Search Results (1,226)

To deepen our understanding of human–ecosystem interactions, researchers need to be able to retrieve and analyze historical dark data such as plant and animal phenology, but there are often barriers to doing so. Despite the development of online digitization and other tools, including library search engines, digital collections, machine translation, OCR (optical character recognition), HTR (handwritten text recognition), and generative AI technologies, and the establishment of standards and frameworks (e.g., FAIR Principles and the International Image Interoperability Framework), barriers to converting analog records to digital records (“digital barriers”) and to translating local languages to an international common language (“language barriers”) still remain. We present a case study example of the use of historical dark data in phenology in Japan and the digital and language barriers encountered. We then briefly summarize factors and challenges hindering use of this data and describe the benefits of further removal of these barriers. Full article

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Leaf area index (LAI) is widely used to characterize foliage amount and seasonal canopy development, but it captures only selected aspects of forest structure and can be difficult to retrieve reliably in heterogeneous, multilayered stands. This study evaluates Sentinel-2-based LAI information across eight sites in the Slovenian Classical Karst encompassing post-disturbance regeneration and established forest stands in dolines and relatively level inter-doline terrain. Field effective LAI measured during six periods in 2021 was compared with six Sentinel-2 spectral variables, LAI derived using the Sentinel Application Platform (SNAP), and the Copernicus Land Monitoring Service High-Resolution LAI product. The analysis explicitly distinguished two dimensions of retrieval performance that are often conflated: seasonal fidelity within sites and preservation of structural differences among sites. Most satellite-derived variables and LAI products captured the broad phenological progression from canopy development to senescence. However, strong temporal agreement within sites did not consistently translate into preservation of the ordering or magnitude of structural differences among sites. Several methods compressed the range of high effective LAI values at dense regeneration sites with substantial lower-layer vegetation. The study therefore provides a more informative framework for evaluating LAI products by identifying which component of variation drives apparent agreement. These findings indicate that Sentinel-2 can support phenological monitoring and broad screening of post-disturbance vegetation development. However, quantitative comparisons of canopy density or structural recovery across heterogeneous stands require consideration of canopy heterogeneity, potential spectral saturation, and plot-to-pixel support. The evaluation framework and the observed retrieval limitations are relevant beyond karst forests, particularly to post-disturbance stands, fragmented forests, open woodlands, and sites with dense understory or regeneration layers. Full article

The Jianghan Plain is one of the important freshwater aquaculture regions in China. Accurate information on the spatial distribution and spatiotemporal dynamics of aquaculture ponds is essential for regional ecological management. However, large-scale and accurate identification remains challenging for aquaculture ponds because they have spectral and seasonal hydrological characteristics similar to those of rice fields, rivers, canals, and lakes. In this study, we developed a framework for mapping inland aquaculture ponds using multi-source remote sensing data from Sentinel-2, Sentinel-1, and PlanetScope. The framework integrated elevation-zoned Otsu thresholding for candidate water extraction, phenological features for rice field removal, and object classification based on a Gradient Boosting Decision Tree (GBDT) model using 12 shape and spatial-context features. It was applied to identify aquaculture ponds in the Jianghan Plain from 2016 to 2025. Overall accuracy exceeded 93%, and the F1-score exceeded 0.93 in all validations. Results from different sensors also showed high spatial consistency. In 2025, aquaculture ponds covered 2365.17 km² in the Jianghan Plain and were mainly concentrated in the central and eastern parts of the plain, especially near Honghu Lake and along the Yangtze and Hanjiang river meanders. Over the ten-year period, the aquaculture pond area fluctuated between 2033.58 and 2383.52 km², showing an initial decline followed by recovery. Lost aquaculture ponds were mainly located around lakes, while newly added ponds were mostly distributed along the margins of existing clusters. The decade dataset generated in this study can support freshwater aquaculture management and wetland conservation. Full article

The seasonal development and overwintering success of Halyomorpha halys remain insufficiently resolved in temperate continental regions of southeastern Europe, where climatic conditions may permit more than one generation per year. We monitored seasonal development, thermal accumulation, overwintering survival, and pheromone-trap captures of H. halys during 2024 and 2025 in continental Croatia. Experimental populations were maintained under unheated semi-outdoor conditions and followed from overwintered adults through oviposition, all five nymphal instars, and adult emergence. Degree-days were calculated above a lower developmental threshold of 12.2 °C. Two complete egg-to-adult generations developed in both years. The observed phenological interval from the first egg masses to first adult emergence was 56 days in 2024 and 42 days in 2025 for G1, and 49 and 53 days, respectively, for G2. Thermal accumulation during immature development ranged from 468.5 to 562.7 DD_12.2. First-generation adults emerged in early July in both years, whereas second-generation adults appeared on 12 September 2024 and 23 September 2025. Developmental stages overlapped during summer and early autumn. Overwintering survival under the two anthropogenic refuge conditions was low, reaching 9.4% under heated and 7.8% under unheated indoor conditions. Pheromone-trap catches showed marked seasonal variation but were not correlated with temperature, relative humidity, or precipitation during corresponding trapping intervals. These results demonstrate the developmental potential for two generations under temperate continental conditions in Croatia and provide phenological benchmarks for stage-specific monitoring. Monitoring should begin with spring adult activity and be intensified during May–June and again from late July onwards as successive generations develop. Full article

