Remote Sensing Applications-Society and Environment最新文献

{"title":"Spatio-temporal variation and trend analysis of ground-level ozone in major Indian metropolitan cities: A geospatial approach","authors":"Roshan George Moncy,&nbsp;Aneesh Mathew,&nbsp;Padala Raja Shekar","doi":"10.1016/j.rsase.2024.101395","DOIUrl":"10.1016/j.rsase.2024.101395","url":null,"abstract":"<div><div>Air pollution refers to any chemical, physical, or biological contamination that contaminates an interior or outdoor environment and modifies the intrinsic qualities of the atmosphere. It can be produced by natural or anthropogenic activities. Among those pollutants mentioned by the World Health Organization (WHO), ground-level ozone, also known as tropospheric ozone, possesses a significant impact on human life. The current study was developed in response to the need to study ground-level ozone concentrations around India and metropolitan cities. The spatiotemporal variation across India was analyzed using geospatial methods. Using trend tests, trend analysis of the main metropolises in Bangalore, Chennai, Delhi, Hyderabad, Kolkata, and Mumbai was presented. 18 years of data (2005–2022) from the Ozone Monitoring Instrument (OMI) were used to conduct the test. According to geospatial research results, the northern region of India has a higher concentration of ozone than other locations. Delhi has a higher ozone rate than other metropolitan cities, ranging from 0.1219 to 0.1567 mol/m², followed by Kolkata (0.1085–0.1418 mol/m²). In these cities, summertime is often the time of year when the ground-level ozone concentration is at its maximum. Trend analysis using the Mann-Kendall and modified Mann-Kendall tests from 2005 to 2022 shows that the concentration increases with each year that goes by, even though there isn't a significant trend (p &lt; 0.05) across all of the monthly, seasonal, or annual periods. The research identifies high ozone areas and seasons, guiding policies, health advisories, urban planning, and accurate pollution forecasts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101395"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127839","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":"CCD-Conv1D: A deep learning based coherent change detection technique to monitor and forecast floods using Sentinel-1 images","authors":"Mohammed Siddique ,&nbsp;Tasneem Ahmed","doi":"10.1016/j.rsase.2024.101440","DOIUrl":"10.1016/j.rsase.2024.101440","url":null,"abstract":"<div><div>Floods are among the most common natural disasters affecting human lives and public amenities. In the North-Indian region, the situation is severe as floods continue to create havoc with flood fatalities and huge infrastructure damages every year. To mitigate this risk, flood monitoring based on detecting the changes in land cover and future predictions is required to be developed using Synthetic Aperture Radar (SAR) images. In this paper, a novel DL-based coherent change detection (CCD-Conv1D) model comprising a combination of coherent change detection technique, deep learning (DL) models based analysis, and flood forecasting implementation on the obtained change patterns, which pave the way to generate flood maps and identify the flooded areas has been developed. The proposed coherent change detection technique on Sentinel-1 images using image segmentation generated a log ratio image with statistics creating a changed band. An enhanced accuracy achieved in detecting changes from log-ratio-based temporal composition for Ayodhya and Basti cities shows positive threshold values of 2.96 and 2.01 during and after the crisis which is higher than 2.34 and 1.46 before and during the crisis respectively. The experimental outcomes demonstrated that the inundation concentrated mostly over the vegetation region of these cities. Additionally, the DL-based flood prediction performed through the Convolutional Neural Network (Conv1D) and Naïve Forecast (NF) model demonstrated that the positive changes for Ayodhya city were 31.4 and 31.8 and for Basti city were 30.40 and 35.04 respectively, depicting larger variation inferring that significant area is expected to be inundated. The outcomes from CCD-Conv1D based on the analysis of results, accuracy in change detection, and DL-based flood predictions confirmed that it is more reliable when compared with individual traditional approaches. In the future, more DL models can be explored for a wider insight and for comparative analysis of the outcomes from CCD-Conv1D implementation to develop an efficient flood monitoring and early warning system (FMEWS).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101440"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128308","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":"Estimation of transplanting and harvest dates of rice crops in the Philippines using Sentinel-1 data","authors":"Arturo G. Cauba ,&nbsp;Roshanak Darvishzadeh ,&nbsp;Michael Schlund ,&nbsp;Andrew Nelson ,&nbsp;Alice Laborte","doi":"10.1016/j.rsase.2024.101435","DOIUrl":"10.1016/j.rsase.2024.101435","url":null,"abstract":"<div><div>Rice is a staple crop in the Philippines, thus, identifying the ideal window to carry out crop management activities is valuable for efficient monitoring and resource allocation. This study used Sentinel-1A and 1B Synthetic Aperture Radar (SAR) data to estimate the transplanting and harvesting dates of paddy rice under dry and wet seasons and varying climatic conditions. A total of 99 rice fields in three provinces with distinct climatic patterns were considered in this study.