Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1,423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (⍴=0.3, p<1x10 -8 ) between individual wavebands or vegetative indices and growth rate, assessed as the relative change of tree height over the growing season. The growth rate prediction was substantially improved by a regularization multivariate model (⍴>0.5, p<1x10 -16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.

Bridging the climate–phenology–recreation nexus: Implications for the sustainability of grassland recreational services

Hurricanes have significant consequences for ecosystems, potentially disrupting the carbon cycle at both local and regional scales and releasing carbon back into the atmosphere through storm-associated impacts on vegetation and agricultural areas. The present work analyzes the interactions amongst terrestrial carbon fluxes, rainfall, and land cover for three significant hurricanes: Harvey (Texas), Irma (Florida), and Maria (Puerto Rico). This study utilized net ecosystem exchange (NEE) data derived from the Soil Moisture Active Passive (SMAP) NASA satellite mission, which provides global estimates of soil moisture and carbon flux, and analyzed these data for coastal climate zones during the hurricane season. The results were validated using eddy covariance tower-based in-situ CO 2 flux observations during hurricane landfall. Results showed that southern Texas (Harvey) experienced the highest amount of carbon release (0.33 megatons), followed by Florida (Irma) (0.03 megatons) and Puerto Rico (Maria) (0.02 megatons). The land cover products, such as the National Land Cover Dataset (NLCD) and the Copernicus Global Land Service (CGLS), showed overall reductions in land cover in Florida (-1.02%), Texas (-0.97%), and Puerto Rico (-0.46%). Furthermore, vegetation cover changes were estimated using MODIS-derived enhanced vegetation index (EVI), showing major changes over Puerto Rico (-3.81%) and southeast Texas (-2.94%), while normalized difference vegetation index (NDVI) showed more moderate reductions over Puerto Rico (-3.06%), southeast Texas (-1.12%), and Florida (-0.16%). These reductions indicate short-term vegetation stress and decreased photosynthetic activity, which may temporarily reduce carbon uptake, leading affected regions to transition from carbon sinks to temporary carbon sources. These findings highlight hurricanes as significant drivers of short-term carbon emissions and vegetation change. This study enhances understanding of hurricane-associated disturbances in the carbon cycle by examining spatial and temporal variations in carbon fluxes during extreme weather events. • The impacts of hurricanes Harvey, Irma, and Maria (2017) on terrestrial carbon fluxes were assessed. • Carbon release during and after landfall was measured using SMAP-derived NEE and eddy covariance CO 2 flux data. • Hurricane Harvey resulted in the largest carbon emission (0.33 megatons), followed by Irma (0.03 megatons) and Maria (0.02 megatons). • Vegetation loss, derived from MODIS NDVI and land cover change products, was greatest in Texas (6,199.3 km 2 ), then Florida (492.92 km 2 ), and Puerto Rico (14.53 km 2 ). • Vegetation declines led to reduced photosynthetic activity, temporarily turning affected areas from carbon sinks into carbon sources. • Findings highlight hurricanes as significant short-term drivers of carbon emissions and ecosystem disturbance across coastal regions.

Weed classification in sugarcane fields in Northeast Thailand from multi-temporal Sentinel-1 and Sentinel-2 data together with random forest algorithm

High-resolution winter cover crop mapping with PlanetScope imagery: Comparative analysis of Random Forest, Convolutional Neural Network, and unsupervised classification

Remote sensing estimation for winter wheat plant nitrogen based on nitrogen distribution model building and leaf nitrogen quantitatively inversion using vegetation index clustering method

Mortality impacts of air pollution and greenness: real-world and counterfactual exposure scenarios in seven Northern European cities.

Residential greenness and depressive symptoms in the Mexican adult population: A cross-sectional study.

Hyperspectral remote sensing captures hundreds of contiguous, narrow spectral bands in the VNIR and SWIR ranges, enabling detailed analysis of vegetation, water quality, soil properties, and other environmental variables. prismatools is an open-source Python package that facilitates processing, visualization, and analysis of PRISMA Level 2 products. It converts VNIR, SWIR and panchromatic PRISMA data into georeferenced xarray datasets, supporting seamless integration into workflows with other popular Python packages. The package also provides interactive mapping and spectral exploration leveraging the capabilities of the popular package Leafmap, along with methods for computing vegetation indices, performing PCA, extracting spectral signatures and exporting processed images.

Identifying the environmental drivers of the distribution of carbon isotopes in global inland waters.

Spatial characteristics and driving factors of human brucellosis and plague infections in the Meriones unguiculatus plague focus in China.

