Remote Sensing Applications-Society and Environment最新文献

{"title":"Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India","authors":"Rajkumar Guria ,&nbsp;Manoranjan Mishra ,&nbsp;Richarde Marques da Silva ,&nbsp;Carlos Antonio Costa dos Santos ,&nbsp;Celso Augusto Guimarães Santos","doi":"10.1016/j.rsase.2024.101399","DOIUrl":"10.1016/j.rsase.2024.101399","url":null,"abstract":"<div><div>Recurrent droughts in India have severely impacted the economy and the quality of life. The agricultural drought from June to October 2023 in Odisha (the Kharif season), India, highlighted the urgent need for precise monitoring and assessment due to its significant effects on crop yield and food security. This study develops and validates the multisensor Integrated Drought Severity Index (IDSI) to accurately assess agricultural drought severity using multiple remote sensing indices, including optical, thermal, and microwave sensors. Ten indices were computed and combined using the Analytic Hierarchy Process (AHP) to assign weights, aiming to establish a new agricultural drought index that can monitor severity, identify critical indices, and assess uncertainties in affected areas. Validation results from ROC-AUC indicate that the IDSI model achieved a precision exceeding 85% using empirical weights. The study area's mapping shows that approximately 8.91% experience extreme drought conditions, with significant impacts in specific districts of Odisha. This comprehensive tool provides critical insights for policymakers and farmers, enhancing global drought preparedness and response strategies through its adaptable methodology.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101399"},"PeriodicalIF":3.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703689","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 coastal wetland changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat time series and national wetland inventories","authors":"Courtney A. Di Vittorio ,&nbsp;Melita Wiles ,&nbsp;Yasin W. Rabby ,&nbsp;Saeed Movahedi ,&nbsp;Jacob Louie ,&nbsp;Lily Hezrony ,&nbsp;Esteban Coyoy Cifuentes ,&nbsp;Wes Hinchman ,&nbsp;Alex Schluter","doi":"10.1016/j.rsase.2024.101392","DOIUrl":"10.1016/j.rsase.2024.101392","url":null,"abstract":"<div><div>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101392"},"PeriodicalIF":3.8,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652884","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":"Assessment of Dry Microburst Index over India derived from INSAT-3DR satellite","authors":"Priyanshu Gupta,&nbsp;Neeti Singh,&nbsp;R.K. Giri,&nbsp;A.K. Mitra","doi":"10.1016/j.rsase.2024.101393","DOIUrl":"10.1016/j.rsase.2024.101393","url":null,"abstract":"<div><div>Dry microbursts can generate severe meteorological conditions including turbulence and strong winds even in the absence of precipitation. Present study evaluate the performance of Indian geostationary satellite, INSAT-3DR in capturing Dry Microburst Index (DMI) and validated against the radiosonde dataset. Data is validated across 14 selected stations across the India for 3 year (2020–2022). However, radiosonde data is very limited but spatial and temporal resolution of INSAT-3DR is good to analyse and predict the atmospheric phenomena. Different statistics have been used to validate INSAT-3DR against radiosonde observation. A Taylor plot confirm strong correlation and low RMSE between INSAT-3DR and radiosonde data. Spatial distribution depicts annual mean DMI values, it is influence by diurnal variation, regional weather pattern, and seasonal factors. Seasonal analysis indicates lower DMI during winter (5–45) due to reduced instability and moisture, while post-monsoon season witness increased DMI owing to warmer, humid conditions. The pre-monsoon season shows rising DMI as temperature increase. Study also analyses the co-occurrence of thunderstorm during DMI events, revealing a Probability of Detection (POD) of 0.75 for the INSAT-3DR DMI product, indicating 75% correct identification of thunderstorms. However, the False Alarm Rate (FAR) suggest false alarms occurred in approximately 55.2% of cases. Overall, study underscores the importance of considering local factors and conditions in interpreting INSAT-3DR satellite-based DMI data. Understanding and accurately predicting dry microbursts are crucial for enhancing aviation safety and improving the resilience of infrastructure in regions prone to these phenomena.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101393"},"PeriodicalIF":3.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652920","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 radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia","authors":"Dini Andriani ,&nbsp;Supriyadi ,&nbsp;Muhammad Aufaristama ,&nbsp;Asep Saepuloh ,&nbsp;Alamta Singarimbun ,&nbsp;Wahyu Srigutomo","doi":"10.1016/j.rsase.2024.101376","DOIUrl":"10.1016/j.rsase.2024.101376","url":null,"abstract":"<div><div>This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m² and 115.13 W/m², indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101376"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663074","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":"Effective cooling networks: Optimizing corridors for Urban Heat Island mitigation","authors":"Teimour Rezaei,&nbsp;Xinyuan Shen,&nbsp;Rattanawat Chaiyarat,&nbsp;Nathsuda Pumijumnong","doi":"10.1016/j.rsase.2024.101372","DOIUrl":"10.1016/j.rsase.2024.101372","url":null,"abstract":"<div><div>The detrimental impacts of the Urban Heat Island (UHI) effect are widely recognized in cities globally. Despite the natural