Remote Sensing Applications-Society and Environment最新文献

{"title":"Mapping crop water productivity of rice across diverse irrigation and fertilizer rates using field experiment and UAV-based multispectral data","authors":"Sumit Kumar Vishwakarma,&nbsp;Benu Bhattarai,&nbsp;Kritika Kothari,&nbsp;Ashish Pandey","doi":"10.1016/j.rsase.2025.101456","DOIUrl":"10.1016/j.rsase.2025.101456","url":null,"abstract":"<div><div>Crop water productivity (CWP) is an important indicator for optimizing water use and yield in agriculture. In this study, an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera was used for estimating the CWP of rice crop. To our knowledge, this is the first study that assessed CWP of rice at high spatial resolution in the <em>tarai</em> region of north India using UAV-based multispectral data. A field experiment was conducted in Roorkee, India, where rice was cultivated under two irrigation levels (continuous flooding (CF), and alternate wetting and drying (AWD)) and three nitrogen treatments (high nitrogen (HN): 150 kg/ha, medium nitrogen (MN): 120 kg/ha, and low nitrogen (LN): 60 kg/ha). There were a total of seven treatments (T0 = rainfed, T1 = CF-HN, T2 = CF-MN, T3 = CF-LN, T4 = AWD-HN, T5 = AWD-MN, and T6 = AWD-LN). UAV-derived Normalized Difference Vegetation Index (NDVI) was used for the estimation of crop evapotranspiration (ETa) and CWP. The highest and lowest ETa were found in treatments T4 (316.06 mm) and T0 (311.49 mm), respectively. The above-ground biomass (AGB) and grain yield were calculated using the radiation utilization efficiency (RUE). The model estimated AGB with R² 0.63 and RMSE 0.61 t ha⁻¹, and yield with R² 0.95 and RMSE 0.41 t ha⁻¹. The CWP was highest in treatment T5 (1.13 kg m⁻³) and lowest in treatment T0 (0.76 kg m⁻³). For treatments, T1, T2 T3, T4, and T6, the CWPs were 1.13, 1.13, 1.07, 1.05, and 1.12 kg m⁻³, respectively. Considering the global CWP categories for rice crop as low (≤0.70 kg m⁻³), medium (0.70–1.25 kg m⁻³), and high (&gt;1.25 kg m⁻³), the CWP in the present study was within the medium category. Among the treatments, AWD with MN was found to be the most suitable strategy for achieving high CWP. Monitoring the CWP of rice fields using UAV and providing high-resolution CWP maps could be helpful for farmers and policymakers in better allocating the resources and enhancing resource use efficiency.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101456"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the gas emissions from volcanic activity in the East African Rift System using remote sensing over the past two decades","authors":"Sakine Moradi ,&nbsp;Elham Ghasemifar","doi":"10.1016/j.rsase.2025.101471","DOIUrl":"10.1016/j.rsase.2025.101471","url":null,"abstract":"<div><div>Monitoring volcanic gas emissions is crucial for assessing volcanic hazards, as they vary across spatial and temporal scales more than most other natural hazards and have significant environmental impacts. They can directly affect climate change, which in turn poses challenges to human society and global sustainable development. Satellite remote sensing plays a pivotal role in monitoring and studying atmospheric gases, particularly in regions like the East African Rift System (EARS) that hosts large and active intra-continental rift-related volcanoes. These predominantly mafic volcanoes sit above a major mantle upwelling, which are contributing to the breakup of the East African continent. Volcanic activity in this region has been persistent since Tertiary. Recent advancements in satellite remote sensing technology have greatly enhanced our ability to monitor gas emissions from volcanoes across the globe. However, data on gas composition and emissions in the EARS remains limited. Therefore, the present study focuses on eight volcanoes, including Erta Ale, Alu Dalafilla, Manda Hararo, Fentale, Mount Longonot, Ol Doinyo Lengai (from the eastern branch of the EARS), and Mt. Nyiragongo and Nyamulagira (from the western branch of the EARS). At each volcano, we used data from the atmospheric infrared sounder (AIRS) to measure H₂O, CO, and CH₄ gases, along with the spatial and temporal variability of NO₂ and SO₂ gases from the Ozone Monitoring Instrument (OMI). This combined dataset offers the most comprehensive dataset of gas variations during EARS activity from 2004 to 2024, establishing a robust baseline for future monitoring efforts. To assess the vertical profile of volcanic gases in atmosphere above the EARS, we analyzed Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission over a 12-month period from 2014 to 2024. June 2011 and January 2019 are selected as the representative months, reflecting periods of