Exposure to residential greenness has been shown to mitigate anxiety and stress, buffering negative mental health impacts of COVID-19 restrictions. In 2020, a cross-sectional survey in Stockholm County, Sweden, examined associations between residential greenness and mental health during the early pandemic. To evaluate potential long-term effects, a follow-up survey was conducted in 2022. This study aimed to assess whether these associations persisted and whether nature-related behaviours changed after the pandemic. Cross-sectional data from 2020 and 2022 were compared. Residential greenness was quantified using the Normalized Difference Vegetation Index (NDVI), calculated within 50 m, 100 m, 300 m, 500 m, and 1000 m buffers surrounding participants’ residences. Mental health outcomes included well-being, vitality, depressive symptoms, and perceived and cognitive stress. Across the pooled data, higher residential greenness was consistently associated with better mental health. Associations were generally stronger in 2020 than in 2022, suggesting that contact with nature may be especially beneficial during acute stress or social isolation. Findings underscore the role of urban greenness in supporting mental health resilience during crises and indicate that the pandemic may have led to lasting shifts in nature-related behaviours. Further longitudinal research is needed to clarify long-term effects of environmental and behavioural changes on population mental health. • NDVI was positively associated with mental health in both 2020 and 2022. • COVID-19 had long-term consequences on mental health in Stockholm County, Sweden. • Findings underscore the public health value of accessible urban greenspaces.

• Morphology-based method is proposed for temporal NDVI reconstruction. • It preserves low-frequency phenological signals under cloud contamination. • It outperforms moving average, Savitzky-Golay, and HANTS in spectral fidelity. • It enhances vegetation clustering stability and separability in unsupervised analysis. Cloud contamination remains a major challenge in the analysis of satellite-derived Normalized Difference Vegetation Index (NDVI) time series, particularly in dryland and semi-arid ecosystems where phenological signals are sparse and irregular. This study investigates temporal reconstruction of NDVI under quality masking–induced data gaps, with a specific focus on preserving low-frequency phenological structure rather than maximizing pointwise accuracy. We propose a fully unsupervised reconstruction strategy based on one-dimensional flat morphological closing applied along the temporal dimension, and systematically compare it against common baseline methods, including moving average smoothing, Savitzky-Golay filtering, and harmonic analysis of time series (HANTS). Reconstruction fidelity is first evaluated under controlled cloud simulations using spectral-domain metrics derived from dominant annual and intra-annual harmonics. At a noise level of 0.3, morphological reconstruction achieves a spectral fidelity of 0.93 and an RMSE of 0.02, compared to spectral fidelity value of 0.81 and RMSE of 0.073 for the strongest competing method. The practical implications of reconstruction fidelity are then assessed through unsupervised clustering of real Sentinel-2 NDVI time series. Clustering performance is evaluated using F1-score and precision, both with and without spectral feature augmentation derived from low-order Fourier amplitudes. Morphological reconstruction achieves a mean F1-score of 0.68 compared to 0.59–0.62 for baseline methods and shows minimal improvement (<0.02) after spectral augmentation, indicating that dominant phenological information is already preserved. In contrast, competing methods gain 0.05–0.08 in F1-score after augmentation, suggesting compensation for spectral distortion. Together, these results demonstrate that morphological temporal reconstruction provides a simple, parameter-light, and phenologically consistent alternative for quality-based masking mitigation in NDVI time series, with measurable advantages for downstream unsupervised analysis

This contribution showcases advanced artificial intelligence applications that transform over 20 years of terrestrial water storage anomaly (TWSA) observations from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission into a comprehensive 100-year dataset for the Congo Basin. We develop CM-RecNet, a climate-memory hybrid model, to reconstruct the basin’s TWSA for the period 1923–2024. CM-RecNet combines two RecNet deep learning models—one capturing climate-driven TWSA and another capturing memory effects—fused via a multilayer perceptron. The model achieves strong performance, with a correlation coefficient (CC), Nash–Sutcliffe Efficiency, and normalized root mean square error of 0.82, 0.70, and 0.20 during the testing period, respectively. Our reconstruction aligns well with observed runoff (CC>0.6 at most stations), Normalized Difference Vegetation Index (CC = 0.71), and water balance budget (CC = 0.69). In addition to its consistency with existing reconstructions, CM-RecNet exhibits a heightened capacity to capture the basin’s climate variability. This innovative approach enables access to previously unavailable data within the Congo Basin, necessary for understanding its critical water challenges associated with climate change and anthropogenic activities.

Liver flukes (Opisthorchis viverrine, OV) infections in water sources continue to persist in Sakon Nakhon Province, which is linked to the Mekong River. The agency's traditional infection data comprises the locations of infected water sources. However, this data is insufficient for developing a predictive model for infections within the sub-basin. When analyzed alongside independent variables, represented as identical points, it lacks the necessary information to generate a trend line that produces a reliable coefficient. This study implemented a spatial model that integrates a geographic-weighted regression (GWR) framework with appropriate weighting as a prototype. This approach improves the selection of independent variables by shifting from a point-based methodology to a weighted hexagonal grid. A set of eight independent variables land use, soil drainage, road network, water sources, streamlines, surface temperature, NDMI (Normalized Difference Moisture Index), and NDVI (Normalized Difference Vegetation Index) was initially weighted. This study developed three linear models based on the Geographically Weighted Regression (GWR) model. It demonstrates the advantages of utilizing a hexagonal grid instead of a point grid. The three alternative models were tested with various independent variables and employed a factor-by-factor averaging approach, which necessitates the hexagonal grid size as a counterweight to ensure fairness across the entire grid, rather than relying solely on point data. A mathematical model was developed to calculate the average of each factor in order to achieve equality across a hexagonal grid area. Subsequently, the correlation was tested, and the alternative models were grouped. The resulting dendrogram includes three models. The results of the GWR comparison test were derived from both infected and hexagonal water source data. Models constructed from hexagonal grids consistently outperformed all alternative models, with R2 values improving to 58.7%, 41.1%, and 53.2% for Model-1, Model-2, and Model-3, respectively. The RMSE also showed significant improvement, decreasing to 77.1%, 60.2%, and 67.2%. Additionally, the model's accuracy was evaluated using AUC values of 0.725, 0.652, and 0.707, indicating that the developed model can effectively predict water source infections. Model-1 emerged as the most representative across all tests, incorporating soil drainage factors and road proximity as key influences on water source infection. Finally, the results are presented as infection prediction maps for each grid, highlighting areas of both overestimation and underestimation. The most accurate prediction model identified that over 95% of grids had a high degree of accuracy. This study is anticipated to be applicable to infections caused by other water-mediated parasites.