cooling capacity of urban cold islands (UCIs), their fragmented state diminishes overall effectiveness. Previous research focused on identifying corridors to connect these isolated UCIs, aiming to enhance cooling networks. However, optimal connection strategies remained elusive. This study introduces a novel framework to address this gap. Utilizing ArcGIS Pro's optimal region connection tools alongside Morphological Spatial Pattern Analysis (MSPA) and ecological parameters, corridors in Ghaemshahr, Iran were meticulously planned and assessed. Through minimum cumulative resistance and gravity models, 63 potential corridors totaling 153 km were identified. Optimization procedures then refined this selection to 27 key corridors spanning 22 km, with 67% measuring less than 0.5 km and strategically positioned near UCIs. This prioritizes adjacency, maximizing corridor protection and construction likelihood. This cost-effective approach fosters stronger connectivity between adjacent UCIs, ultimately linking all UCIs within the region. This innovative methodology provides a holistic solution for mitigating UHI effects, promoting sustainable urban development.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101372"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663076","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":"Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data","authors":"Samvedya Surampudi,&nbsp;Vijay Kumar","doi":"10.1016/j.rsase.2024.101361","DOIUrl":"10.1016/j.rsase.2024.101361","url":null,"abstract":"<div><div>Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101361"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663075","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":"Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region","authors":"Sona Alyounis ,&nbsp;Delal E. Al Momani ,&nbsp;Fahim Abdul Gafoor ,&nbsp;Zaineb AlAnsari ,&nbsp;Hamed Al Hashemi ,&nbsp;Maryam R. AlShehhi","doi":"10.1016/j.rsase.2024.101374","DOIUrl":"10.1016/j.rsase.2024.101374","url":null,"abstract":"<div><div>This research applies machine learning to predict soil coherence for Etihad Rail, marking the first comprehensive study in the United Arab Emirates (UAE)'s arid regions. By integrating Sentinel-1 SAR and Sentinel-2 data with MODIS Aerosol Optical Depth (AOD) observations, the study develops detailed models that depict complex soil coherence patterns crucial for urban planning and risk assessment. Findings show variations in soil coherence between operational and under-construction phases, influenced by seasonal changes in aerosol dynamics and sand dust levels. Higher soil coherence is linked with lower annual sand dust deposition and AOD measurements, emphasizing the importance of this data for informed decision-making. The study employs a unique combination of data sources and machine learning algorithms to predict soil coherence, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Gaussian Process Regression (GPR), Random Forest (RF), and 1D Convolutional Neural Network (CNN), with the Random Forest model achieving the lowest root mean squared error (RMSE) of 0.0826. These contributions enhance our understanding and provide a valuable framework for infrastructure development in similar environments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101374"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663073","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":"Recent trends in moisture conditions across European peatlands","authors":"Laura Giese ,&nbsp;Maiken Baumberger ,&nbsp;Marvin Ludwig ,&nbsp;Henning Schneidereit ,&nbsp;Emilio Sánchez ,&nbsp;Bjorn J.M. Robroek ,&nbsp;Mariusz Lamentowicz ,&nbsp;Jan R.K. Lehmann ,&nbsp;Norbert Hölzel ,&nbsp;Klaus-Holger Knorr ,&nbsp;Hanna Meyer","doi":"10.1016/j.rsase.2024.101385","DOIUrl":"10.1016/j.rsase.2024.101385","url":null,"abstract":"<div><div>Peatlands play a key role in climate change mitigation strategies and provide multiple ecosystem services, presuming near natural, waterlogged conditions. However, there is a lack of knowledge on how spatially heterogeneous changes in climate across Europe, such as the predicted increase in drought frequency in Central Europe, might affect these ecosystem services and peatland functioning. While analysis of peat cores and moisture sensors provide high-quality insights into past or present hydrological conditions, this information is usually only available for a limited number of locations. Satellite remote sensing is an effective method to overcome this limitation, providing spatially continuous and temporally highly resolved environmental information.</div><div>This study proposes to use freely available data from the Landsat Mission to analyze trends in proxies of surface moisture of European peatlands over the last four decades. Based on a large random sample of peatland sites across Europe, we performed a pixel-wise trend analysis on monthly time-series dating back to 1984 using the Normalized Difference Water Index as a moisture indicator.</div><div>The satellite-derived moisture changes indicated a pronounced shift towards wetter conditions in the boreal and oceanic region of Europe, whereas in the temperate, continental region, a high proportion of peatlands experienced drying. Small-scale patterns of selected sites revealed a high spatial heterogeneity, the complexity of hydro-ecological interactions, and locally important environmental and anthropogenic drivers affecting the moisture signal. Overall, our results support the expected effects of current climate trends of increasing precipitation in boreal northern and oceanic north-western Europe and increasing frequency of drought in continental Europe.