high and low gases anomalies, respectively. These analyses, which conducted for these months were evaluated using sea level pressure, geopotential height at 850 and 500 hPa, and meridional and zonal winds components, providing a detailed three-dimensional structure of volcanic gases over the EARS. These measurements can also support the development of effective policies to manage air pollution emissions from these volcanoes, which influence various aspects of human life and ecosystems in this region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101471"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using the dry matter productivity model as an estimator of biomass production in native grassland communities","authors":"Thiago Frank ,&nbsp;Carlos Antonio da Silva Junior ,&nbsp;Paulo Eduardo Teodoro ,&nbsp;José Francisco de Oliveira-Júnior ,&nbsp;Jonathan Bennett ,&nbsp;Xulin Guo","doi":"10.1016/j.rsase.2024.101441","DOIUrl":"10.1016/j.rsase.2024.101441","url":null,"abstract":"<div><div>Estimating biomass production in the grassland ecosystem is useful for applications such as determining how much forage is produced for wildlife and livestock, quantifying carbon stocks, monitoring the consequences of climate change, detecting land use change, and determining losses focusing on agricultural insurance. Historically, production estimates required field work in which the biomass went through the collection, drying, and weighing process. Despite being a common process that generates accurate results, this type of activity is time consuming, has high financial costs and has limited geographic coverage. To overcome these limitations, remote sensing emerges as a viable alternative, since the systematic imaging of the Earth allows the characterization of the canopy and the extraction of information that can be useful to estimate biomass production. The main objective of this study is to provide a more accurate method to estimate biomass production in native grasslands communities in three Canadian Prairies ecoregions, as well as to investigate the relationship between biomass production and the dry matter productivity (DMP) model and propose adjustments to increase the prediction power of the model. The highest R value (between biomass and DMP) was observed in the Moist-Mixed Ecoregion (R = 0.71) (R² = 0.50) and the lowest was observed in the Mixed Ecoregion (R = 0.66) (R² = 0.43). By correlating three vegetation indices derived from satellite images (the enhanced vegetation index, the green normalized difference vegetation index (GNDVI), and the normalized difference vegetation index (NDVI)), we identified that NDVI had the best performance to estimate biomass production in the study area. However, by including other variables such as the fraction of absorbed photosynthetically active radiation (FPAR), the leaf area index (LAI), precipitation, and temperature, the NDVI was surpassed by the LAI. It is worth noting that, the best results for estimating the biomass production were obtained via DMP (except for the Mixed Ecoregion where the LAI had a greater correlation with biomass production than the DMP). By including variables such as annual mean temperature and mean annual NDVI as biomass production penalty tools, the prediction power of the DMP increased by 19.10% in the Fescue Ecoregion, 5.47% in the Moist-Mixed Ecoregion, and 20.19% in the Mixed Ecoregion.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101441"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pasture monitoring using remote sensing and machine learning: A review of methods and applications","authors":"Tej Bahadur Shahi ,&nbsp;Thirunavukarasu Balasubramaniam ,&nbsp;Kenneth Sabir ,&nbsp;Richi Nayak","doi":"10.1016/j.rsase.2025.101459","DOIUrl":"10.1016/j.rsase.2025.101459","url":null,"abstract":"<div><div>Pastures are important feed sources for livestock and require an optimal management strategy to boost the productivity and sustainability of grassland. Remote sensing (RS) has been explored for grassland monitoring and estimating pasture biophysical characteristics. The array of sensors, including hyperspectral, multispectral, and RGB, integrated with sensing platforms such as satellites, drones, and ground-based vehicles, yields massive amounts of data. This heterogeneous RS data necessitates machine learning (ML) methods for precisely estimating pasture quality and quantity. This survey aims to provide a systematic review and meta-analysis of the progress in pasture monitoring using RS with ML. First, we propose a taxonomy that assimilates and categorises the existing works based on the various approaches used in the RS data processing pipeline. Second, we analyse