Carbon dioxide (CO2) is a significant contributor to atmospheric climate change, making long-term monitoring crucial for developing effective regional/global mitigation policies. A multidisciplinary approach is adopted in this study, utilizing a combination of AIRS satellite CO2 data, vegetation (NDVI) data, and fire data to obtain information on atmospheric and ecological dynamics in India. The long-term spatial and time series trends of CO2 during 2002-2017 provides useful information about regional climate trends. The observations in this study have examined the monthly, annual, and seasonal dynamics of CO2 across Indian terrestrial ecosystems. The results indicate that there has been a steady increase in CO2 concentrations at a mean rate of 2.1 ppm annually in India. Seasonal analysis reveals that there are higher concentrations of CO2 in winter, primarily due to reduced atmospheric mixing, and lower concentrations during the monsoon, resulting from increases carbon uptake by vegetation. Correlation analysis between CO2 and NDVI on a monthly basis showed strong vegetation effects in April, May, and August and weak correlations in other months due to anthropogenic influences. An effective approach, namely the Standard Deviation Ellipse (SDE), has been used in this study to elucidate the spatial behaviour and directionality stability of CO2 patterns of concentration that would not have been identified in traditional pixel-based analysis. The results of the SDE indicate that the geographical location of the CO2 concentration was stationary throughout time, implying that there still existed emission hot spots in central India. This study primarily presents a dynamic and holistic view of the Indian carbon landscape by integrating a solid SDE model with the use of various data source during 2002-2017. The outcomes of the present study provide useful guidance on mitigation measures and highlight the need to resolve long-term sources of emissions.

In this article, two active satellite observations namely Ku band Scatsat-1 (13.454 GHz) and C-band Sentinel-1 (5.35 GHz) were used for the accurate retrieval of wheat crop growth variables with the development of novel Frequency Difference Vegetation Index (FDVI). The coinciding VV polarized temporal observations of both satellites at spatial resolutions of 2 km are considered for the computation of frequency difference vegetation index (FDVI) at various wheat growth stages. The temporal variations of backscattering coefficients from both satellite missions and FDVI were evaluated by regression analysis with the ground measured crop growth variables such as vegetation water content (VWC), leaf area index (LAI), and plant height (PH) over six different test sites in terms of R2 and RMSE. In addition, FDVI is also evaluated using MODIS LAI and MODIS NDVI derived VWC over the six different fields. Results showed that the FDVI is highly correlated with all the crop growth variables over different fields. The average value of R2(0.714, 0.850, and 0.690) and RMSE (0.210 kg.m-2, 0.147 m2m-2, and 0.214 cm) were found for VWC, LAI and PH, respectively. However, in case of MODIS NDVI derived VWC (R2=0.677, RMSE = 0.195) and MODIS LAI (R2=0.646, RMSE = 0.198), good performances were also obtained like in-situ measurements. The results stated that the FDVI may be a robust method for the monitoring of wheat crop variables and thus may help in crop biophysical parameter estimation. The outcome may also be used in the improvement of crop growth variables retrieval NISAR mission observations from two S- and L-band co-located sensors. FDVI can also be effectively utilized for soil moisture retrieval within passive microwave soil moisture retrieval algorithms.

This study compares component substitution (CS) and multiresolution analysis (MRA) pansharpening algorithms applied to high-resolution WorldView-4 imagery over the Indian Western Himalaya. The performance of these methods was evaluated using quantitative (i.e., visual assessment) and qualitative metrics (such as Relative Average Spectral Error (RASE), Root Mean Square Error (RMSE), Error Relative Global Dimensionless Synthesis (ERGAS), Bias, and the Fidelity-Deformation (FD) metric). The FD metric captures both spectral fidelity and spatial structure preservation by integrating localized and global error measures. The results indicated that MRA-based approaches (i.e., ATWT_M2, M3, and MTF_GLP) exhibit reduced spectral distortions, as reflected by lower Bias and RASE values, making them suitable for applications that demand high spectral fidelity. In contrast, CS-based approaches, such as HCS and BDSD, achieved lower ERGAS and RMSE values, suggesting improved spatial detail preservation. Overall, although pansharpened imagery may be advantageous for developing fine-resolution applications, the choice of the pansharpening algorithm should be made carefully, considering the specific application.

