Selection of Landsat 8 OLI Levels, Monthly Phases, and Spectral Variables on Identifying Soil Salinity: A Study in the Yellow River Delta

Applied Sciences, Год журнала: 2025, Номер 15(5), С. 2747 - 2747

Опубликована: Март 4, 2025

Soil salinization is a significant threat to agricultural production, making accurate salinity prediction essential. This study addresses key challenges in the Yellow River Delta (YRD) soil inversion, including (1) determining which Landsat 8 OLI level performs better, (2) identifying most suitable month for and (3) improving model performance important variables modeling. Thus images (Level-1 Level-2) 12 months were collected, then having less than 10% cloud cover selected processed extract spectral values. A total of 86 sampled points measure salinity. Using Pearson correlation expert insights, January 15 August 26 identified as dates inversion. Then, seven original bands, 29 indicators, 39 derived created through six mathematical transformations, used construct following three models: partial least squares regression (PLSR), random forest (RF), backpropagation neural network (BPNN). The results showed following: Level-1 data, after FLAASH atmospheric correction, outperforms Level-2 data. optimal Among models, RF outperformed others, achieving test set R2 = 0.55, RMSE 3.4, suggesting that combination indicators mathematically transformed can effectively enhance accuracy predicting YRD. Furthermore, SWIR1, SWIR2, CLEX, second-order difference first-order SWIR2 along with NIR played role

Язык: Английский

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A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality

The Science of The Total Environment, Год журнала: 2024, Номер 957, С. 177180 - 177180

Опубликована: Ноя. 23, 2024

Язык: Английский

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An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103001 - 103001

Опубликована: Янв. 1, 2025

Язык: Английский

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Improving remote sensing dehazing quality through local hybrid correction and optimization of atmospheric attenuation model based on wavelength

Frontiers in Remote Sensing, Год журнала: 2025, Номер 5

Опубликована: Янв. 30, 2025

Near-ground remote sensing image dehazing is crucial for accurately monitoring land resources. An effective technique and a precise atmospheric attenuation model are fundamental to acquiring real-time ground data with high fidelity. The dark channel prior (DCP) widely used method improving visibility in hazy conditions, but it often results reduced clarity artifacts, that limit its practical utility. To address these limitations, we propose novel hybrid correction method, local (LHC), which integrates gamma high-contrast regions logarithmic low-contrast within dehazed image. We calculated the cumulative distribution function (CDF) of Weber contrast analyzed impact different thresholds on effectiveness reducing artifacts. Our showed threshold corresponding 90% CDF significantly improved sharpness artifacts compared other thresholds. Furthermore, LHC outperformed both corrections terms artifact reduction, even after applying additional post-processing methods such as multi-exposure fusion guided filtering. quantitative analysis images, using gray-level co-occurrence matrix (GLCM) metrics, indicated offered balanced advantage enhancing details, texture consistency, structural complexity. Specifically, images processed by exhibit moderate correlation, low homogeneity entropy, all made very suitable solution near-ground tasks required enhanced detail also examined coefficient, observing increased distance, deviating progressively from empirical values, this phenomenon underscored complex effects scattering accuracy, especially at extended ranges. Additionally, refined transmittance light reflection 550 nm wavelength verdant landscapes, model’s alignment real-world conditions. This approach was not only could adapt wavelengths future studies. Overall, our research advanced precision techniques, promising decision-making resource management variety environmental applications.

Язык: Английский

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Trends in remote sensing of water quality parameters in inland water bodies: a systematic review

Frontiers in Environmental Science, Год журнала: 2025, Номер 13

Опубликована: Март 3, 2025

Monitoring water quality is crucial for sustainable management and meeting the United Nations Sustainable Development Goals. Urbanisation, agricultural practices, industrial activities, population growth increase presence of biological, chemical physical properties in bodies. Traditional monitoring methods (laboratory situ measurements) are limited spatially, temporarily costly. Satellite remote sensing has been shown to provide a systematic, cost-effective, near-real-time alternative. This paper analysed 142 peer-reviewed articles published between 2002 2024 from Web Science Scopus databases. The final included review were achieved through PRISMA flowchart. revealed that low-resolution sensors with long-term records, such as MODIS, commonly applied study large lakes. In contrast, Landsat-8 Sentinel-2 both lakes dams. These contain necessary spectral regions quality, where it was 500–600 nm region critical chlorophyll assessment, while 640–670 used turbidity. Secchi disk depth total suspended solids assessed using 860–1040 1570–1650 nm. Water research also focused on countries China, India, Brazil, South Africa, an emphasis optically active parameters. There is, however, non-optically parameters, nitrogen, phosphorus, temperature, especially small inland Therefore, there need more these areas, direct indirect parameter estimation integration machine learning algorithms.

