Meteorological Monitoring Research Articles

Validation of a laboratory method for the traceability of a rainfall weighing gauge

This study aims to develop a laboratory method for the traceability of a rainfall weighing gauge, including an evaluation of the measurement uncertainty. The adopted procedure is similar to the one used for the non-automatic weighing instruments. A static approach is followed to achieve the calibration deviation of the precipitation scale. The method used to evaluate the measurement uncertainty is based on a nonlinear mathematical model. The Monte Carlo method is used to calculate uncertainties and validate estimates following the conventional Guide to the Expression of Uncertainty in Measurement (GUM) approach. Measurement uncertainty contributions of input quantities to the mathematical model used to calculate rainfall also require specific calibration procedures. Results show the accuracy level achievable with rainfall weighing gauges commonly used as a reference for meteorological monitoring networks and data modelling.

Government Support Mechanisms for Sustainable Agriculture: A Systematic Literature Review and Future Research Agenda

Government support for enterprises can be provided in different forms, such as subsidies, tax incentives, or direct public investment. Government support can also be given to develop environmentally and socially responsible initiatives. In the agriculture sector, government support involves providing early-warning information from meteorological monitoring, policies to encourage companies and citizens to engage in sustainable behavior, and the organization of disaster relief activities. A comprehensive view of the government support mechanisms employed in the agriculture industry and their possible effects on the adoption of sustainable initiatives is yet to be provided in the literature. Through a systematic literature review of articles published from 1992 to 2023 in the Web of Science and Scopus databases, this study found that subsidies, programs, regulations, and financial assistance are the main mechanisms used by governments to support sustainable initiatives in agriculture. Moreover, our review also found that the reduction in environmental impacts, the stimulus to organic production, industry growth, and rural development are the most common goals of using government support for sustainable agriculture. We also geographically categorize research in this field by showing where research has been produced and where it should be improved. We call for more research focused on support from governments in South America. We also observed that environmental protection, the supply of organic foods, and urban agriculture are frequently impacted when there is not enough government support. At last, we propose some research questions to guide future studies. We expect that policymakers will use the outcomes of this study to guide the design and implementation of new government support mechanisms aimed at improving the sustainable performance of agriculture firms. The outcomes of this study will also help managers make investment decisions.

Development of a new intelligent instrument for visibility and turbulence meteorological environment parameters in traffic network

The number of transport network systems in the world is increasing and the transport network passes through different geographical environments, there is an urgent need to develop an environmental meteorological monitoring equipment for the transport network security. So, in this study, a new intelligent instrument for monitoring the visibility and turbulence meteorological environmental parameters (temperature, pressure, humidity, etc.) of traffic networks (IVMPT) is developed. The feasibility of simultaneous measurement of visibility, turbulence and various meteorological elements is verified. The atmospheric visibility is measured based on the extinction principle and the atmospheric turbulence in a certain range based on the light intensity scattering principle. The commercial Visibility instrument (DNQ2-1) and Micro-temperature Sensor are used to compare with our new instrument. The results show that the difference between the visibility and turbulence measurements and the comparison instrument is 3% and 8.4%, respectively. After comparative examination, the new instrument meets the design requirements. In addition to environmental meteorological monitoring applications in the transport network, the instrument provides a new scientific instrument for the study of aerosol-turbulence interactions in basic science issues.

Development of a nonstationary Standardized Precipitation Evapotranspiration Index (NSPEI) and its application across China

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs.

Evaluation of Daily Temperature Extremes in the ECMWF Operational Weather Forecasts and ERA5 Reanalysis

In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from two European Centre for Medium-range Weather Forecasts (ECMWF) products: ERA5 reanalysis (1980–2019) and operational weather forecasts (2017–2021). Both products are evaluated using in situ observations from the Global Historical Climatology Network (GHCN). While the analyses span globally, emphasis is given to four key regions: Europe, East and West United States, and Australia. Results reveal a general underestimation of TMAX and overestimation of TMIN in both operational forecasts and ERA5, highlighting the limitation of the ECMWF model in estimating the amplitude of the diurnal cycle of air temperature. ERA5′s accuracy has improved over the past decade, due to enhanced constrain of land–atmosphere analysis streaming from more and higher-quality satellite data. Furthermore, ERA5 outperforms one-day-ahead weather forecasts, indicating that non-real-time dependent studies should rely on ERA5 instead of real-time operational forecasts. This study underscores the importance of ongoing research in model and data assimilation, considering the relevance of daily temperature extremes forecasting and reanalysis for operational meteorology and climate monitoring.