The need for a robust food security framework in India requires assessing the effects of air quality and weather on crop yields, while adopting practices such as choosing suitable varieties, adjusting planting schedules, and improving irrigation to reduce adverse impacts. In this study, a long-term assessment of the impact of weather, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), soil moisture (SM), and Aerosol Optical Depth (AOD) on historical rice production was conducted across various Agroclimatic Zones in India from 1998 to 2019. A statistical model was developed for this purpose, achieving an exceptional accuracy of 94.9 The graphical abstract visually represents the development and assessment of a rice yield prediction model with 94.9

Agricultural land classification is a crucial and demanding task, essential for managing resources and tracking changes in farming activities. Remote sensing (RS) is an excellent technology for monitoring agricultural land and detecting seasonal fluctuations globally. Deep learning models offer promising prospects for crop monitoring. While traditional machine learning methods struggle to capture temporal variations in agricultural land efficiently. This study addresses the challenge of accurately classifying land cover and detecting year-to-year changes using a deep learning (DL)-based approach. The novelty of this research is an integration using a pixel-based deep neural network (PDNN) classifier, which will advance the classification abilities for identifying land cover classes. By comparing images taken over time, the PDNN can help identify different land cover efficiently. The thematic images using the PDNN were derived, and change detection was carried out by adopting a posterior probability space (PPS)-based change detection. The application of the proposed model is demonstrated using the Landsat-9 dataset over Moga District, Punjab, India. Compared to the random forest (RF) and support vector machine (SVM), the PDNN model achieved great performance. While PDNN had an accuracy ranging from 90.6 % to 93.6 %, RF and SVM had lower accuracies, with RF ranging between 86.8 % and 92.2 % and SVM between 88 % and 92.4 %. The PDNN model also excelled in detecting changes in land cover, showing an accuracy between 87.4 % and 90 %, while RF achieved 82.9 %-86.2 % and SVM ranged from 79 % to 83.9 %. The proposed model was adept at capturing changes in agricultural land cover, such as year-to-year variations. The PDNN model demonstrated superior proficiency in capturing seasonal and year-to-year variations in agricultural land cover, effectively identifying subtle transitions in crop cycles. This highlights its potential for long-term agricultural monitoring and precision farming applications. This approach would serve as a key to sustainable agriculture, which guides farmers and policymakers to make better choices.

Air pollution is an important worldwide issue, especially pronounced in metropolitan and suburban regions, significantly affecting both public health and surroundings. This study investigates the particles' morphology and elemental analysis in Varanasi, a highly inhabited metropolis in the Indo-Gangetic Plain. The research was conducted over a year, from April 2019 to March 2020, utilizing Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy, Ion Chromatography, and Atomic Absorption Spectroscopy to analyse particulate matter. Results indicated that mean values of PM (2.5 )and PM10 were 106.5 +/- 67.2 mu g/m(3) and 180.8 +/- 71.4 mu g/m(3), respectively. Often, these amounts exceeded the National Ambient Air Quality Standards. SEM-EDX analysis revealed diverse particle morphologies, with significant contributions from both manmade sources including industrial activities and vehicle emissions, and natural sources, like soil dust. Elemental analysis identified major components, including Carbon, Oxygen, Fluorine, Aluminium, and Silicon. IC analysis highlighted dominant ionic species, such as Ca++, SO4-- , NO3-, and Cl-, with monthly variations reflecting different emission sources. Heavy metals concentrations such as Ni, Cd, Cr, Mn, Cu, Pb, Zn, and Fe were quantified, with concentrations varying significantly across months. The findings underscore the complex nature of aerosols in Varanasi and highlight the immediate need for targeted control over air quality measures to minimize the particulate matter's detrimental effects on the local population and ecosystem.