Язык: Английский

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Current Capabilities and Challenges of Remote Sensing in Monitoring Freshwater Cyanobacterial Blooms: A Scoping Review

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 918 - 918

Опубликована: Март 5, 2025

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates current capabilities challenges RS for bloom monitoring, with a focus achievable accuracy. We find that chlorophyll-a (Chl-a) phycocyanin (PC) are primary indicators used, PC demonstrating greater stability than Chl-a. Sentinel Landsat satellites most frequently data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high recent years. In contrast, Medium-Resolution Imaging Spectrometer (MERIS) Moderate-Resolution Spectroradiometer (MODIS) exhibited lower performance. The choice methods is also essential accuracy, regression machine learning models generally outperforming other approaches. Temporal analysis indicates notable improvement 2021 2023, reflecting advances technology techniques. Additionally, findings suggest combined approach using Chl-a large-scale preliminary screening, followed by more precise detection, can enhance effectiveness. integrated strategy, along careful selection sources models, crucial improving reliability ultimately contributing better management public health protection.

Язык: Английский

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Retrieving Inland Water Quality Parameters via Satellite Remote Sensing: Sensor Evaluation, Atmospheric Correction, and Machine Learning Approaches

Remote Sensing, Год журнала: 2025, Номер 17(10), С. 1734 - 1734

Опубликована: Май 15, 2025

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters inland waters. Effective parameter retrieval via optical satellite requires three key components: (1) sensor whose measurements are sensitive variations in quality; (2) accurate atmospheric correction eliminate the effect of absorption scattering atmosphere retrieve water-leaving radiance/reflectance; (3) bio-optical model used estimate from signal. This study literature review an evaluation these components. First, decommissioned, active, upcoming sensors is presented, highlighting their advantages limitations, ranking method introduced assess suitability chlorophyll-a, colored dissolved organic matter, non-algal particles can aid selecting appropriate future studies. Second, strengths weaknesses algorithms over waters examined. The results show that no algorithm performed consistently across all conditions. However, understanding allows users select most suitable specific use case. Third, challenges, recent advances machine learning models discussed. Machine have including low generalizability, dimensionality, spatial/temporal autocorrelation, information leakage. These issues highlight importance locally trained models, rigorous cross-validation methods, integrating auxiliary data enhance dimensionality. Finally, recommendations promising research directions provided.

Язык: Английский

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Optimum Data Scale for Remote Sensing Vegetation Cover Mapping Using the Multi-criteria Decision Analysis Method

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Июнь 2, 2025

Язык: Английский

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Evaluation of atmospheric correction algorithms for salt lake water assessment: Accuracy, band-specific effects, and sensor consistency

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0315837 - e0315837

Опубликована: Дек. 23, 2024

Atmospheric correction plays an important role in satellite monitoring of lake water quality. However, different atmospheric algorithms yield significantly accuracy for inland waters beset by shallowness and turbidity. Finding a suitable algorithm specific is critical quantitative water-environmental monitoring. This study used Landsat 8 Sentinel 2 L1 level data Ebinur Lake arid northwest China on May 19, 2021. corrections were performed using FLAASH, QUAC, 6S, Acolite-DSF Acolite-EXP algorithms. The reflectance product verified the consistency Quasi-simultaneously measured hyperspectral determined applicable to waters. results indicate that has good high images. Extracting images found relative error at 0.3 Blue, Green, Red bands 0.5 NIR band. For comparison, errors all are 0.3. Therefore, these four recommended temporal parameters Lake. Besides identifying Lake, this analyzed common wavebands remote sensing bodies, especially salt lakes regions.

Язык: Английский

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Research on the Protection and Dissemination of Cultural Heritage in Rural Landscapes Based on Image Processing

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract The countryside is an important part of the social development process, but with acceleration urbanization, protection rural landscapes as cultural heritage facing increasingly severe situation. In this study, image radiation correction, fusion, cropping and mosaicing, geometric band selection, enhancement are applied to using remote sensing processing technology. A digital system for landscape created processed landscapes. By comparing accuracy paper’s method other classification methods, we can explore performance PCA method. changes in types before after protection, effect explored. Finally, communication on media explored by utilizing evaluation index system. employed paper achieves a 83%, which significantly superior IHS transformation (73.5%) Brovey (76%). After degree fragmentation Village was improved compared remarkable. scores users each dimension were greater than 4, achieved positive effect.

Язык: Английский

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