Curly-leaf pondweed (Potamogeton crispus) has spread widely in shallow lakes along the East Route of China’s North Water Diversion Project, posing ecological risks, while its habitat and phenological dynamics remain poorly understood. Taking Weishan Lake in the North China Plain as the study area, we integrated multisource remote sensing data (Sentinel-2, Landsat-5 8 and moderate resolution imaging spectroradiometer (MODIS)) to build a dense normalized difference vegetation index (NDVI) time series. Based on the life-history and spectral features of curly-leaf pondweed, we applied double logistic function fitting and the dynamic threshold method to extract key phenological metrics: start of growing season (SOS), end of growing season (EOS), and length of growing season (LOS). The results showed that during 2003–2023, the habitat area of curly-leaf pondweed followed a logistic growth pattern, expanding rapidly throughout the lake from 2009 to 2019 and then declining and shifting southwestward under human interference after 2019. Although the interannual phenological trends of curly-leaf pondweed were insignificant, the standard deviations of EOS and LOS increased markedly, indicating enhanced spatial heterogeneity. Water quality (especially total phosphorus) strongly regulated the habitat area variation of curly-leaf pondweed, and water temperature dominated its phenological variations, with June water temperature significantly affecting EOS and LOS. Water depth induced a lakeshore-to-lake-center gradient in the phenology of curly-leaf pondweed, particularly for SOS. Human harvesting further reshaped the phenological spatiotemporal patterns of curly-leaf pondweed, especially EOS. This study provides insights into curly-leaf pondweed management and remote sensing-based phenological mapping.

In China, the northernmost distribution area of the invasive species Spartina alterniflora is found in the city of Tangshan, Hebei Province. To understand its expansion mechanisms and ecological impacts in Tangshan, this study investigated the expansion dynamics of S. alterniflora and its impacts on the carbon (C) and nitrogen (N) contents of plants and soil based on multi-source remote sensing data, a random forest classification model, and the Google Earth Engine platform data combined with field investigations. The results show that S. alterniflora in Tangshan exhibited a trend of rapid expansion from 2010 to 2023 at a rate of 7.85 hm2/a. Furthermore, the rate of expansion in Tangshan was notably faster when compared with those reported in other regions in China. Rapid expansion was primarily influenced by the accelerated sedimentation occurring on the tidal flats against the backdrop of large-scale reclamation. Field data revealed positive correlations between the colonization age of S. alterniflora and stem width as well as the total organic carbon (TOC) and total nitrogen (TN) contents in soil; conversely, colonization age demonstrated an inverse relationship with the soil grain size. Notably, compared with S. alterniflora in mid-latitude regions, S. alterniflora in Tangshan exhibited increased stem and leaf TN content, as well as elevated soil TOC and TN levels; however, the finding revealed lower aboveground biomass and total carbon content within plants in Tangshan than has been reported in other regions. The findings from Tangshan are similar to those reported for low-latitude areas. This study offers valuable insights into coastal wetland management under future scenarios with the northward expansion of S. alterniflora.

Mountain photovoltaic (PV) development is increasingly recognized as a strategic approach to harmonizing renewable energy expansion with land resource optimization. However, its ecohydrological implications for vegetation resilience in moutain regions remain insufficiently understood. Focusing on 23 mountain PV stations in Shijiazhuang, China, this study integrated Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) and Kernel Normalized Difference Vegetation Index (KNDVI) with the Standardized Precipitation Evapotranspiration Index (SPEI) to assess the impact of PV infrastructure on vegetation resistance and resilience. Results indicated a distinct spatial gradient in vegetation greenness: mean NDVI/KNDVI within PV arrays (0.28) was significantly lower than in the 50 m (0.30) and 200 m (0.32) buffer zones. Conversely, PV infrastructure significantly enhanced vegetation resistance to short-term drought (SPEI-3), with intra-array resistance averaging 27.9% higher than peripheral areas. This benefit is mechanistically linked to the synergistic effects of microclimatic shading, runoff concentration, and panel cleaning, which optimize local hydrothermal regimes. Differential Eco physiological responses were observed: crops and trees exhibited higher resistance than shrubs, yet crops demonstrated the lowest resilience. A consistent trade-off emerged, characterized by higher resistance but lower resilience within PV arrays compared to buffers. We conclude that mountain PV systems effectively buffer vegetation against short-term drought by altering local water-energy fluxes, thereby stabilizing fragile montane ecosystems.