The significance of the Normalized Difference Vegetation Index (NDVI) variations in India lies in its implications for ecosystem health, agricultural productivity, and climate change monitoring. This study aims to assess the NDVI patterns across India with different spatial/temporal scales. The spatial and temporal NDVI variations were examined through geospatial methods using satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2010 across India. It was discovered that the time series of India's annual averaged NDVI from 2001 to 2010 shows a positive trend with a slope of $0.003 / \text{yr}$. It also suggests that NDVI values were lower in arid regions like Rajasthan and Gujarat, while they were greater in the western Himalayas, north-eastern states, and coastal areas. The study also found that, as a result of increased vegetation growth, NDVI values increased during the monsoon months (June to September) and declined during the dry winter months. It has also been discovered that changes in land use and cover, along with variations in temperature and rainfall, have a major influence on NDVI patterns. The understanding of the spatial and temporal variation of NDVI over the Indian climate region is crucial for ecosystem management, agricultural planning, and climate change mitigation. The findings of this study can serve as a valuable reference for policymakers, land managers, and researchers working towards sustainable land use practices, conservation efforts, and climate adaptation strategies in India.

The long-term spatiotemporal vegetation dynamics with climate variables is essential for effectively managing environmental assets. This study presented the long-term spatio-temporal trends of vegetation dynamics and its association with rainfall/temperature in Koppen climate regions from 2000 to 2022. Linear regression (LR) and the Mann-Kendall (MK) test used to analyse the inter-annual and seasonal long-term spatiotemporal trend of vegetation dynamics across the Koppen climate regions during 2000–2022. The parameters (slope, Sen’s slope, Pearson correlation coefficients, Z value, p-value, Kendall Tau, etc.) from the LR and MK test with Sen’s slope are quantifying the significance of vegetation dynamics trends, and their association strengths with climate variables. The statistically significant and strong upward trend of inter-annual NDVI dynamics are found across the Indian region (growth rate of 0.0034/yr) and Koppen climate regions (growth rate ranges for five climate regions is 0.0029/yr–0.0043/yr and for Mountain Climate Region is 0.0011/yr). The statistically significant and strong upward trend found in seasonal NDVI growth for the monsoon, post monsoon, and winter seasons. However, the statistically significant and weak upward trend found in the pre-monsoon season for the Indian region and most of the climate regions. A significant positive association of vegetation dynamics observed with the rainfall across all seasons and climate regions, except the Mountain Climate Region. However, an inverse association of vegetation dynamics observed with temperature across all climate regions, except in a few cases. These findings have significant impacts, influencing decisions in land management, conservation, and strategies for regional climate resilience.

The complex relationship between climate change and global potato production is examined in this extensive review article. The changing climate has caused changes in temperature, precipitation patterns, and the frequency of extreme weather events, posing unprecedented challenges to potatoes, a critical staple. Global agricultural productivity, growth, and tuberization are all impacted by these climate changes that upset basic physiological processes. Different regions are more vulnerable than others, which increases the risks even more. In order to mitigate these repercussions by strategic actions, the report highlights how vital it is to understand them. The adoption of novel agronomic techniques, the execution of sustainable water management plans, and the creation of climate-resilient potato varieties through breeding programs are some of the suggested actions. To further enhance resilience in potato farming systems, coordinated methods to disease and pest management and farmer education are essential. The review emphasizes how crucial it is to have coordinated policy frameworks and international cooperation in order to handle the intricate problems that climate change presents to the world's potato production. In response to the changing environment, the article provides a road map for sustainable and adaptable tactics to ensure the continued existence of this essential crop.