</div><div>From Sentinel-1, we extracted the mean backscatter coefficients in VV, VH, and VH/VV polarizations for each field to generate time series curves with a temporal resolution of 6 days. To mitigate noise, locally weighted scatterplot smoothing (LOWESS) was applied. Periodogram analysis and the Breusch-Godfrey test were used to identify repetitive patterns and their statistical significance. Local extrema and corresponding dates suggest potential transplanting and harvesting dates. The identified dates were then compared with field data from farmer interviews. The root mean squared difference (RMSD) for transplanting ranged from 9 to 16 days and 14–29 days for dry and wet seasons, respectively. Harvest estimates followed similar trends with generally less scattered RMSD during the dry season (16–17.5 days) compared to the wet season values (8–22 days). Results show that VH and VV polarizations are promising for estimating transplanting and harvest dates during the dry season, whereas, VH/VV polarization were better during the wet season. The study emphasized the importance of SAR data for monitoring crop management strategies which are important for the agricultural sector.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101435"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128309","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":"Intra- and inter-annual spatiotemporal variations and climatic driving factors of surface water area in the Irtysh River Basin during 1985–2022","authors":"Enzhao Zhu ,&nbsp;Alim Samat ,&nbsp;Wenbo Li ,&nbsp;Ren Xu ,&nbsp;Junshi Xia ,&nbsp;Yinguo Qiu ,&nbsp;Jilili Abuduwaili","doi":"10.1016/j.rsase.2025.101455","DOIUrl":"10.1016/j.rsase.2025.101455","url":null,"abstract":"<div><div>Climate change and human activities have significantly altered the dynamics of surface water area (SWA) in the Irtysh River Basin (IRB). While inter-annual trends in SWA can be detected using Landsat imagery, the characteristics of seasonal SWA changes under long-term scenarios remain uncertain due to reduced data availability caused by cloud cover. In this study, we propose a time-disaggregated water frequency (TWF) that is more suitable for seasonal surface water analysis and develop a cloud-filling algorithm utilizing a Random Forest approach. The results demonstrate that the TWF effectively represents seasonal surface water distribution and achieves high cloud-filling accuracy. Using this method, we reconstructed monthly cloud-filled SWA series for the IRB from 1985 to 2022 at a spatial resolution of 30 m with high accuracy (&gt;94%). Analysis indicates that the multi-year average SWA of the IRB was 41,003 km², reflecting a decrease of 22%. The peak SWA occurs in spring (May), following the general trend of spring &gt; summer &gt; fall &gt; winter. Surface water loss primarily occurs during summer and fall, particularly in the middle reaches of the Irtysh River Basin (35%). Time-series correlation analysis reveals that snowmelt, precipitation, and temperature are the most significant climatic factors affecting SWA in spring, summer, and fall.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101455"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128312","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":"Tourism and environmental change in Saint Martin Island, Bangladesh: Insights from remote sensing data","authors":"Jayanta Biswas ,&nbsp;Tanmoy Malaker ,&nbsp;Taposh Mollick","doi":"10.1016/j.rsase.2025.101484","DOIUrl":"10.1016/j.rsase.2025.101484","url":null,"abstract":"<div><div>This study uses remote sensing data and geospatial analysis to evaluate the impact of unregulated tourism on the ecological vulnerability and land use dynamics of Saint Martin Island, Bangladesh. Saint Martin, the only coral-bearing island in Bangladesh, has experienced significant environmental degradation due to increased tourist activities, population growth, and tourism-induced development. In this study, multi-temporal Sentinel-2 imagery from 2018 to 2024 has been utilized for land use and land cover (LULC) classification to assess the impact of tourism on ecology, achieving an accuracy range of 94.56%–98.89%. Key environmental indices were calculated to assess vegetation cover, water quality, and climate patterns, along with land surface temperature (LST). The results showed a 2.52% increase in developed areas and a 12.77% decrease in sandy water between 2018 and 2024. Polluted water areas shrank from 2.16 acres in 2018 to 0.74 acres in 2020, reflecting ecological recovery due to reduced tourist activity during the COVID-19 lockdown period. However, pollution resurged to 0.97 acres by 2024 after restrictions were lifted. Coral reef degradation reached 25% between 2015 and 2022, severely impacting the island's marine biodiversity and future marine life. Additionally, a rise in land surface temperature (LST) from 32 °C in 2020 to 36 °C in 2024 was observed, along with a decrease in vegetation cover. The study demonstrates a clear link between unregulated tourism and environmental degradation, emphasizing the urgency of sustainable tourism practices such as limiting tourist visits, enhancing waste management, and protecting sensitive ecological areas to prevent further harm to the island's ecosystem and livelihoods.