately 84% of annual rainfall, with extreme events such as in 2010 (2884 mm) and 2009 (650.1 mm) highlighting increasing extremes. Post-monsoon rainfall, particularly in October, shows erratic behavior, with a notable extreme in 2010 (1862.7 mm), reflecting possible shifts in weather systems. Event-based analysis indicates that most rainfall occurs in short-duration events, with one-day events contributing 14.29 mm per event. Z-score classification of storm events reveals a dominance of extremely low (915 rainy days) and very low intensity events, while very high intensity events are rare but catastrophic. Skewness and kurtosis values consistently show positive skew and leptokurtic distribution, confirming the presence of extreme rainfall events. The study underscores the intensification of rainfall extremes and the increasing frequency of anomalous events, likely influenced by climate change, land-use change and regional atmospheric dynamics. These findings have critical implications for flood risk management, agricultural planning and water resource sustainability in the region.

Harnessing Neural Networks for Accurate Potential Evapotranspiration Forecasting: A Case Study in Semi-Arid Aurangabad District of Maharashtra State Using the Thornthwaite Method

Harish H Deshpande — 2026-02-09

Potential evapotranspiration (PET) is a crucial parameter for effective water resource management, particularly in semi-arid regions like Aurangabad district. PET was estimated for nine stations in this region using the Thornthwaite method, which is a widely-used approach for determining this important variable. Time series analysis revealed significant autocorrelation and stationarity, confirming the suitability of the data for modeling. A feed-forward neural network with 12 input, 4 hidden, and 1 output neuron was implemented for PET forecasting. The FFNN (Feed Forward Neural Network) demonstrated robust accuracy, with training mean absolute error of 7.86–11.52 and validation MAE of 11.21–16.83. RMSE ranged from 10.32 to 15.12 during training and 14.94 to 23.06 during validation. Percent bias remained below 2.2%, and Nash-Sutcliffe efficiency exceeded 0.97 in training and 0.92 in validation. Vaijapur performed best, achieving a validation RMSE of 14.94, MSE of 223.35, and R² of 0.95. Conversely, Sillod recorded the highest validation errors, highlighting regional variability. The FFNN effectively captured PET dynamics with minimal over-fitting, supporting its application in optimizing water use and agricultural planning. Localized calibration is recommended for stations with higher errors to enhance accuracy.

Spatial and Temporal Evolution of Urban Heat Islands in a Warm Sub Humid City (2019–2024): The Guadalajara Metropolitan Area, Mexico

Ortiz-Bañuelos Alma Delia — 2026-02-11

The rapid growth of the Guadalajara Metropolitan Area (GMA), Jalisco, Mexico, has caused the urban heat island effect to increase its average annual temperature year after year, with record maximum temperatures recently recorded during the spring, specially in 2023 and 2024. This context motivates the present study, which analyzes the spatial and temporal evolution of the heat island effect in order to identify patterns and trends that can inform decision-making to mitigate the long-term adverse effects in specific areas of the study region. The approach is quantitative, using descriptive statistics, vector calculus tools and the thermal gradient analysis. Maximum temperature data were obtained from the ERA5-Land reanalysis and were adjusted in Python based on the National Meteorological Service (SMN) meteorological station database. The ERA5-Land data were bias-adjusted using station data to strengthen methodological transparency. The results show an increase in the average maximum temperature over the last 6 years in most of the GMA, with a trend toward an increase in the average monthly minimum temperature of ≈0.1°C per year in the municipalities of Guadalajara and Tlaquepaque, and ≈0.5°C/year in Tonalá. The heat island effect was concentrated in the south and east of Guadalajara, areas with the highest population density and the lowest density of green space per inhabitant. All of the above highlights the need to strengthen the mitigation strategies that have been implemented in the study area to reduce the development of the urban heat island effect, especially in the south and east of Guadalajara.

Assessment of the Spatiotemporal Variations of Turbidity Flow in Lake Pontchartrain Using Remote Sensing (NDTI Method)

Tolulope Uriel OLOWU — 2026-02-11

Turbidity flow plays a critical role in sediment transport, water quality degradation, and ecosystem functioning in lacustrine and estuarine environments, particularly in urbanized coastal regions. Lake Pontchartrain, one of the largest estuaries along the Gulf Coast of the United States, has experienced increasing turbidity driven by sediment inflow, resuspension processes, hydrodynamic forcing, and anthropogenic pressures. This study assesses the spatiotemporal variations of turbidity flow in Lake Pontchartrain using multispectral remote sensing and the Normalized Difference Turbidity Index (NDTI). Sentinel-2 MSI imagery acquired monthly throughout 2023 was processed using Google Earth Engine and ArcGIS Pro to generate turbidity distribution maps. The Normalized Difference Water Index (NDWI) was first applied to delineate open water, followed by NDTI computation to classify turbidity into low, medium, and high categories using a quantile classification scheme. Results reveal pronounced seasonal and spatial variability in turbidity patterns, with higher turbidity concentrations predominantly observed along nearshore zones, river inflow areas, and during periods of increased runoff and sediment resuspension. Conversely, lower turbidity conditions prevailed during periods of reduced discharge and calmer hydrodynamic conditions. The findings demonstrate the effectiveness of NDTI-based remote sensing for monitoring turbidity dynamics and highlight critical zones of sediment influence within Lake Pontchartrain. This study provides valuable insights for environmental monitoring, water quality management, and sustainable conservation planning in estuarine lake systems.