Nitrogen (N) is one of the most important nutrients influencing wheat growth, plant nutrition, and grain production. Appropriate timing of nitrogen application is essential to synchronize nutrient availability with crop demand. This study evaluated seven nitrogen management treatments, including a control (N0) and six split nitrogen application schedules (N1–N6), in durum wheat under Mediterranean conditions using an integrated approach combining ground-based hyperspectral sensing and unmanned aerial vehicle (UAV)-based multispectral imagery. Plant nutrient concentrations (N, P, K, Ca, and Mg), spectral reflectance, vegetation indices, plant height, and grain yield were evaluated at different phenological stages. Split nitrogen application significantly affected plant nutrient concentrations, spectral reflectance, vegetation indices, plant height, and grain yield. Plant nutrient concentrations generally declined with crop development, whereas spectral reflectance increased across the visible and near-infrared regions of the spectrum. Vegetation indices derived from both hyperspectral and UAV multispectral data successfully differentiated phenological stages and nitrogen treatments. UAV-derived plant height showed strong agreement with field measurements, confirming the reliability of photogrammetric measurements for monitoring crop development. Among the nitrogen treatments, the N3 split application schedule produced the most favorable overall crop response, with higher plant nitrogen concentration, stronger spectral responses, and the highest grain yield. In addition, UAV-derived NDVI measured at the booting stage showed the strongest relationship with grain yield (r = 0.717, p < 0.01). These findings demonstrate that integrating ground-based hyperspectral sensing with UAV multispectral imagery provides complementary information for evaluating crop development and plant nutritional responses under different split nitrogen application schedules. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C₄ carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment. Full article

Persistent global warming significantly impacts crop phenology and productivity, with perennial fruit trees facing heightened challenges due to their long life cycles and complex heat stress accumulation. Apple and pear, which hold substantial economic and nutritional value, are particularly vulnerable to high temperatures, manifesting as accelerated phenology, impaired floral organ development, disrupted pollination and fertilization, and insufficient fruit coloration—all of which severely compromise fruit quality and commercial value. Although recent advances have been made in elucidating heat stress signal transduction and regulatory networks in model plants such as Arabidopsis and rice, research on heat stress responses in apple and pear remains limited. This review systematically synthesizes the physiological responses, gene expression regulation, and protective cultivation strategies under high-temperature stress in apple and pear, aiming to provide a theoretical foundation for thermotolerance breeding and the establishment of heat stress regulatory networks, thereby supporting sustainable production in the context of global warming. Full article

Seasonal forest phenology is commonly summarized as canopy greenness or phenophase timing, although leaf development also redistributes observed plant material through three-dimensional space. We evaluated whether repeated RGB-colorized handheld simultaneous localization and mapping (SLAM) can provide height-resolved trajectories of seasonal observed occupancy in adjacent Ailanthus altissima-dominated and native-dominated sectors of a young deciduous forest. We acquired 149 scans on 25 dates from March 2025 to March 2026 at six permanent locations. Point clouds were restricted to date-invariant common support, normalized to a March terrain model, and voxelized at 0.20 m. New occupancy was referenced to the union of two strict March leaf-off scans. A weakly supervised foliage likeness proxy combined geometry-first pseudo-labels with relative color, intensity, and local three-dimensional features; its outputs were interpreted as relative scores rather than leaf fraction, LAI, or biomass. The strongest and most persistent invaded positive signal was localized to 2–4 m. Continuous-time models supported the integrated 1–5 m contrast from late April through October, whereas formal support for the 5–12 m crown domain was limited to the late season invaded positive phase; the earlier native positive crown feature remained descriptive. Height-integrated SLAM showed broad seasonal concordance with intercepted PAR (r_rm = 0.881), GCP-linked Sentinel-2 EVI2 (r_rm = 0.670) and the five-date litterfall comparison (r_rm = 0.914). However, correlations of the invaded minus native trajectories were positive but imprecise. The independent observations therefore supported the broad seasonal cycle rather than the detailed sector-specific or height-specific pattern. The workflow provides a conservative means of localizing relative seasonal observed occupancy in three dimensions, but the resulting contrasts remain site-specific and hypothesis-generating. Full article