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101484"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376573","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":"Automated floating debris monitoring using optical satellite imagery and artificial intelligence: Recent trends, challenges and opportunities","authors":"Kamakhya Bansal,&nbsp;Ashish Kumar Tripathi","doi":"10.1016/j.rsase.2025.101475","DOIUrl":"10.1016/j.rsase.2025.101475","url":null,"abstract":"<div><div>Unwanted and harmful floating debris creates aesthetic, economic, social, and ecological harm. The optical satellites provide frequent global coverage across multiple spectral bands. Utilizing this abundant multi-banded optical satellite data for floating debris monitoring, many artificial intelligence-based approaches were proposed. These approaches face various challenges due to the multidimensional nature of the earth observation data visualized on a reduced scale. This work identifies various stages of AI deployment for floating debris identification, classification, segmentation, density estimation, and/or temporal study. The challenges during each stage along with some potential solutions applied in this field or elsewhere have been identified. Since AI approaches are data-driven, the limitation of labeled data with real-time diversity of shape, color, texture, size, and composition of floating debris placed against different backgrounds is most acute. The work proposes the utilization of some recent AI-based systems, like continuous learning, transfer learning, attention-based transformers, explainable AI, etc., to resolve these identified challenges. The work calls for further research into the application of pre-trained models, semi-supervised learning, and multi-modal data fusion for overcoming the labeled data deficiency. Additionally, harmful debris density estimation and factors leading to a change in the estimated density need further research.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101475"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143346716","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":"Exploring the link between spectra, inherent optical properties in the water column, and sea surface temperature and salinity","authors":"Solomon White ,&nbsp;Encarni Medina Lopez ,&nbsp;Tiago Silva ,&nbsp;Evangelos Spyrakos ,&nbsp;Adrien Martin ,&nbsp;Laurent Amoudry","doi":"10.1016/j.rsase.2025.101454","DOIUrl":"10.1016/j.rsase.2025.101454","url":null,"abstract":"<div><div>Sea surface salinity and temperature are important measures of ocean health. They provide information about ocean warming, atmospheric interactions, and acidification, with further effects on the global thermohaline circulation and as a consequence the global water cycle. In coastal waters they provide information about sub mesoscale circulations and tidal currents, riverine discharge and upwelling effects. This paper explores the methodology to extract sea surface salinity (SSS) and temperature (SST) from ground based hyperspectral ocean radiance. Water leaving radiance is linked to the inherent optical properties of the water column, effected by the constituent parts. Hyperspectral data at ground level is then used as input to train a linear regression model against temporally and spatially matched water data of SSS and SST. Furthermore, a neural network model to be able to estimate the SST and SSS with the hyperspectral data averaged to multispectral bands to emulate the satellite use case. The neural network model is able to learn the relationship between the multispectral radiance to both SSS and SST values, and can predict these with a root mean square error (RMSE) of 0.2PSU and 0.1 degree respectively. This demonstrates the feasibility of similar algorithms applied to multispectral ocean colour satellites with enhanced coverage and spatial resolution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101454"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091898","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":"Enhancing grassland cut detection using Sentinel-2 time series through integration of Sentinel-1 SAR and weather data","authors":"Aleksandar Dujakovic ,&nbsp;Cody Watzig ,&nbsp;Andreas Schaumberger ,&nbsp;Andreas Klingler ,&nbsp;Clement Atzberger ,&nbsp;Francesco Vuolo","doi":"10.1016/j.rsase.2025.101453","DOIUrl":"10.1016/j.rsase.2025.101453","url":null,"abstract":"<div><div>The detection of grassland cuts is relevant for modelling grassland yield and quality because information on cut dates and cut intensity aids in the modelling of the nutrient biomass ratio of fodder. This research improves an existing grassland cut detection methodology developed for Austria based on Sentinel-2 (S2) optical time series. To further improve the detection accuracy, the new method incorporates Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and daily weather data utilizing a machine learning-based model (Catboost). Cuts are first identified through a threshold-based comparison between a fitted idealized grassland growth curve and the observed NDVI values. The Catboost model subsequently addresses limitations in S2 data caused by cloud cover and other sub-optimum observation conditions. The Catboost model (1) identifies missing cuts in periods with no S2 data, and (2) eliminates false positive cuts. Weather data is utilized to identify the start of the cutting season and to define the (minimum required) time span between two consecutive cuts. Results demonstrate an improvement in cut date f-score (from 0.77 to 0.81), a reduced false detection rate (from 0.21 to 0.16), and a slight decrease in mean absolute error between true and estimated cut dates (from 4.6 to 4.1). The improvement in the accuracy was more evident for plots with high mowing frequency, while some remaining false detections were evident for extensively managed grasslands. The incorporation