Fine particle pollution during megafires contains potentially toxic elements

Wildfires that raged across Australia during the 2019–2020 ‘Black Summer’ produced an enormous quantity of particulate matter (PM) pollution, with plumes that cloaked many urban centres and ecosystems along the eastern seaboard. This has motivated a need to understand the magnitude and nature of PM exposure, so that its impact on both built and natural environments can be more accurately assessed. Here we present the potentially toxic fingerprint of PM captured by building heating, ventilation, and air conditioning filters in Sydney, Australia during the peak of the Wildfires, and from ambient urban emissions one year later (Reference period). Atmospheric PM and meteorological monitoring data were also assessed to determine the magnitude and source of high PM exposure. The wildfires were a major source of PM pollution in Sydney, exceeding the national standards on 19 % of days between November–February. Wildfire particles were finer and more spherical compared to Reference PM, with count median diameters of 892.1 ± 23.1 versus 1484.8 ± 96.7 nm (mean ± standard error). On an equal-mass basis, differences in potentially toxic elements were predominantly due to higher SO42−-S (median 20.4 vs 4.7 mg g−1) and NO3−-N (2.4 vs 1.2 mg g−1) in Wildfire PM, and higher PO43−-P (10.4 vs 1.4 mg g−1) in Reference PM. Concentrations of remaining elements were similar or lower than Reference PM, except for enrichments to F−, Cl−, dissolved Mn, and particulate Mn, Co and Sb. Fractional solubilities of trace elements were similar or lower than Reference PM, except for enhanced Hg (12.1 vs 1.0 %) and greater variability in Cd, Hg and Mn solubility, which displayed upper quartiles exceeding that of Reference PM. These findings contribute to our understanding of human and ecosystem exposures to the toxic components of mixed smoke plumes, especially in regions downwind of the source.

Estimation of the Carrying Capacity and Relative Stocking Density of Mongolian grasslands under various adaptation scenarios

Mongolia's vast grasslands, crucial for both environmental and economic stability, are currently facing challenges due to overgrazing, climate change, and land-use changes. Understanding and effectively managing their Carrying Capacity (CC) and Relative Stocking Density (RSD) is essential for maintaining ecological balance. This study rigorously evaluates the CC and RSD of Mongolia's grasslands through an innovative approach that integrates ecological models with socio-economic data, aimed at improving grazing management practices. Data from the National Agency for Meteorology and Environmental Monitoring validates the model, providing precise CC and RSD estimates at the Soum level from 2000 to 2019. The study reveals significant regional variations in CC: northern grasslands exhibit a high CC of 2.8 Sheep Units (SU) per hectare, contrasting with the fragile CC in some southern regions, like the Gobi Desert, where it is as low as 0.3 SU per hectare. Approximately 38.8 % of Mongolia's territory maintains a CC exceeding 1.0 SU per hectare, indicative of sustainable grasslands. In contrast, 41.7 % of the land, primarily in southern regions, shows CCs below 0.5 SU per hectare, highlighting ecosystem vulnerability. The RSD, reflecting livestock numbers relative to CC, averages 1.07, suggesting a high livestock concentration near Ulaanbaatar but a more sustainable density across 43.2 % of the country. The research also explores adaptation scenarios against desertification and degradation, as well as improving pasture accessibility, providing insights for future grassland management strategies. In conclusion, this study emphasizes the need for sustainable land management practices to balance carrying capacity and stocking rates, offering a vital tool for policymakers and stakeholders in grassland conservation.

Ditch control and land reclamation promote vegetation recovery in Loess Plateau.