Spectral feature extraction via 1D-CNN and band-synergy analysis for grassland aboveground fresh biomass estimation

• The 675 wind farms in China exhibited an overall LST effect of nighttime warming and daytime cooling. • The vegetation decreased by wind farm construction and recovered over time. • The LST and vegetation impacts of wind farms depended on land cover types. • Land cover distributions contributed to the latitudinal variations of LST impacts. The rapid development of wind energy in China since 2000 has raised concerns about its impacts on local climate and vegetation. Despite regional and local studies, a comprehensive national assessment is lacking. Here, we analyzed the effects of 675 onshore wind farms, representing >90,000 identified wind turbines in China, on land surface temperature (LST) and vegetation using Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data from 2003 to 2022. We find a daytime cooling effect of -0.05 ± 0.48°C (mean ± STD) and a nighttime warming effect of 0.06 ± 0.28°C across all wind farms. The infrastructure construction of wind farms initially reduced peak normalized difference vegetation index (NDVI) by -0.006 ± 0.036, and this adverse impact weakened over time (-0.004 after 7 years), indicating vegetation recovery. The wind farm impacts varied by land cover type. The nighttime warming was largest for barren lands (0.19°C), followed by croplands (0.10°C), grasslands (0.07°C), and forests (0.01°C). These differences contributed to increasing warming from south to north China. The adverse vegetation impacts were largest for forests (-0.010), followed by grasslands (-0.008) and barren lands (-0.003), with croplands (0.001) being almost unaffected. Correlation analysis identified precipitation and mean LST as significant factors influencing spatial variations in nighttime LST impact, with greater vegetation decline reinforcing night warming. Our large-scale analysis provides comprehensive evidence of the heterogeneous environmental impacts of wind farms across China, informing the sustainable development of wind energy.

This study presents an integrated machine learning framework to predict potential spring occurrence zones in the high-altitude Kishtwar region of the Indian Himalayas, where springs are rapidly depleting due to climate and anthropogenic pressures. Forty-two geotagged spring locations obtained from field visits combined with pseudo-absence background locations were used to train and test the machine learning models using 80:20 split. Predictor variables-including soil moisture, elevation, slope, Enhanced Vegetation Index (EVI), annual precipitation, and saturated soil hydraulic conductivity-were derived from remote sensing and environmental datasets. Random Forest, MaxEnt, and CART models were trained and combined through linear regression-based ensemble integration to reduce the bias. Elevation, precipitation, and soil parameters were key predictors affecting the springs occurrence. Model outputs delineated 815.6 km2 as potential and 467.3 km2 as high-probability spring zones in the study area. While the ensemble achieved high discriminatory performance (AUC up to 0.99), the results are based on a limited set of field-observed springs and should be interpreted as screening-level prioritization outputs rather than definitive predictive maps. The study, although designed on a regional scale, demonstrates scalable potential for Himalayan springshed management, enabling data-driven restoration through advanced geospatial-machine learning integration.

Multivariate sensitivity analysis and threshold response of dust to drought indices and their underlying variables.

Maize (Zea Mays) is one of the world's most important staple crops, providing food for humans and feed for livestock. However, its production is threatened by a range of stresses, including crop diseases, which significantly reduce yields, particularly in smallholder farming systems. Traditional disease detection methods, such as visual inspection, are often labour-intensive, subjective, and prone to error, leading to delayed interventions and widespread crop losses. This study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases in a smallholder farm located in the Mopani District of Limpopo Province, South Africa. UAV-derived vegetation indices including NDVI, GNDVI, and NDRE were combined with UAV multispectral bands and the three ML algorithms, namely - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), to first distinguish healthy from diseased plants and then to classify specific maize diseases. The SVM algorithm achieved the highest accuracy in both, distinguishing healthy and diseased crops from other land cover classes (91.73%) and in distinguishing specific diseases (89.41%). Among the diseases identified, Southern Corn Leaf Blight was classified with the highest user's accuracy, while phosphorus deficiency had the lowest user's classification accuracy. The results demonstrate the potential of integrating UAV-based multispectral imaging and ML for precision agriculture by providing timely, spatially detailed disease information that enables targeted management practices, reducing crop losses and enhancing food security for smallholder farmers.