of S1 SAR and weather data enables the cut detection for the entire calendar year and eliminates the need for fixed growing season start/end dates. However, S1 SAR data alone did not provide reliable detection accuracy, showing its limitations in depicting vegetation dynamics for grassland. Overall, the improvements in accuracy and flexibility demonstrate the efficacy of the enhanced methodology, emphasizing the potential of combining S1 and S2 with weather data in large scale and cost-efficient grassland monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101453"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091978","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":"Evaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques","authors":"João Lucas Della-Silva ,&nbsp;Valeria de Oliveira Faleiro ,&nbsp;Tatiane Deoti Pelissari ,&nbsp;Amanda Ferreira ,&nbsp;Neurienny Ferreira Dias ,&nbsp;Daniel Henrique dos Santos ,&nbsp;Thaís Lourençoni ,&nbsp;Joelma Nayara ,&nbsp;Wendel Bueno Morinigo ,&nbsp;Larissa Pereira Ribeiro Teodoro ,&nbsp;Paulo Eduardo Teodoro ,&nbsp;Dthenifer Cordeiro Santana ,&nbsp;Izabela Cristina de Oliveira ,&nbsp;Ester Cristina Schwingel ,&nbsp;Renan de Almeida Silva ,&nbsp;Carlos Antonio da Silva Junior","doi":"10.1016/j.rsase.2025.101461","DOIUrl":"10.1016/j.rsase.2025.101461","url":null,"abstract":"<div><div>Soybeans (<em>Glycine max</em> (L.) Merrill) are a major player in food security, and pest loss control is a major focus of research and technological development by the agricultural sector. Among these pests, <em>Aphelenchoides besseyi</em> contaminates the aerial part of the plant, which can be detected in the leaf's spectral response, based on in situ hyperspectral sensors with the adoption of remote sensing techniques, such as spectral models. Assessing such data using machine learning allows the identification of optimal computational conditions to evaluate different levels of infection by the green stem nematode in soybeans. Thus, this research aimed to (i) discriminate the spectral bands most sensitive to nematode infection, (ii) identify the spectral model with the greatest accuracy for distinguishing different levels of nematode infection according to reflectance, and (iii) verify the resilience to the impact of <em>A. besseyi</em> on soybeans. From this approach, the near and short-wave infrared spectral portions contributed most to discriminating different amounts of nematodes in the plant, in a scenario in which the logistic regression algorithm had greater performance. Finally, this evaluation suggests that the best discrimination conditions occur with data obtained in the final half of the soybean cultivation cycle.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101461"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091979","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":"Effect of different deficit irrigation regimens on soil moisture, production parameters of mango (Mangifera indica L.), and spectral vegetation indices in the Mediterranean region of Southern Spain","authors":"J.A. Sillero-Medina ,&nbsp;J. González-Pérez ,&nbsp;P. Hueso-González ,&nbsp;J.J. González-Fernández ,&nbsp;J.I. Hormaza-Urroz ,&nbsp;J.D. Ruiz-Sinoga","doi":"10.1016/j.rsase.2024.101415","DOIUrl":"10.1016/j.rsase.2024.101415","url":null,"abstract":"<div><div>Mediterranean region is facing a severe water resource crisis, exacerbated by climate change. In recent decades, the region has experienced increased anthropogenic pressure due to population growth, tourism, and urban and agricultural expansion, intensifying competition for water among economic sectors. The agri-food sector is one of the most affected by water scarcity. This presents significant challenges for the sustainability of irrigated crops and underscores the need for efficient irrigation strategies and adaptive mechanisms. Among innovative strategies is deficit irrigation. In this context, to ensure effective water management, it is essential to constantly monitor soil moisture and adapt water conditions to the specific requirements of each crop. Precision agriculture, supported by technologies such as remote sensing and UAVs, plays a fundamental role in this context, enabling detailed crop monitoring and facilitating more efficient irrigation management. This study aims to evaluate the impact of using three different irrigation treatments on mango cultivation, a subtropical crop of growing importance in the Mediterranean region. Specifically, Treatment 1 is based on conventional surface drip irrigation without restrictions; Treatment 2 involves conventional surface drip irrigation with a 65% water reduction; and Treatment 3 uses deep subsurface drip irrigation (20 cm), with a similar water restriction as the previous treatment. The effect on mango cultivation has been evaluated based on: (i) soil moisture, (ii) production data collected during the 2022–2023 growing season on the experimental plot; and (iii) two vegetation indices (NDVI and NDRE) derived from multispectral data collected via two UAV flights at different phenological stages. The results indicate that surface drip irrigation has shown better outcomes in terms of production, yield, and crop quality compared to other treatments involving significant water reductions or subsurface irrigation. Deep deficit irrigation has obtained the worst results in the evaluation of plant production, and yield.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101415"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092295","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}