and synthesise the performance of ML methods on the RS data for pasture monitoring tasks such as pasture identification and classification, biomass estimation, and pasture quality estimation. Finally, we report the survey findings and underscore the challenges and future avenues of research in pasture modelling using hybrid RS with ML approaches. The survey highlights that integrating RS data into ML models has demonstrated considerable success in pasture monitoring, particularly in biomass estimation, whereas pasture quality estimation warrants elevated focus in future research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101459"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring ground surface deformation in the Kharga and Dakhla Oases in Egypt using persistent scatterer interferometry technique","authors":"Kyotaro Kurokami ,&nbsp;Akira Hama ,&nbsp;Erina Iwasaki ,&nbsp;Nobuhiro Matsuoka","doi":"10.1016/j.rsase.2025.101495","DOIUrl":"10.1016/j.rsase.2025.101495","url":null,"abstract":"<div><div>Agriculture is the primary sector in the Kharga and Dakhla oases of the Western Desert in Egypt. In this region, agriculture relies on the groundwater obtained from the Nubian Sandstone Aquifer System (NSAS). Recently, the groundwater level of this aquifer has been declining, posing a risk to the regional sustainability of the sector. Thus, it is necessary to understand the overall groundwater conditions in this region. Notably, the groundwater information about this aquifer is not publicly available, and its current condition remains unclear. Previous studies have only conducted NSAS assessments on a large spatial scale. Furthermore, no studies have observed land subsidence associated with groundwater use. Observing land subsidence could enable future assessments of the aquifer on a smaller spatial scale. This study is novel in that it observes land subsidence in oases using satellite data. The objective of this study is to promote future investigations of the NSAS by spatially observing land subsidence associated with the decrease in the groundwater level. The regional land subsidence was analyzed using interferometric synthetic radar (InSAR). The vertical surface deformation in the Kharga Oasis varied from −9.35 to 7.48 mm/year, and that in the Dakhla Oasis varied from −10.69 to 7.57 mm/year. The areas of significant subsidence were located within the agricultural land. The well-head uplift phenomena in the irrigation wells were observed in the study area. Thus, the subsidence in the region could be attributed to the large amount of groundwater extraction for irrigation. Significant uplift was also noted in the sand-dune regions and the outer edges of the vegetated area, likely attributed to the sand transport and deposition driven by northerly winds. This study presents a comprehensive analysis of the land subsidence in the Kharga and Dakhla oases and its association with the local decrease in the groundwater level. Consequently, this study successfully demonstrated the potential occurrence of land subsidence caused by groundwater extraction in the oases through the use of satellite-based observations. Notably, our method can be adapted to analyze similar areas worldwide.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101495"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in the evaluation of earth observation products: Accuracy assessment case study using convolutional neural networks","authors":"Thomas Prantl ,&nbsp;Til Barthel ,&nbsp;Dennis Kaiser ,&nbsp;Maximilian Schwinger ,&nbsp;André Bauer ,&nbsp;Samuel Kounev","doi":"10.1016/j.rsase.2024.101420","DOIUrl":"10.1016/j.rsase.2024.101420","url":null,"abstract":"<div><div>Earth observation is essential for monitoring natural resources and the Earth’s climate. However, the accuracy of earth observation products is sometimes not adequately evaluated, mainly when deep learning (DL) is used. One reason is that the DP and remote sensing communities have different ways of evaluating accuracy. The DL community tends to use single metrics to summarize results, which the remote sensing community overlooks. On the other hand, the remote sensing community emphasizes transparency in map creation, documenting sampling methods, and error matrices, which is not a priority for the DL community. Therefore, a significant challenge in applying DP methods for earth observation is the lack of evaluation using the established metrics of the remote sensing community, which are not commonly used in the DL community. In addition, assessing the accuracy of deep learning models adds another layer of complexity to the process. To tackle this challenge, we conducted a case study on creating a map of