This study aimed to assess the impact of land consolidation projects and climate change on changes in vegetation in the Loess Plateau during 2012-2021. The study also explored the impacts of human activities and climate change on the ecological quality of the Loess Plateau during this period. The spatial and temporal normalized difference combined meteorological monitoring data, project data, and normalized difference vegetation index (NDVI) data that was used to create the vegetation index dataset spanning from 2012-2021. The study discussed and assessed the effectiveness of the project, revealing the following results: 1) A significant increase was observed in the vegetation index of the Loess Plateau region from 2012 to 2021, with an upward trend of 0.0024 per year (P < 0.05). 2) Contributions to changes in vegetation attributed to climatic factors and the anthropogenic factors of the ditch construction project were 82.74 and 17.62%, respectively, with climatic factors dominating and the degree of response of the ditch construction project increasing annually. 3) In the Loess Plateau, climatic variables dominated changes in vegetation. However, land consolidation projects in vegetation factors played a key role in changes in vegetation, and the degree of influence was gradually increasing.

2013–2022年云南丽江森林生物多样性国家野外科学观测研究站日气象数据集

Meteorological factors are important environmental elements of field positioning observation stations of CNERN, CERN and CFERN. The acquisition of high-quality observational data through long-term positioning observations is beneficial for advancing our scientific understanding of ecosystem processes and tracking dynamic changes in biodiversity. It also provides important support for the development of regional and national ecological, environmental, and resource security construction initiatives. As one of the Forest Biodiversity National Observation and Research Stations approved in 2021, Lijiang Station has been collecting data in accordance with the CERN meteorological monitoring standards for past consecutive years. Up to now, Lijiang Station has consistently maintained comprehensive data parameters, a structured approach, and a high level of data standardization. These data can serve as important foundation for various research endeavors, including dynamic changes in the structure, processes, and functions of regional forest ecosystems, the formation, evolution, and preservation of regional biodiversity mechanisms, forest biodiversity and ecosystem functioning, the monitoring, evaluation, and conservation of biodiversity for key wild protected species, and the assessment and innovative exploitation of distinctive biological resources. This dataset comprises 3,652 entries and 39 fields of daily meteorological data spanning from 2013 to 2022. It covers atmospheric elements (air temperature and humidity, atmospheric pressure, wind speed and direction, precipitation, radiation elements) and soil elements (soil stratified temperature and soil heat flux). The release of the dataset offers comprehensive fundamental meteorological data for scientific research in related fields and provides essential data support for local and large-scale networking research.

WEATHER STATION DESIGN USING ARDUINO PLATFORM

The paper presents an application that involves the real-time acquisition of four parameters: temperature, relative humidity, pressure and light intensity. Several sensors are used that acquire these values (DHT11- temperature and relative humidity, BMP180 - pressure, BH1750 – light intensity) connected to an Arduino Uno development board equipped with the Atmega328 microcontroller, using digital and analog pins. The display of information is done on a monochrome screen attached to the Arduino board. The developed weather station is capable of measuring these four environmental parameters and providing readings at 5-minute intervals. Weather stations can be used in a variety of locations and domains from meteorology and weather monitoring, to agriculture, scientific research, navigation and aviation, or renewable energy industry. The weather stations can provide useful information on different weather parameters which is essential for providing accurate weather forecasts and monitoring climate changes, for optimizing the performance of energy installations and forecasting energy production.

Performance and uncertainties of five popular satellite-based precipitation products in drought monitoring for different climate regions

Satellite-based precipitation products (SPPs) can provide crucial precipitation estimations for large-scale, near-real-time meteorological drought monitoring. However, the performances and uncertainties of SPPs in capturing drought information may vary greatly by product and climate region and are still poorly understood. In this study, we comprehensively evaluated the accuracy of five popular SPPs (MSWEP, TMPA 3B42, PERSIANN-CDR, CHIRPS, and COMRPH) in characterizing meteorological drought in mainland China. (1) Under global warming conditions, the standardized precipitation evapotranspiration index (SPEI) identified a significant decrease in drought associated with higher potential evapotranspiration (PET), which indicated the essential role of PET in determining drought evolution over different climate regions. (2) Among all the SPPs investigated, MSWEP, TMPA 3B42, and PERSIANN perform best in capturing the spatiotemporal pattern of the SPEI trend in the arid Tibetan Plateau regions, transition regions, and humid regions, respectively. In terms of drought category and drought characteristics, MSWEP and TMPA 3B42 perform very well for humid regions, and CHIRPS is better for cold and arid regions, with the poorest performance being found in CMORPH. However, no product performs well for all climate regions for drought monitoring. (3) The optimized parameters (OPs) are the dominant factor for drought uncertainties, followed by the interaction effect and SPPs, with great regional dependence. The OPs contributed the most to drought uncertainties over the relatively arid area of northern China, the interaction of OPs and SPPs did so in the humid area of China, and SPPs were found to contribute the most in the central Tibetan Plateau and some parts of humid regions. Our findings provide guidelines on the choice and application of existing SPP datasets for drought monitoring in different climate zones, as well as some clues to further improve the products over areas where they perform poorly.

