Search Results (46)

Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay’s nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale “spectral reliability-turbidity stability” evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R²) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management. Full article

This study investigates the seasonal variability of water quality in the Yelek River Basin, Western Kazakhstan, using data from 2010 to 2025 that combine remote sensing, GIS, and hydrochemical monitoring data. This research addresses growing pressures on river systems from both natural and anthropogenic factors. Archival records from Kazhydromet and recent field measurements were analysed for dissolved oxygen, total suspended solids (TSSs), and total dissolved solids (TDSs), while satellite indices (NDWI, NDTI) provided spatiotemporal insights into turbidity. The results show clear seasonal contrasts: total suspended solids and turbidity rise sharply during spring floods due to snowmelt and erosion; water quality declines during summer–autumn low-flow periods under intensified human influence; and partial recovery occurs in winter when ice cover stabilises flow. Dissolved oxygen consistently indicates moderate pollution, while total dissolved solids (TDSs) remains within the “clean” range. Integration of satellite data with field observations enabled the development of a turbidity model and highlighted the lower river reaches as most vulnerable, where total suspended solids exceeded permissible limits. The findings confirm the value of combining remote sensing and GIS with traditional monitoring to capture long-term river water dynamics. This approach offers practical tools for sustainable water management, informs regional environmental policies, and provides transferable insights for semi-arid transboundary basins in Central Asia. Full article

Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article

This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow. Full article

Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R² values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R² values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R² of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R² of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R², RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R² values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC. Full article

Satellite remote sensing enables monitoring of regenerative agriculture practices, such as crop rotation, cover cropping, and conservation tillage to allow tracking and quantification at unprecedented scales. The Monitor system presented here capitalizes on the scope and scale of these data by integrating crop identification, cover cropping, and tillage intensity estimations annually at field scales across the contiguous United States (CONUS) from 2014 to 2023. The results provide the first ever mapping of these practices at this temporal fidelity and spatial scale, unlocking valuable insights for sustainable agricultural management. Monitor incorporates three datasets: CropID, a deep learning transformer model using Sentinel-2 and USDA Cropland Data Layer (CDL) data from 2018 to 2023 to predict annual crop types; the living root data, which use Normalized Difference Vegetation Index (NDVI) data to determine cover crop presence through regional parameterization; and residue cover (RC) data, which uses the Normalized Difference Tillage Index (NDTI) and crop residue cover (CRC) index to assess tillage intensity. The system calculates field-scale statistics and integrates these components to compile a comprehensive field management history. Results are validated with 35,184 ground-truth data points from 19 U.S. states, showing an overall accuracy of 80% for crop identification, 78% for cover crop detection, and 63% for tillage intensity. Also, comparisons with USDA NASS Ag Census data indicate that cover crop adoption rates were within 20% of estimates for 90% of states in 2017 and 81% in 2022, while for conventional tillage, 52% and 25% of states were within 20% of estimates, increasing to 75% and 67% for conservation tillage. Monitor provides a comprehensive view of regenerative practices by crop season for all of CONUS across a decade, supporting decision-making for sustainable agricultural management including associated outcomes such as reductions in emissions, long term yield resiliency, and supply chain stability. Full article

Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r² values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future. Full article

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources. Full article

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. Full article

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R² = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R² = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R² = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p < 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended. Full article

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R²) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R² = 0.85 and R² = 0.77, followed by STI, R² = 0.82 and R² = 0.80 and NDRI, R² = 0.69 and R² = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region. Full article

The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. In this work, we show as a proof-of-concept that muddy water mapping can be accomplished with machine learning-based semantic segmentation constituting an extra source of sediment-laden water information. Among others, such an approach can solve issues including (i) the presence/absence, frequency and spatial extent of pollutants (ii) generalization and expansion to unknown reservoirs (assuming a curated global dataset) (iii) indications about the presence of other pollutants since it acts as their proxy. Our train/test approach is based on 13 Sentinel-2 (S-2) scenes from inland/coastal waters around Europe while treating the data as tabular. Atmospheric corrections are applied and compared based on spectral signatures. Muddy water and non-muddy water samples are taken according to expert knowledge, S-2 scene classification layer, and a combination of normalized difference indices (NDTI and MNDWI) and are evaluated based on their spectral signature statistics. Finally, a Random Forest model is trained, fine-tuned and evaluated using standard classification metrics. The experiments have shown that muddy water can be detected with high enough discrimination capacity, opening the door to more advanced image-based machine learning techniques. Full article

In this study, we are reporting the impact of the incorporation of ferroelectric nanoparticles (FNPs), such as BaTiO₃ (BTO), BiFeO₃ (BFO), Bi₄NdTi₃Fe_0.7Ni_0.3O₁₅ (BNTFN), and Bi₄NdTi₃Fe_0.5Co_0.5O₁₅ (BNTFC), as well as the mass loading of sulfur to fabricated solvent-free sulfur/holey graphene-carbon black/polyvinylidene fluoride (S/FNPs/CBhG/PVDF) composite electrodes to achieve high areal capacity for lithium-sulfur (Li-S) batteries. The dry-press method was adopted to fabricate composite cathodes. The hG, a conductive and lightweight scaffold derived from graphene, served as a matrix to host sulfur and FNPs for the fabrication of solvent-free composites. Raman spectra confirmed the dominant hG framework for all the composites, with strong D, G, and 2D bands. The surface morphology of the fabricated cathode system showed a homogeneous distribution of FNPs throughout the composites, confirmed by the EDAX spectra. The observed Li⁺ ion diffusion coefficient for the composite cathode started at 2.17 × 10⁻¹⁶ cm²/s (S₂₅(CBhG)₆₅PVDF₁₀) and reached up to the highest value (4.15 × 10⁻¹⁵ cm²/s) for S₂₅BNTFC₅(CBhG)₆₀PVDF₁₀. The best discharge capacity values for the S₂₅(CBhG)₆₅PVDF₁₀ and S₂₅BNTFC₅(CBhG)₆₀PVDF₁₀ composites started at 1123 mAh/g_s and 1509 mAh/g_s and dropped to 612 mAh/g_s and 572 mAh/g_s, respectively, after 100 cycles; similar behavior was exhibited by the other composites that were among the best. These are better values than those previously reported in the literature. The incorporation of ferroelectric nanoparticles in the cathodes of Li-S batteries reduced the rapid formation of polysulfides due to their internal electric fields. The areal capacity for the S₂₅(CBhG)₆₅PVDF₁₀ composites was 4.84 mAh/cm² with a mass loading of 4.31 mg_s/cm², while that for the S₂₅BNTFC₅(CBhG)₆₀PVDF₁₀ composites was 6.74 mAh/cm² with a mass loading of 4.46 mg_s/cm². It was confirmed that effective FNP incorporation within the S cathode improves the cycling response and stability of cathodes, enabling the high performance of Li-S batteries. Full article