</div><div>Our fully reproducible approach provided new insights on continental and local scales, relevant not only to a better understanding of moisture trends in general, but also to practitioners and stakeholders in ecosystem management. It may thus contribute to developing a cost-effective long-term monitoring strategy for European peatlands.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101385"},"PeriodicalIF":3.8,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652885","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":"Uncovering true significant trends in global greening","authors":"Oliver Gutiérrez-Hernández ,&nbsp;Luis V. García","doi":"10.1016/j.rsase.2024.101377","DOIUrl":"10.1016/j.rsase.2024.101377","url":null,"abstract":"<div><div>The global greening trend, marked by significant increases in vegetation cover across ecoregions, has attracted widespread attention. However, even robust traditional methods, like the non-parametric Mann-Kendall test, often overlook crucial factors such as serial correlation, spatial autocorrelation, and multiple testing, particularly in spatially gridded data. This oversight can lead to inflated significance of detected spatiotemporal trends. To address these limitations, this research introduces the True Significant Trends (TST) workflow, which enhances the conventional approach by incorporating pre-whitening to control for serial correlation, Theil-Sen (TS) slope for robust trend estimation, the Contextual Mann-Kendall (CMK) test to account for spatial and cross-correlation, and the adaptive False Discovery Rate (FDR) correction. Using AVHRR NDVI data over 42 years (1982–2023), we found that conventional workflow identified up to 50.96% of the Earth's terrestrial land surface as experiencing statistically significant vegetation trends. In contrast, the TST workflow reduced this to 38.16%, effectively filtering out spurious trends and providing a more accurate assessment. Among these significant trends identified using the TST workflow, 76.07% indicated greening, while 23.93% indicated browning. Notably, considering areas (pixels) with NDVI values above 0.15, greening accounted for 85.43% of the significant trends, with browning making up the remaining 14.57%. These findings strongly validate the ongoing global greening of vegetation. They also suggest that incorporating more robust analytical methods, such as the True Significant Trends (TST) approach, could significantly improve the accuracy and reliability of spatiotemporal trend analyses.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101377"},"PeriodicalIF":3.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652921","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":"Classification of soil horizons based on VisNIR and SWIR hyperespectral images and machine learning models","authors":"Karym Mayara de Oliveira ,&nbsp;João Vitor Ferreira Gonçalves ,&nbsp;Renan Falcioni ,&nbsp;Caio Almeida de Oliveira ,&nbsp;Daiane de Fatima da Silva Haubert ,&nbsp;Weslei Augusto Mendonça ,&nbsp;Luís Guilherme Teixeira Crusiol ,&nbsp;Roney Berti de Oliveira ,&nbsp;Amanda Silveira Reis ,&nbsp;Everson Cezar ,&nbsp;Marcos Rafael Nanni","doi":"10.1016/j.rsase.2024.101362","DOIUrl":"10.1016/j.rsase.2024.101362","url":null,"abstract":"<div><div>The use of spectral signature to classify soil horizons and orders is becoming increasingly popular in the field of geotechnology. With the introduction of precise sensors and robust models for obtain data and classifying attributes, the traditional surveys can be improved with a computational analytical approach. Despite the benefits, few authors have addressed the classification of soil horizons given the budget and time-consuming required to obtain and analyze data. This study aimed to assess the efficiency of combining soil spectral reflectance (obtained by two hyperspectral imaging sensors) with robust ML (machine learning) models for classifying soil horizons. Six monoliths were collected from soil profiles located in the central northern region of Parana State, Brazil. The monoliths were scanned by VIS-NIR and SWIR hyperspectral cameras in the laboratory. Spectral signatures were obtained and explored by principal component analysis (PCA). The spectral data were subdivided into training (70%) and test (30%) sets and subjected to the random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) methods for the classification of soil horizons. The overall accuracy, F1-score, and confusion matrix were used to verify the performance of the models. There was a significant influence of particle size and soil organic carbon on the spectral signature of the soils. Despite the data overlap between adjacent horizons observed in the PCA, the machine learning models were able to classify the horizons with promising accuracy and PCA explained the dataset with a percentage above 98%. For VIS-NIR spectra, the accuracies varied between 81.4% (KNN) and 89.9% (RF), and the F1-scores varied between 51.9% (SVM) and 78.3% (RF). For the SWIR spectra, the variation in accuracy was between 72.1% (SVM) and 86.5% (RF), but the variation in the F1-score was between 61.9% (SVM) and 85.4% (RF). These results demonstrate the promising potential of using hyperspectral imaging and machine learning models combined with traditional soil classification methods as tools.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101362"},"PeriodicalIF":3.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357031","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}