An Assessment of Selected Metals in Puthurpadam Wetland Area, Malappuram District, Kerala, Southern India

Nivya. P — 2026-02-13

Wetlands are amongst the most productive ecosystems on the Earth that provides many important services to human society. They are biologically beneficial habitats that act as a transition zone between land and water. However, they are also ecologically sensitive and adaptive systems. The present study is conducted in a wetland area of Puthurpadam, Malappurram at three different locations, in order to monitor the status of metal contamination in the study area. The three sites consist of a stream, paddy field, and a pond which represent significant forms, particularly in agricultural and rural settings. The study was conducted from October 2024 to March 2025. The water and soil samples were collected from all the three sites during the morning hours and plant sample was collected from the paddy field (one sample per month) during the study period due to the absence of water at the time of cultivation. Water quality analysis was done for all the three sites, metal contamination detection was conducted by using ICP-MS (Inductively coupled plasma mass spectrometry) method for all the water, soil and plant samples taken. Site-specific elemental distribution, Water-soil comparative analysis and Integrated contamination assessment were also recorded. The presence of lead was higher than the desired values in all the three sites respectively (0.094, 0.061, 0.07) and nickel at site 3(0.032) water samples. The boron shows remarkably higher values in the soil samples of all the three sites respectively (22.738, 20.896, 20.257) while Magnesium (10.71), Calcium (0.5202) and Chromium (0.0914) has shown slightly higher values in plant samples than the limits at site 2 in mg/L. In the present study, the maximum metal pollution was observed during the advent of summer season that is in the month of March. A comprehensive strategy for regular long-term monitoring of water quality of such wetlands should further be evolved into effective conservation and management programmes, bringing community participation to the forefront.

Spatio-temporal Catchment-scale Land-use Changes in the Shahpura Lake, India (1980-2021)

Pramila Majumdar — 2026-02-17

Rapid urbanisation is a major driver of land use and land cover (LULC) transformation in urban lake catchments, often threatening the long-term sustainability of lake ecosystems. This study aimed to quantify and analyse long-term spatio-temporal LULC dynamics within the Shahpura Lake catchment, Bhopal, India, over 40 years (1980–2021). The lake catchment was delineated using a DEM-based hydrological approach derived from Shuttle Radar Topography Mission (SRTM) data. Multi-temporal Landsat imagery was analysed using supervised classification with a Maximum Likelihood algorithm to generate LULC maps for five reference years. Temporal changes were assessed using area statistics, linear trend analysis, and correlation analysis at the catchment scale. The results reveal a statistically significant expansion of built-up land, increasing by 220.7% (+0.062 km²/year; R² = 0.94; p < 0.01), accompanied by substantial declines in vegetation (−40.1%) and barren land (−39.6%). A strong inverse correlation between built-up and vegetation cover (r = −0.96; p < 0.05) confirms urban expansion as the dominant driver of land transformation. The surface area of Shahpura Lake remained relatively stable throughout the study period, showing no significant long-term trend. The study emphasises the importance of catchment-scale assessment for understanding urban lake dynamics. It also presents a remote sensing and GIS-based framework to support sustainable planning and management of urban lake ecosystems in rapidly urbanising regions.

Spatio-Temporal Soil Quality Dynamics of Ashtamudi: A Tropical Ramsar Wetland, India

T. Alexander — 2026-02-21

This study presents a spatio-temporal assessment of soil nutrient dynamics across five representative ecosites of Ashtamudi Lake, a tropical Ramsar-designated estuarine wetland on the southwest coast of India. Seasonal soil sampling was conducted during pre-monsoon, monsoon, and post-monsoon periods (2023–2024) at Neendakara, Puthenthuruthu, Asramam, Kidapram, and Cherikadavu. Key soil physicochemical parameters, including pH, electrical conductivity, organic carbon, and available nitrogen, phosphorus, and potassium, were analysed using standard methods. Statistical evaluation involved descriptive analysis, two-way ANOVA, Tukey HSD post-hoc tests, Pearson correlation, Principal Component Analysis (PCA), and development of a Soil Quality Index (SQI). Results revealed significant spatial and temporal variation in all soil parameters, with strong effects of season, site, and their interaction. Post-monsoon periods showed enhanced organic carbon and improved soil quality, while monsoon conditions were characterized by episodic nutrient enrichment, particularly phosphorus. PCA identified organic carbon, potassium, pH, and electrical conductivity as dominant drivers of soil quality variability, with the first two components explaining approximately 59% of the total variance. SQI values ranged from moderate to good (0.52–0.74), with consistently higher scores during the post-monsoon season. These findings provide a scientifically robust baseline for Ramsar site monitoring, nutrient management, and conservation planning, supporting sustainable ecosystem management of tropical estuarine wetlands.