Study of Cloud Liquid Water Content Over Iraq Through Heavy Rain Cases

Because of heavy rains and drought, Iraq’s water resources have been catastrophically depleted. In this study, we relied on four cities distributed in Iraq. The date of this study where the floods occurred is January 27-28 2019. Torrential rains cause great dangers to lives, agriculture, and water resources, as happened in the history of this study. These risks can be avoided by knowing more accurately the behavior of the cloud. The basic building block for knowing cloud behavior is knowing the behavior of the liquid water content cloud (CLWC). Through it, we addressed the issue of the characteristics of the CLWC, its variation with time and space, and its vertical distribution within the cloud over Iraq, affected by the synoptic low-pressure system. Through this study, they noticed a significant difference in the behavior of CLWC in the different cities of Iraq. We also determined the nature of the relationship between CLWC and relative humidity, air temperature, and precipitation. Data used in the study are taken from ERA5 | ECMWF, and the General Authority for Meteorology and Seismic Monitoring).

A development of a calibration method for field pyranometers using satellite image data

In this work, a calibration method for deriving a sensitivity of field pyranometers using Himawari satellite image data was developed. The purposed method representing a relation between 10–bit digital counts ranging between 0–1023 from satellite data and global solar irradiance of a standard pyranometer measured at Nakhon Pathom (13.82 °N, 100.04 °E) was investigated and it was found to be an exponential relation. Afterward, this model was applied to estimate the sensitivity of field pyranometers installed at three meteorological monitoring stations located in the main regions of Thailand namely, Chaing Mai in the North (18.78 °N, 98.98 °E), Ubon Ratchathani in the Northeast (15.52 °N, 104.87 °E) and Songkhla in the South (7.20 °N, 100.60 °E) for the validation. The results showed that the sensitivity of field pyranometers derived from satellite data and that obtained from the standard outdoor calibration method of ISO9847 was in a very good agreement with a discrepancy between both datasets of about 1%.

Visibility estimation via near-infrared bispectral real-time imaging in bad weather

This manuscript proposes a physical inversion based visibility estimation model. The proposed approach uses a dual-spectral imaging system, which is composed of near-infrared cameras, lenses, filters, condenser, and spectroscope. The visibility is estimated using the two images captured over the selected near-infrared wavelengths. In order to increase the precision of estimated visibility, a visibility error correction model based on a machine learning regression algorithm is proposed. We address the issue of accurate visibility estimation, which is essential for various applications in environmental conservation, meteorological monitoring, and industrial pollutant emission surveillance. The results of experiments, which have conducted in this study, validated the effectiveness of our proposed method in visibility estimation in comparison with the existing methods.

Effectiveness of potato late blight (Phytophthora infestans) forecast by meteorological estimation in mountainous terrain based on CARAH rules

Potato late blight (PLB) caused by Phytophthora infestans (Mont.) de Bary, its incidence and development are highly dependent on meteorological conditions. To solve the problem of PLB in mountainous terrain under the condition of limited meteorological monitoring capability, the air temperature and humidity was estimated based on the basic meteorological datasets, the forecast effect of the onset period and infection cycle of PLB based on CARAH rules was evaluated. The average MAE, RMSE and CI of the estimated air temperature and observations were 1.17 °C, 1.52 °C and 0.95, respectively. The average MAE, RMSE and CI of the estimated relative humidity and observations were 8.0 %, 10.7 % and 0.53, respectively. The curve of the infection cycle of PLB at different locations were estimated from the basic meteorological datasets based on the CARAH rules, and the false alarm and missing ratios were 8.8 % and 4.6 % respectively. It may be delayed by 1 or 2 fungal generations compared to the observations, and then the protective fungicide should be adjusted to a systemic fungicide. The false alarm of the infection cycle of PLB may increase in dry air conditions, and the missing report may occur in humid condition.

