Remote Sensing最新文献

{"title":"An Open and Transferable Deep Learning Framework for Mapping Urban Tree Canopy Using NAIP Imagery.","authors":"Jooyoung Yoo, Yi Qi, Isaac Ashe-McNalley, Beau MacDonald, John P Wilson","doi":"10.3390/rs18121899","DOIUrl":"10.3390/rs18121899","url":null,"abstract":"<p><p>The urban tree canopy is an important resource that spans public and private property and whose form and quantity change over short distances. Although remote sensing and deep learning approaches have been used to map urban tree canopy, the high cost of commercial imagery and the technical complexity of model development have limited their adoption by urban forestry practitioners. We developed a structured and reproducible deep learning workflow optimized for freely available USDA National Agriculture Imagery Program (NAIP) imagery. The workflow incorporates a reproducible U-Net segmentation model for canopy delineation and a YOLOv9e object detection model for individual tree identification, enabling complementary estimation of the canopy extent and individual tree locations. Across two neighborhoods in Los Angeles, the optimized U-Net achieved a Dice coefficient of 0.824 for canopy segmentation, while YOLOv9e reached an F1-score of 0.687 for individual tree detection on a held-out test set with 17,466 annotated trees. A data sufficiency experiment showed that model performance stabilizes when approximately 130 trees are annotated per 320 × 320 pixel (px) tile, corresponding to about 25,379 training and 2641 validation labels, providing a practical target for annotation effort. Additional experiments demonstrate a structured workflow for spatial sampling, training data requirements, and the use of model inferences to estimate tree canopy extent and individual tree locations. The workflow also shows encouraging evidence of transferability to previously unseen urban areas without retraining. By relying solely on NAIP-optimized approaches, this new workflow bridges the gap between complex deep learning techniques and the practical needs of urban foresters; empowers local stakeholders to create accurate, affordable, and timely urban tree inventories; and fosters data-driven decision-making for the sustainable management of urban green infrastructure.</p>","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"18 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13521618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148841888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Spatiotemporal Dynamics of Soil Moisture Under Water-Nitrogen Interactions in Arid Farmland Using UAV-Based Hyperspectral Sensing and Triple-Band Indices","authors":"Minghui Sun, Kaikai Su, Fei Tian","doi":"10.3390/rs18050726","DOIUrl":"https://doi.org/10.3390/rs18050726","url":null,"abstract":"In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (&gt;80 cm) (R2 = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"18 5","pages":"726-726"},"PeriodicalIF":0.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/18/5/726/pdf?version=1772270489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147901115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Satellite Observations of Inland and Coastal Waters: Building Towards a Global Validation Network.","authors":"Dulcinea M Avouris, Fernanda Maciel, Samantha L Sharp, Susanne E Craig, Arnold G Dekker, Courtney A Di Vittorio, John R Gardner, Emma Goldsmith, Juan I Gossn, Steven R Greb, Brice K Grunert, Daniela Gurlin, Mahesh Jampani, Rabia Munsaf Khan, Ben Lowin, Lachlan McKinna, Colleen B Mouw, Igor Ogashawara, Sara Rivero Calle, Wilson Salls, Joan-Albert Sánchez-Cabeza, Blake Schaeffer, Bridget N Seegers, Jari Silander, Emily A Smail, Menghua Wang, Jeremy Werdell","doi":"10.3390/rs17244008","DOIUrl":"10.3390/rs17244008","url":null,"abstract":"<p><p>The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development.</p>","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 24","pages":"4008"},"PeriodicalIF":4.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Autonomy for UAV Remote Sensing: A Survey of Video Prediction","authors":"Zhan Chen, Enze Zhu, Zile Guo, Peirong Zhang, Xiaoxuan Liu, Lei Wang, Yidan Zhang","doi":"10.3390/rs17203423","DOIUrl":"https://doi.org/10.3390/rs17203423","url":null,"abstract":"The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 20","pages":"3423-3423"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/17/20/3423/pdf?version=1760354024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147913671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Atmospheric Preprocessing Methods and Chlorophyll Algorithms for Sentinel-2 Imagery in Coastal Waters.","authors":"Tori Wolters, Naomi E Detenbeck, Steven Rego, Matthew Freeman","doi":"10.3390/rs17203503","DOIUrl":"10.3390/rs17203503","url":null,"abstract":"<p><p>Cyanobacterial blooms have been increasingly detected in estuaries and freshwater tidal rivers. To enhance detailed monitoring, an efficient approach to detecting algal blooms through remote sensing is needed to focus more detailed monitoring focused on cyanobacteria. In this study, we compared different remote sensing processing methods to determine an efficient approach to mapping chlorophyll-a. Using a subset of paired chlorophyll-a observations with Sentinel-2 imagery (2015-2022), with sites located in the Chesapeake Bay and Indian River selected along gradients of salinity, turbidity, and trophic status, we compared the combined performance of two different atmospheric processing methods (Acolite, Polymer) and a suite of empirical (band ratio, spectral shape indices) and machine learning algorithms for chlorophyll-a prediction. Acolite outperformed Polymer, resulting in 176 observation points, compared to 106 observation points from Polymer, and a greater range in chlorophyll-a values (0-74 μg/L from Acolite compared to 0-36 μg/L from Polymer), although Polymer showed more responsiveness at lower chlorophyll-a levels. Two algorithms performed best in predicting chlorophyll-a, as well as trophic state and HABs risk classes: the machine learning mixture density network (MDN) approach and the one band-ratio approach (Mishra).