In this study, the temporal variation of Equivalent Black Carbon (eBC) and its source apportionment is studied using a yearlong (Dec. 2020-Nov. 2021) multiwavelength Aethalometer (AE-33 model) measurements over Varanasi, located in the central Indo-Gangetic Basin (IGB). Results suggest that mean mass concentrations of eBC vary in the range between 0.46 +/- 0.13 to 11.22 +/- 5.09 mu g m(-3) with an annual mean value of similar to 3.57 +/- 2.39 mu g m(- 3) during the study period. A strong temporal variation in eBC and its components i.e., eBCff (eBC from fossil fuel), and eBCbb (eBC from biomass burning) are found which shows a large variation on different temporal scales with an average value during winter (6.21 +/- 3.56 mu g m(-3)), summer (5.09 +/- 3.61 mu g m(-3)), monsoon season (1.52 +/- 1.03 mu g m(-3)), and post-monsoon (3.75 +/- 2.68 mu g m(- 3)). The diurnal variation of eBC shows two different maxima between 07:00-08:00 a.m. and 08:00-10:00 p.m. An inverse relationship between eBC concentration and all meteorological parameters (temperature, wind speed, and boundary layer height) is found except relative humidity. The concentration of eBC increases with respect to RH (up to 70 %) suggesting hygroscopic growth while for higher RH (>70 %) value, eBC concentration decreases and indicates the possible wet scavenging processes in the atmosphere. Source apportionment of eBC using the "Aethalometer Model" reveals that eBCff is dominant over eBCbb in total eBC loading during the study period. Cluster analysis of HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model computed five days airmass back-trajectory suggests that airmass reached at Varanasi passes through a highly dense fire count region over the northwestern IGB and surrounding which could be the most responsible for the black carbon loading over the study region.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

The analysis of crop variation and the ability to quantify it is a critical and challenging task. Remote sensing (RS) has proven to be an effective tool for monitoring crops and detecting seasonal variations worldwide. This opens new opportunities for developing effective crop monitoring models, with deep learning models showing great promise. This study presents a deep learning-based U-Net v5 Change Detection (UCD) model capable of identifying and monitoring the spatio-temporal variations in crop fields. The application of the model is demonstrated using Sentinel-2 imagery over Patiala district in India to monitor the seasonal crop variation (rabi crop) during 2017-2018. The results have shown that the UCD model has achieved better results (95.6-98.4%) in accuracy for classified maps and more than (91.6%-96.6%) in accuracy for change maps. This study will be useful for crop monitoring, precision agriculture and crop yield prediction and can assist in decision and policy making towards a more sustainable environment.

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

This study is about the influence of film thickness and temperature on the magnetic characteristics of Co/Au bilayers. Through a combination of X-ray Reflectivity (XRR), Reflectivity High Energy Electron Diffraction (RHEED), and Magneto Optic Kerr Effect (MOKE) in situ measurements, we meticulously examine how these parameters impact the behavior of the system. Our observation reveals intriguing dynamics such as, that thinner Co layers prompt the emergence of island-like structures on the Au surface, thereby amplifying coercivity values. Additionally, we unveil the remarkable thermal stability of the Co/Au bilayer up to temperatures of 300 °C. However, beyond this threshold, inter-diffusion between the Co and Au layers ensues, leading to alterations in surface roughness. Furthermore, our scrutiny highlights the prominent role played by the nonmagnetic layer in thinner Co layers, as well as the formation of a Magnetic Dead Layer (ML). These novel findings provide crucial insights into the intricate interplay between film thickness, temperature, and surface morphology in Co/Au bilayers, distinguishing our study from previous research articles. These insights are crucial for tailoring the magnetic properties of thin film systems, offering potential applications in the areas of spintronics and magnetic data storage applications. Overall, our comprehensive study in this article contributes to advancing the understanding of magnetic material behavior in the bi-layer system through through in situ investigations opening new avenues for optimizing thin film technologies for practical applications.

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 $0.059\,\,m^{3}m^{-3}$ with bias and correlation coefficients of $0.036\,\,m^{3}m^{-3}$ and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE = $0.059\,\,m^{3}m^{-3}$ , bias = $0.024\,\,m^{3}m^{-3}$ , 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.

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.

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.

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.