Integrated soil-fruit-climate management system to improve apple production stability

Unstable agricultural systems, such as unreliable fruit production, threaten global food security and could negatively impact human nutrition. However, methods of maintaining a stable fruit supply have received little attention. Therefore, this study explored the impact of a systematic service model on the establishment of an integrated soil-fruit-climate management (ISM) approach and stabilization of apple yield based on a fixed-effects model using sample data from Fuji apple farmers in China for six consecutive years (2016–2021). The results showed that the systematic service model dramatically improved the adoption of ISM-based technology. By 2021, the rate of ISM technology adoption among farmers reached 84.4 %, especially pendulous branch-fruiting technology and Osmia pollination, which increased by 82.2 % and 37.8 %, respectively, compared to that in 2016. With the adoption of the ISM approach, apple yields and tree loadings stabilized over time. For example, the yield change rate and tree loading change rate were 14.8 % and 7.3 % lower, respectively, in 2021 than in 2016. We demonstrated through a fixed-effects analysis that the ISM approach exerts a fully mediated effect on the mechanism of action of service measure quantity disclosure, which positively impacted the rate of change in yield and tree loadings. This finding indicated that the six-in-one systematic service model of soil testing, pest and disease diagnosis, course training, meteorological monitoring, on-site guidance, and experimental demonstration established by the Science and Technology Backyard model resolved the information, goal, and hardware gaps that limited the adoption of this technology by farmers and promoted the construction of an integrated soil-fruit-climate management approach, which in turn has stabilized the apple production system.

Micrometeorological Analysis and Glacier Ablation Simulation in East Kunlun

Worldwide, there are great challenges for meteorological monitoring and glacier ablation monitoring in high-altitude mountain areas. It is often difficult to capture fine-scale climate and glacial changes in high-altitude mountainous areas due to the harsh natural environment and the extreme lack of observational sites. Based on high-altitude meteorological stations erected on the eastern shore of Aqikkule Lake (AQK) and at the terminus of Shenshechuan Glacier (SSG), as well as on mass balance data from SSG, the characteristics and correlation of temperature, solar radiation, relative humidity, precipitation, wind speed and direction of the two regions, and the mass balance in the ablation area of SSG from 30 May 2022 to 18 May 2023 were analyzed, and the average melting depth of SSG was simulated. The results indicate the following: (1) The average annual temperature of AQK and the terminus of SSG is −3.7 °C and −7.7 °C, respectively, and the vertical lapse rate of temperature in the summer half of the year is greater than that in the winter half of the year. Precipitation timing has a great influence on daily temperature differences. (2) Precipitation in both places is concentrated in summer; the glaciers in this area are of the summer recharge type, and precipitation has a significant reducing effect on the solar incident radiation and increases the relative humidity in this region. (3) AQK and SSG both have local circulation development, in the area of AQK all year round due to the lake effect, while the terminus of SSG only has the development of valley winds in the summer, being controlled in the winter by the westerly wind belt. (4) The average mass balance value of the ablation area of SSG was −1786 mm as measured by the range poles method. The average annual ablation depth of SSG simulated by using the empirical formula was 587–597 mm, which is not large compared with other glacier areas in the Tibetan Plateau, and it has the characteristics of typical continental-type glaciers.

Distribution characteristics of snow and evaluation of snowdrift susceptibility along the Xinku section of Yining-Aksu Railway in China

The frequent occurrence of snowdrift hazards in the Tianshan region of China often poses a serious threat to transportation corridors, such as roads and railways. Revealing and understanding the characteristics of snow distribution and using a mixed model to assess the susceptibility to snowdrifts can effectively and quickly evaluate the regional risk of the snowdrift. In this study, the key factors affecting the formation of a snowdrift in the study area, such as snow, are analyzed by utilizing remote sensing interpretation, meteorological monitoring, and field investigation along the Xinyuan-Kutaikelike railway section of Yining to Aksu Railway in China as the study area object. Based on the multi-source data, ten snowdrift susceptibility evaluation factors were extracted with the help of GIS. On this basis, three susceptibility models were used to evaluate the snowdrift susceptibility, namely the subjective evaluation method – Analytic Network Process (ANP) method, the objective evaluation method - CRITIC weighting method, and the combination of subjective and objective Game theory combination method. The evaluation showed that the susceptibility indices obtained by the ANP, CRITIC, and Game theory combination were in the ranges of [0.243, 0.746], [0.202, 0.751], and [0.249, 0.735] respectively, with the percentages of the area of medium susceptibility and above being 67.3%, 67.2%, and 67.5%. All three models exhibited suitable robustness. The predicted results of the three models were verified by the field survey, and it was found that the results of the Game theory combined model were in good agreement with the field survey, which allows it to be suitable for the study of snowdrift susceptibility zoning in the study area. The results revealed that the medium-high susceptibility areas of a snowdrift were mainly in the grassland area of the Small Yultuz Basin and the A'erxian-Bayinguoleng area. The high susceptibility zones were predominately located in the Nalati Ridge, the northern ridge of the Koktag Mountains, the northern ridge of the Huola Mountains, and the pre-mountain alluvial and floodplain areas of these mountain systems. This study provides a scientific basis and methodology for preventing and mitigating snowdrift disasters in cold regions, specifically for major engineering constructions such as transportation corridors.