settlements in Bavaria using CNNs and satellite images. We then evaluated the resulting map according to the recommendations found in remote sensing literature. Our evaluation revealed that the CNNs we trained had an Overall Accuracy of over 97%. Since the remote sensing literature recommends not reporting only Overall Accuracy as a metric, especially for class imbalances, we also specified the confusion matrices with sample and proportion counts and the estimators for a 95% confidence interval as additional metrics and performed a visual evaluation. Our evaluation is based on our previous examination of recommended assessment practices. Our aim in presenting this overview, along with the case study, is to help readers identify potential issues and offer guidance for evaluating their earth observation products.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101420"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Displacement time series forecasting and anomaly detection based on EGMS-PSInSAR data towards effective bridge monitoring","authors":"M. Pięk ,&nbsp;K. Pawłuszek-Filipiak","doi":"10.1016/j.rsase.2024.101433","DOIUrl":"10.1016/j.rsase.2024.101433","url":null,"abstract":"<div><div>Monitoring bridges is essential due to their critical role in infrastructure networks. With its high temporal resolution, Differential Synthetic Aperture Radar Interferometry (DInSAR) emerges as promising remote sensing technique for this purpose. This study demonstrates the application of historical displacements time series provided by the European Ground Motion Service (EGMS) with daytime average temperature data for displacement time series forecasting and anomaly detection. This application was applied to 15 bridges in Wroclaw city, Poland covering 1441 points across two Sentinel-1 orbit geometries. Three forecasting models— Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short Term Memory (LSTM), and Prophet—were evaluated for their predictive performance. Since thermal expansion is common in bridges, an exogenous temperature variable was incorporated into each model, resulting in six predictive models. Root Mean Squared Error (RMSE) was used to assess prediction accuracy, with results showing that a displacement prediction accuracy on the level of 2 mmcan be achieved. LSTM and Prophet models performed the best, achieving RMSE values between 1.5 mm and 1.6 mm, outperforming SARIMA. Moreover, an approach for detecting anomalous displacement was proposed based on confidence intervals, using Student's t-distribution and standard deviation to establish a 90% confidence margin. This study highlights the benefits of combining DInSAR time series data with machine learning models for accurate displacement time series prediction and anomaly detection, contributing to more effective bridge monitoring and infrastructure management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101433"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping seasonal flood-recession cropland extent in the Senegal River Valley","authors":"Laurent Bruckmann ,&nbsp;Andrew Ogilvie ,&nbsp;Didier Martin ,&nbsp;Finda Bayo Diakhaté ,&nbsp;Amaury Tilmant","doi":"10.1016/j.rsase.2025.101473","DOIUrl":"10.1016/j.rsase.2025.101473","url":null,"abstract":"<div><div>Flood-recession agriculture (FRA) represents a crucial source of livelihood for numerous communities across Africa who reside near expansive floodplains and wetlands. However, it is currently insufficiently monitored. In this study, we present a methodology for mapping FRA harvested areas in the Senegal River Valley that is both reproducible and scalable. Our methodology entails the integration of optical and radar data from Sentinel platforms, conducted through a multitemporal analysis with a seasonal focus, and the application of the Random Forest algorithm. The results, supported by a kappa coefficient of 91.9%, demonstrate the first comprehensive mapping of FRA in the Senegal River valley, conducted between 2019 and 2023. This mapping facilitates the identification of the hydrological factors that influence FRA harvesting. The results of the analyses have demonstrated the importance of interannual variability in the cultivated areas of FRA, which range from 14,000 to 75,000 ha depending on the intensity of the annual flood. The duration and flooded extension are the primary factors that regulate the cropping pattern of FRA over the floodplain. The flood duration must be around 35 days to permit the cultivation, with growth generally starting between 10 and 30 November. In consideration of these findings, we recommend that future water management strategies and rural development initiatives give due consideration to FRA, to enhance the visibility of farmers.