Search Results (957)

Black locust (Robinia pseudoacacia L.) breeding is an important component of plantation forestry in Central and Eastern Europe; however, clone trials are still mainly evaluated using conventional field surveys, and the application of high-resolution red-edge satellite indices at the intraspecific level remains rarely applied. As a result, less information is available on the phenological status of black locust clones derived from red-edge satellite data. This study evaluates a clone trial established in Eastern Hungary on slightly acidic Arenosol soil, assessing the growth performance and seasonal spectral dynamics of newly bred black locust clones during their fifth growing season by integrating field measurements with PlanetScope-derived Normalized Difference Red-Edge Index (NDRE) time series. Clone NK2 exhibited the most vigorous growth, reaching a mean height of 11.1 ± 0.15 m and a diameter at breast height (DBH) of 11.21 ± 0.19 cm, which were 35.4% greater in height and 19.0% larger in DBH than those (8.2 ± 0.12 m height, and 9.42 ± 0.23 cm diameter) of the control (‘Üllői’ cultivar). Clone PL251 also exceeded the control by 25.6% in height and 19.2% in DBH. Spectral analysis (NDRE value ± standard error) revealed marked differences in phenological development: in the early stage (April 15), NK1 and PL040 had the highest NDRE values (0.472 ± 0.020 and 0.461 ± 0.019), whereas NK2 showed delayed leaf emergence (0.398 ± 0.019). By June 21, PL251 had reached an NDRE value of 0.692 ± 0.013, which was higher than that of the control (0.673 ± 0.016). In mid-July, NDRE peaked for NK2 and NK1 (0.732 ± 0.012 and 0.731 ± 0.013), with ‘Üllői’ showing consistently lower values across the season. In the final stage, NK2 maintained the highest NDRE values (October 22: 0.618 ± 0.015; November 9: 0.466 ± 0.021), indicating prolonged photosynthetic activity, while NK1 and ‘Üllői’ declined earlier (e.g., November 9: 0.354 ± 0.018 and 0.390 ± 0.027, respectively). These findings highlight NK2 and PL251 as superior candidates for high-yield, climate-resilient tree plantations because of their strong growth and extended physiological activity. Full article

Accurate, nondestructive assessment of fresh tea leaf quality is important for breeding and field management, yet most spectral work still targets processed or low-moisture products. Here, a mechanistically guided hyperspectral method was developed to estimate free amino acids (AA) and total polyphenols (TP) in fresh leaves. Spectral experiments on purified AA and TP powders and their water mixtures identified a key spectral window at 1660 nm. Fractional-order derivatives were applied to leaf reflectance spectra from 102 spring samples (53 varieties), and full-spectrum Partial Least Squares Regression (PLSR) models were used as comparison and validated on an independent set of 40 summer samples. PLSR achieved decent cross-validation coefficient of determination accuracy for AA ($R_{\mathrm{c}\mathrm{v}}^2=0.867$) and TP ($R_{\mathrm{c}\mathrm{v}}^2=0.755$) and good external prediction coefficient of determination accuracy ($R_{\mathrm{P}}^2=0.793$ and $0.776$, respectively). Guided by the powder and leaf-level analysis, two-band NDSI indices were derived: the AA index of 1735/1626 nm (R²ₚ = 0.687, RPDₚ = 1.788) and the TP index of 1673/1660 nm (R²ₚ = 0.785, RPDₚ = 2.157) approached the PLSR, indicating that much of the useful information for AA and TP is concentrated in this narrow window and can be captured by simple, interpretable indices potentially suitable for in-field sensing, pending validation across multiple sites, seasons, and management conditions. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified. Full article

Detecting nearshore underwater targets in hyperspectral imagery faces significant challenges due to complex background clutter, weak and distorted underwater target signals. Extracting discriminative features is a critical step. Current methods are often constrained by high spectral redundancy and reliance on manual annotations, leading to suboptimal detection performance. To address these problems, this paper proposes a novel underwater target detection framework that integrates self-supervised band selection with a physically-constrained detection, called the negatively constrained network with self-supervised band selection (NCSS-Net). Specifically, NCSS-Net first generates a target-prior abundance map via Normalized Difference Water Index and spectral unmixing. This abundance map is then converted into a binary target mask through adaptive thresholding. The binary target mask serves as pseudo labels and guides an Artificial Bee Colony algorithm to identify a maximally discriminative band subset. These bands are then fed into a negatively-constrained autoencoder. This network is trained with a specialized loss function to enforce negative correlation between the target and water endmembers, thereby enhancing their separability. Experimental results demonstrate that NCSS-Net outperforms existing state-of-the-art methods, offering an effective and practical solution for nearshore underwater monitoring applications. Our code will be available online upon acceptance. Full article