Structural temperature gradient evaluation based on bridge monitoring data extended by historical meteorological data

The structural temperature gradient (STG) is one of the most key factors causing cracking and even damage to bridge structures. However, its real effects on bridge structures are often over- or underestimated in practice. For most operating bridges, the structural health monitoring systems have just been put into use recently, and the monitoring structural temperature data are limited, which always leads to unreasonable STG representative value for a long return period based on such short-term structural temperature data. To solve the problems, this article proposes an STG determination method based on the long-term historical meteorological parameters at bridge sites. First, the main meteorological parameters affecting the STG were determined by correlation analysis. Second, considering the different influence mechanisms of various meteorological conditions on STG, a training sample set construction method is proposed by clustering the meteorological parameters and STG monitoring data. Based on such training data, a correlation model between STG and meteorological parameters can be established to extend the STG dataset based on the historical meteorological data. Finally, the peak over threshold method is applied to analyze the obtained extended STG data and to estimate its representative value. The proposed method was verified through a long-span cable-stayed bridge. The results show that the monitoring dataset of the STG can be effectively extended through the established correlation model. Compared with the short-term monitoring data, more reasonable representative values of the STG can be obtained through the extended dataset of monitoring STG.

Impact of Land Surface Temperature (LST) and Ground Air Temperature (Tair) on Land Use and Land Cover (LULC): An Investigative Study

The Land Surface Temperature (LST) is the fundamental concept of measuring the temperature of the ground or soil using remote sensing technologies. LST is an essential environmental parameter that has several uses in meteorology, climatology, agriculture, urban planning, and environmental monitoring. Remote sensing instruments, such as thermal infrared sensors aboard satellites, provide the means to capture LST data on a global scale. LST and ground air temperature ( ) are two related but distinct measures that provide valuable insights into different aspects: LST refers to the temperature of the Earth's surface, and it provides information about the temperature of the soil or crop canopy, which can impact plant growth and development, on the other hand measures the temperature of the surrounding air, which influences the rate of evaporation, transpiration, and photosynthesis in plants. In this study, a linear regression equation was developed to correlate the LST and using air temperature data of 19th March 2017, and 14th March 2021 from the two meteorological stations namely Veganpur and Main Maize Research Station (MMRS) of the Godhra region. The Landsat 8 thermal and optical bands were used to estimate LST, whereas Sentinel 2 optical data was used for the Land Use and Land Cover (LULC) purpose. To correlate LST with , eight ground truth (GT) points were collected from both stations, namely the minimum and maximum air temperatures in 2017 and 2021. The satellite derived LST data and the maximum ground air temperature ( ) were found to strongly concur, with an R2 value of 0.97. It was noted that a temperature difference of 2.9°C was found between the maximum ground air temperature and the Land Surface Temperature (LST). Study showed that from 2017 to 2021, the area used for agriculture, forestry, water bodies, and barren land decreased from 75.62 to 71.51%, 15.17 to 14.30%, 1.60 to 1.38%, and 0.03 to 0.02%, respectively, however the area used for build-up increased from 7.59 to 12.79%. During a five-year period, the study identified an enhancement in Land Surface Temperature (LST) due to an increase in built-up area. A temperature change was also seen throughout the course of the five-year period as a result of variations in LULC. Overall, it came to light that, under specific conditions and speculation, LST estimated using thermal data from Landsat 8 can be closely associated with ground air maximum temperature.