</p>","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 20","pages":"3503"},"PeriodicalIF":4.3,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12774331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145918746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution Daily Air Temperature for Tuscany, Italy.","authors":"Giorgio Limoncella, Denise Feurer, Dominic Roye, Kees de Hoogh, Arturo de la Cruz, Antonio Gasparrini, Rochelle Schneider, Francesco Pirotti, Dolores Catelan, Massimo Stafoggia, Francesca de'Donato, Giulio Biscardi, Chiara Marzi, Michela Baccini, Francesco Sera","doi":"10.3390/rs17173052","DOIUrl":"10.3390/rs17173052","url":null,"abstract":"<p><p>Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m <i>×</i> 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects.</p>","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 17","pages":"3052"},"PeriodicalIF":4.1,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7618111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Mechanisms of Hyperspectral Remote Sensing for Belowground Yield Traits in Potato Plants","authors":"Wenqian Chen, Yurong Huang, Wei Tan, Yujia Deng, Cuihong Yang, Xiguang Zhu, Jian Shen, Nanfeng Liu","doi":"10.3390/rs17122097","DOIUrl":"https://doi.org/10.3390/rs17122097","url":null,"abstract":"Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R2 = 0.58–0.84) than indirect approaches (R2 = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 12","pages":"2097-2097"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/17/12/2097/pdf?version=1750305717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147879393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing Long-Term Displacement and Evolution of Open-Pit Coal Mines Using SBAS-InSAR and DS-InSAR","authors":"Zechao Bai, Fuquan Zhao, Wang Jiqing, Jun Li, Yanping Wang, Yang Li, Yun Lin, Wenjie Shen","doi":"10.3390/rs17111821","DOIUrl":"https://doi.org/10.3390/rs17111821","url":null,"abstract":"Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 11","pages":"1821-1821"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/17/11/1821/pdf?version=1747993900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147895382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Severe Disturbance of Aurora on C-Band Sentinel-1 Interferogram at Mid-Latitudes: A Case Study During 11 May 2024","authors":"Yixun Zhu, Chao Xiong, Yifei Ji, Simin Wang, Fengjue Wang, Feixiang Tang, B. Sun","doi":"10.3390/rs17101687","DOIUrl":"https://doi.org/10.3390/rs17101687","url":null,"abstract":"Aurora is caused by the collision of high-energy particles with particles in the Earth’s atmosphere. Recent advances in synthetic aperture radar (SAR) have demonstrated significant potential for ionospheric inversion at various scales, offering new insights into auroral processes. In this study, we present the first reported perturbation of C-band SAR signals induced by auroral activity during the enormous geomagnetic storm of 11 May 2024. The auroral boundaries observed by the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) align closely with Sentinel-1 interferograms, despite the event occurring at mid-latitudes. A novel application of Sentinel-1 is illustrated, namely the inversion of the two-dimensional morphology and boundaries of the aurora at very high resolution and without interference from severe weather. Sentinel-1 promises to become a new method for the detection of precipitation particles, despite operating in the C-band, which is less disturbed by the ionosphere. Interferograms can also capture small-scale sporadic plasma patches associated with aurora. Furthermore, the analysis indicates that distinct polarization channels exhibit varying sensitivity towards auroral boundaries, with cross-polarization displaying heightened responsiveness.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 10","pages":"1687-1687"},"PeriodicalIF":0.0,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/17/10/1687/pdf?version=1746956142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147900530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting Post-Midnight Plasma Depletions Through Plasma Density and Electric Field Measurements in the Low-Latitude Ionosphere","authors":"Giulia D’Angelo, Emanuele Papini, Alessio Pignalberi, Dario Recchiuti, Piero Diego","doi":"10.3390/rs17091529","DOIUrl":"https://doi.org/10.3390/rs17091529","url":null,"abstract":"Plasma depletions in the low-latitude ionosphere are irregularities of special interest in space weather research, as they are highly detrimental to the operation of satellite-based communication and navigation systems. In this frame, we present the results of a systematic study of the low-latitude topside ionosphere, based on in situ measurements of both electron density (Ne) and electric field provided by the Langmuir Probe (LP) and the Electric Field Detector (EFD) onboard the first China Seismo-Electromagnetic Satellite (CSES-01). Specifically, by exploiting in situ measurements from 1 January 2019 to 31 May 2024, we devised two different techniques for the automatic detection of post-midnight plasma depletions at about 500 km of altitude: one using only Ne observations, the other using only electric field measurements. We validated these new techniques against each other and performed a statistical investigation of the main characteristics of the observed plasma irregularities, such as their latitudinal extension, longitudinal distribution, and monthly and seasonal occurrence. To test the robustness and reliability of our algorithms, we also applied them to well-established Swarm B satellite observations. In particular, we first investigated both the monthly and the seasonal occurrences of post-sunset plasma depletions detected between 18:00 and 04:00 local time (LT), by LP onboard the Swarm B satellite at about 500 km of altitude. In addition, we compared ionospheric irregularities detected by Swarm B with those detected by CSES-01. For the comparison, we considered Swarm B LP data collected for the same period as the CSES-01 dataset and under the same conditions by selecting Swarm B observations in the range 01:00 ≤LT&lt; 03:00. Our results prove the robustness and reliability of both LP and EFD algorithms in detecting plasma depletions, and their good agreement suggests their complementarity in detecting such kinds of plasma irregularities. Results also confirm consistency between CSES-01 and Swarm B observations (once the same LT orbits have been considered) and with the relevant literature on the topic.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 9","pages":"1529-1529"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2072-4292/17/9/1529/pdf?version=1745581933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147899820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}