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

Infestations of Dendrolimus punctatus Walker (D. punctatus) pose significant threats to forest ecosystem health, necessitating accurate and efficient monitoring for sustainable forest management. A monthly monitoring framework integrating spectral bands, vegetation indices, time-series features, meteorological variables, and topographic characteristics was developed. First, cloud-free Sentinel-2 composites were generated via median synthesis, and training samples were selected by integrating GF-1/2 data. Subsequently, a Weighted Composite Index (WCI) was constructed through logistic regression to quantitatively classify infestation severity levels. Meanwhile, time-series features extracted from vegetation indices were incorporated to characterize temporal damage dynamics. Finally, Random Forest (RF) models were then trained for monthly monitoring, achieving overall accuracies exceeding 86.9% with Kappa coefficients ranging from 0.825 to 0.858. The Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) exhibited the highest sensitivity to D. punctatus damage and thus received the greatest weights in the WCI. Time-series features ranked second in importance after vegetation indices, substantially enhancing model performance. Monitoring results from 2019 to 2024 revealed that D. punctatus infestation in Qianshan City exhibited an occurrence pattern progressing from mild to severe and from scattered to aggregated distributions, with major outbreak periods in 2019, 2021, and 2023 reflecting characteristic cyclical dynamics. This study advances existing quantitative monitoring methodologies for D. punctatus and provides technical support and a scientific foundation for precision pest monitoring and forest health management. Full article

Efficient and reliable estimation of rice phenological stages is crucial for improving yield prediction, optimizing irrigation, and guiding fertilization management. Spectral indices (SIs) derived from remote sensing have demonstrated strong potential for phenology detection. However, the suitability of specific spectral indices (SIs) for individual growth stages remains unclear due to data limitations. This study addresses this gap using a 7-year (2019–2025) daily in situ hyperspectral dataset that includes shortwave infrared (SWIR) bands. We evaluated various SIs to determine their effectiveness in identifying key phenological stages. The results demonstrate that no single index captures the entire cycle; instead, a multi-index approach is required. The SWIR-based Normalized Difference Vegetation Index (SNDVI) proved superior for detecting irrigation, transplanting, and flowering. The Green–Red Vegetation Index (GRVI) effectively tracked tillering and heading, while the Normalized Difference Vegetation Index (NDVI) and Hue identified the maximum tillering stage. For the ripening phase, the Normalized Difference Yellowness Index (NDYI) exhibited the highest accuracy in detecting maturity. Validation against Sentinel-2 simulations revealed strong correlations ($R^2>0.81$) for greenness-related indices (NDVI, GRVI, SNDVI, EVI), whereas colorimetric indices showed weaker agreement. These findings establish a robust, multi-index framework for high-frequency rice phenology monitoring. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

The mineral deposits in Namibia’s Erongo region are renowned and frequently associated with complex geological environments, including calcrete-hosted paleochannels and hydrothermal alteration zones. Mineral extraction is hindered by high operational costs, restricted accessibility and stringent environmental regulations. To address these challenges, this study proposes an integrated approach that combines satellite remote sensing and machine learning to map and identify mineralisation-indicative zones. Sentinel 2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) multispectral data were employed due to their global coverage, spectral fidelity and suitability for geological investigations. Normalized Difference Vegetation Index (NDVI) masking was applied to minimise vegetation interference. Spectral indices—the Clay Index, Carbonate Index, Iron Oxide Index and Ferrous Iron Index—were developed and enhanced using false-colour composites. Principal Component Analysis (PCA) was used to reduce redundancy and extract significant spectral patterns. Supervised classification was performed using Support Vector Machine (SVM), Random Forest (RF) and Maximum Likelihood Classification (MLC), with validation through confusion matrices and metrics such as Overall Accuracy, User’s Accuracy, Producer’s Accuracy and the Kappa coefficient. The results showed that RF achieved the highest accuracy on Landsat 8 and MLC outperformed others on Sentinel 2, while SVM showed balanced performance. Sentinel 2’s higher spatial resolution enabled improved delineation of alteration zones. This approach supports efficient and low-impact mineral prospecting in remote environments. Full article

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R² = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article

Soil salinity is an essential constraint on sustainable crop production, particularly in arid and semi-arid regions, due to its effects on soil fertility. This study presents a data-driven approach for mapping soil salinity by integrating field-based electrical conductivity (EC) measurements with remote sensing and geospatial analysis in the district of Mandi Baha Uddin, Pakistan. Eleven georeferenced soil samples were collected and analyzed for EC (range: 0.59–1.06 dS/m), serving as training data for model calibration. Using Landsat 8 Surface Reflectance imagery within Google Earth Engine, spectral indices Normalized Difference Salinity Index (NDSI), Salinity Index (SI), and Brightness Index (BI) were extracted. Among various modeling approaches, a linear regression model was applied to these indices, revealing NDSI as the most significant predictor (coefficient = 12.938), while SI and BI show negligible contribution. The model achieved moderate accuracy (R² = 0.566, RMSE = 0.085 dS/m). A Random Forest approach yielded higher training accuracy (R² = 0.841) but suffered from overfitting during cross-validation, indicating limited sample size constraints. The regression equation (EC = 12.938 × NDSI + 5.864) was applied in GEE to generate the EC prediction map. The resulting 30 m resolution EC map was classified into salinity categories and validated through independent field observations. This framework highlights the effectiveness of using freely available satellite data and cloud-based platforms like GEE for cost-effective soil salinity monitoring. The study provides a transferable methodology for precision agriculture, enabling informed land management and crop planning in salinity-affected regions. Full article