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101473"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influence of different building height and density data on local climate zone classification","authors":"Tianyue Ao ,&nbsp;Mengmeng Wang ,&nbsp;Renfeng Wang ,&nbsp;Zhengjia Zhang ,&nbsp;Wei Gao ,&nbsp;Xiuguo Liu","doi":"10.1016/j.rsase.2024.101429","DOIUrl":"10.1016/j.rsase.2024.101429","url":null,"abstract":"<div><div>The selection of classification features is significant for local climate zone (LCZ) classification using machine learning methods. Although the sequential utilization of various feature datasets for LCZ classification, the impact of different spatial datasets on the classification outcomes of LCZ remains unclear. This study systematically analyzes the impact of four building height datasets and two building density datasets, combined with spectral data, on LCZ classification using the random forest method. The comparative analyses are performed in three aspects: different building height data, different building density data, and various combinations of the two. The results show that various types and sources of spatial data have distinct impacts on improving the accuracy of LCZ classification. Generally, building density datasets prove more effective in enhancing LCZ classification compared to building height datasets. Among four building height datasets, the digital surface model (DSM) exhibits the most significant improvement in LCZ classification. Additionally, building density extracted from CNBH-10 m (BD1) demonstrates superior improvement in LCZ classification compared to that attained from roof vector data (BD2). Notably, the synergy of DSM and BD2 exhibits the most substantial enhancement, achieving an OA (Over Accuracy) of 89.60% and a Kappa coefficient of 87.70%. This combination of building height and density data simultaneously enhances the classification accuracy of both building area and natural surface types. The result of this study can not only enhance our understanding of the influences of spatial information data on the LCZ classification, but also provide a useful reference to improve LCZ classification.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101429"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An assessment of the spatiotemporal dynamics and seasonal trends in NO₂ concentrations across India using advanced statistical analysis","authors":"Aman Srivastava,&nbsp;Aditya Kumar Thakur,&nbsp;Rahul Dev Garg","doi":"10.1016/j.rsase.2025.101490","DOIUrl":"10.1016/j.rsase.2025.101490","url":null,"abstract":"<div><div>Increasing industrialization and urbanization pose significant risks to air quality and public health. It increases the necessity of a comprehensive understanding of air pollution dynamics. Further, prior research fails to explain detailed variability with levels of the seasonal trends of the pollutants over the years. This study aims to analyze the spatiotemporal variation of NO₂ concentrations across India from 2019 to 2023, focusing on understanding spatial continuity, seasonal trends, and dominant temporal patterns. The processed Sentinel-5P satellite data, which is spatially averaged over grids, was used in the study to enhance map accuracy. Various statistical parameters were used for spatiotemporal assessment. The consistency of NO₂ pollutants distribution over time was analyzed using Global Moran's Index. Further, moving average and cubic spline smoothing techniques were applied to assess the seasonality behaviour. Fast Fourier Transformation (FFT) was used to determine the dominant frequency cycles within the time series data of NO₂ pollutants. The result shows a significant increase in NO₂ levels from 0.7053∗10⁻⁴ mol/sq.m. in 2019 to 0.9634∗10⁻⁴ mol/sq.m. in 2023. Correspondence analysis showed consistent and shifting pollution patterns with spatial associations ranging from 0.6401 to 0.7072. The seasonal fluctuations in NO₂ concentrations represent peaks during pre-monsoon and fall in winter. Further, FFT analysis shows dominant seasonal patterns, a frequency of 0.0833, with an amplitude of 0.3209, depicting strong yearly variations. However, smaller semi-annual and quarterly fluctuations are also observed with amplitude of 0.1402 and 0.0530, respectively. Overall, this paper provides valuable insights into the spatiotemporal distribution of NO₂ pollutants that can be utilized in effective planning of air quality management and targeted pollution control strategies for mitigating the health impacts of pollution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101490"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}