Urban greening is a cornerstone of climate adaptation strategies, yet its long-term sustainability under changing vegetation dynamics remains poorly understood. This study analyzes 40 years of Landsat data (1984–2024) using normalized difference vegetation index (NDVI) time series to quantify changes in vegetation extent, phenology and interannual stability across four European cities (Paris, Graz, Barcelona and Birmingham). The study investigates long-term phenological trends in heterogeneous urban green spaces. Results showed similar developments in greening measures at all four study sites: In Birmingham, Paris and Barcelona, vegetation extent increased by 12 to 23 absolute percentage points, while Graz showed a moderate increase of 4 percentage points. A general tendency towards a longer growing season was observed across the study sites, driven by directional changes towards an earlier Start of Season (SOS) and a delayed End of Season (EOS), although the magnitude and statistical significance varied between cities. Barcelona exhibited pronounced summer NDVI declines, whereas peak vegetation activity occurred earlier in Paris and slightly later in Birmingham. Interannual variation showed greater differences in smaller, fragmented open green spaces (Coefficient of Variation ≈ 0.3) compared to larger tree-dominated parks (Coefficient of Variation ≈ 0.1). These findings provide a harmonized long-term assessment of urban vegetation dynamics. European cities are becoming greener while simultaneously experiencing shifts in vegetation activity and, depending on location, strong declines during summer months. These long-term changes highlight the importance of considering vegetation dynamics when planning resilient urban green spaces under a changing climate. Full article

Accurate estimation of forest tree species diversity at regional scales is essential for biodiversity monitoring, forest resource management, and ecological conservation. Because tree species differ in canopy spectral responses and phenological dynamics, multi-season remote sensing observations can provide critical information for characterizing species composition and diversity patterns. However, the potential contribution of seasonal image features to improving remote-sensing-based tree species diversity estimation has often been insufficiently considered. In this study, the Yichun forest region in Heilongjiang Province, northeastern China, was selected as the study area. Sentinel-1, Sentinel-2, and topographic data were integrated to extract multi-seasonal spectral, vegetation index, texture, radar, and topographic features. The Boruta algorithm was used for feature selection, and random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector regression (SVR), Bayesian regularized neural network (BRNN), and Stacking ensemble learning were developed to estimate and map Richness, Shannon, and Gini–Simpson indices. The results showed that: (1) Sentinel-2 optical features were the primary information source for tree species diversity estimation, topographic factors further improved model performance, and Sentinel-1 radar features mainly provided complementary structural information; (2) seasonal remote sensing features differed in their predictive ability, with Richness performing better in spring, while Shannon and Gini–Simpson achieved higher accuracy in winter. The four-season fusion scenario produced the highest accuracy for all three indices, with optimal R² values of 0.51, 0.63, and 0.57, respectively; (3) the Stacking ensemble generally improved estimation accuracy and model stability, although the optimal model differed among diversity indices, with Stacking, SVR, and RF performing best for Richness, Shannon, and Gini–Simpson, respectively; and (4) summer Sentinel-2 NDVI, GNDVI, and NDWI contributed strongly to all three indices, elevation was particularly important for Richness, and winter vegetation indices and autumn red-edge bands and texture features were also informative for Shannon and Gini–Simpson. These findings indicate that integrating multi-seasonal remote sensing features and multi-source data using machine learning models can effectively improve forest tree species diversity estimation, providing technical support for regional forest biodiversity monitoring and precision forest management. Full article

Long-term chlorophyll-a (Chl-a) phenology and recurrent high-Chl-a conditions provide important evidence for identifying spatially persistent water-quality concerns in coastal bays. However, seasonally normalized screening of high-Chl-a recurrence at the sampling-cell scale remains limited for subtropical multi-bay coastlines, where regional monsoon forcing, hydrodynamic retention, riverine inputs, aquaculture, and coastal development jointly shape phytoplankton variability. Based on Copernicus Marine ocean-colour records from 2003 to 2024, this study developed a reproducible ~4 km sampling-cell framework for seven coastal bays in Fujian, China. Monthly geometric-mean climatologies, phenological metrics, P90-based high-Chl-a exceedance frequencies, monitoring-priority classes, and exploratory machine-learning diagnostics were derived. Bay-scale phenology showed clear divergence: Sansha and Xinghua Bays exhibited winter or year-end Chl-a enhancement, Xiamen Bay peaked in summer, and Quanzhou Bay reached an early-autumn maximum. Four seasonal phenological regimes further revealed cell-scale heterogeneity, with C2 representing winter-enhanced, high-amplitude cycles and C3 identifying cells with the most frequent seasonally normalized high-Chl-a exceedances. High- and moderate-priority cells were concentrated mainly in Sansha and Xinghua Bays, whereas Dongshan and Quanzhou Bays showed only localized priority cells. Exploratory diagnostics indicated that distance to the bay mouth was the most important correlate of recurrent high-Chl-a susceptibility, suggesting the role of bay-scale exchange and retention gradients. This framework converts long-term ocean-colour archives into spatially explicit phenological and anomaly-screening evidence to support targeted coastal water-quality monitoring and ecosystem management. Full article

The phenological responses of different progenies to the effect of ambient temperature on the duration of the fruit ripening cycle in Coffea arabica L. remain poorly understood. In this study, the ripening cycle in the coffee progenies of the Cenicafé 1 variety was recorded using the number of growing degree days during the main and secondary harvests. Concurrently, variables such as the number of fruits per node, firmness, diameter, volume, fresh mass, dry mass of the pericarp and bean, and the fruit respiration rate were measured. A semiparametric repeated-measures analysis of variance was used to assess inferential components, and Spearman’s multiple correlations were used to examine the associations between variables. No significant differences were observed between the progenies for any variable. However, we detected differences between harvests in terms of the number of growing degree days, number of fruits per node, fruit firmness, fruit diameter, fruit fresh mass, and pericarp and grain dry mass. Furthermore, the correlation analysis revealed that the physical characteristics of the fruit exhibited the strongest associations among themselves. The absence of significant differences in the ripening cycle and fruit characteristics among the progenies may be attributed to their shared genetic origin. Finally, the observed change in the number of fruits per node between harvests directly influenced the physical characteristics of the fruits (diameter, volume, fresh mass, and pericarp dry mass), as the reduced physical space available for individual fruit growth when the node fruit load was higher limited their development. Full article

Environmental variability poses major challenges to bread wheat production in arid regions. This study evaluated the adaptation, productivity, and grain quality of fifteen diverse bread wheat genotypes. The evaluated germplasm comprised advanced breeding lines developed by the Arab Center for the Studies of Arid Zones and Dry Lands (ACSAD), Saudi landraces, newly released cultivars, and a cultivar derived from the International Maize and Wheat Improvement Center (CIMMYT). Field experiments were conducted over two consecutive growing seasons (2022/2023 and 2023/2024) at two contrasting arid environments in Saudi Arabia. Riyadh exhibited warmer, drier conditions, with higher soil calcium carbonate content than Hail. Significant effects (p ≤ 0.01) of genotype, environment, and genotype-by-environment interaction were detected for all traits studied. Compared with Hail, Riyadh exhibited lower grain and biological yields, fewer spikes/m², lighter grains, reduced plant height, and shorter growth duration. Riyadh-1 achieved the highest grain yield (7.54 t/ha) and biological yield (21.44 t/ha). ACS-1454, ACS-1422, ACS-1372, and Maeaa also demonstrated superior productivity and adaptation. Local landraces were characterized by late heading and maturity, whereas ACS-1454, ACS-1422, ACS-1400, and ACS-1464 exhibited early phenology across environments. LR-12 and LR-599 exhibited the highest protein and gluten contents, whereas Riyadh-1 and Yecora recorded the highest gluten index values. Multivariate analyses, including principal component analysis, hierarchical clustering, and AMMI identified Riyadh-1, ACS-1454, ACS-1422, ACS-1372 and Maeaa as promising candidates for cultivation and breeding. Furthermore, the local landraces (LR-12 and LR-599) represent valuable sources of adaptive genetic diversity and grain quality for climate-resilient wheat cultivars. Full article

Temperature is a major environmental determinant of flowering time and reproductive success in plants. Ongoing global warming is changing flowering phenology across natural and agricultural ecosystems, yet the mechanisms by which ambient temperature regulates the transition to reproduction remain less fully resolved than those underlying photoperiodic flowering and vernalization. This review synthesizes eco-physiological and molecular evidence for temperature-dependent flowering across plant groups and terrestrial ecosystems. It considers the development of the florigen concept, the identification of FLOWERING LOCUS T (FT) and related phosphatidylethanolamine-binding protein family members, and the integration of temperature signals with flowering activators and repressors. Particular attention is given to the thermosensory pathway, including alternative splicing, chromatin regulation, membrane-associated signaling, phase separation and temperature-dependent accumulation or stability of regulatory proteins. The review also examines phenological responses to rising temperatures in bulbous geophytes, Arctic and boreal species, subtropical and tropical crops, and desert plants. Available evidence indicates that the temperature requirements for floral initiation, their organogenesis and anthesis vary widely among species and developmental stages, and that these optima reflect life-history strategy, origin and adaptation to seasonal temperature regimes. Full article