Air pollution and climate change pose an increasingly serious threat to the sustainable development of terrestrial forest ecosystems. Extensive research in China has focused on single environmental factors, such as ozone, carbon dioxide, and climate change, but the multifactor interactions remain poorly understood. Here, we coupled the interactions of climate change, elevated CO2 concentration, and increasing O3 into the BEPS_O3 model. The gross primary production (GPP) simulated by the BEPS_O3 is verified at site scale by using the eddy covariance (EC) derived gross primary production data in China. We then investigated the impact of ozone and CO2 fertilization on woodland ecosystem gross primary production in the context of climate change during 2001–2020 over China. The results of multi-scenario simulations indicate that the gross primary production of woodland ecosystems will increase by 1–5% due to elevated CO2. However, increased ozone pollution will result in a gross primary production loss of approximately 8–9%. In the historical climate, under the combined effects of CO2 and O3, the effect of ozone on gross primary production will be mitigated by CO2 to 4–7%. In most areas, the effect of ozone on woodland ecosystems is higher than that of CO2 on vegetation photosynthesis, but CO2 gradually counteracts the effect of ozone on the ecosystem. Our simulation study provides a reference for assessing the interactive responses to climate change, and advances our understanding of the interactions of global change agents over time. In addition, the comparison of individual and combined models will provide an important basis for national emission reduction strategies as well as O3 regulation and climate adaptation in different regions. This also provides a data reference for China’s sustainable development policies.

High-resolution population data are crucial for various applications, from developing regional plans to disaster risk management. Current population spatialization methods typically apply population mapping relationships established at the regional level to the grid level using multi-source data. However, the significant scale difference between the regional and grid levels, combined with the simple integration of multi-source data features without considering the spatial dependence of the population, results in lower accuracy. To address the scale mismatch issue in the downscaling process, we first construct a spatially heterogeneous population label by combining census data with gridded population datasets. Then, we establish a relationship mapping between population covariates and population at a low-resolution scale (100 m) and apply it to a neighboring high-resolution scale (25 m) to reduce the prediction bias resulting from directly downscaling from the regional level to the grid level. Meanwhile, a deep learning model based on transformer feature attention convolution net (TFACNet) is employed to aggregate each geographic unit's global and local spatial relationships, integrating complementary features learned from multi-source heterogeneous data in an end-to-end manner. The experimental results in Wuhan and Guilin show that our method achieved a more accurate population spatialization (overall $R<^>2\approx$R2 approximate to 0.92) at the street level.

Atmospheric CO2 concentration is crucial for understanding carbon emissions, but satellite observations often suffer from extensive pixel loss. Existing interpolation and machine learning methods have been used to estimate missing CO2 concentrations, but these approaches result in coarse resolutions and fail to account for spatial correlations. To address these challenges, this study proposes a novel graph attention-based multi-source data fusion method to achieve high-resolution and high-accuracy CO2 concentration estimation. Each carbon observation is treated as a node in a graph, the method employs clustering-based subgraph partitioning and constructs an adjacency matrix based on spatiotemporal distances to effectively capture spatiotemporal information from sparse data. In the model, spatial attention is used to capture the spatial proximity of CO2 concentrations, while feature attention captures the complex correlations between variables. Furthermore, an advanced fusion module with linear layers models nonlinear relationships between multi-source features and CO2 concentrations. Experiments conducted in urban areas demonstrate the effectiveness of the proposed method (overall R2 = 0.904, RMSE = 1.109 ppm), with smoother mapping results and better identification of CO2 gradients, aiding in the localization of carbon sources. Variable importance analysis indicates that climate and pollutant concentrations contribute the most to CO2 estimation.

UAV-based video multi-object tracking (MOT) is a significant task in the field of remote sensing. However, current research still faces critical issues: (1) the limitations of the single architecture of DNNs inherently hinder performance improvement of object detection; and (2) current linear modeling approaches for spatiotemporal relationships fail to capture complex motion patterns in the real world. To overcome the aforementioned issues, a hybrid architecture tracker (HA-Tracker) with a spatiotemporal Mamba motion model for UAV-based video MOT is the first to be proposed, which has the following innovations and contributions: (1) a CNN–Transformer–Mamba detector (CTM detector) is proposed to enhance the capability of object detection, which is a novel synergistic fusion framework for simultaneously fusing the local details of a CNN, the global context of a Transformer, and the long-range dependency of Mamba; and (2) a spatiotemporal Mamba motion model (STM3) is proposed to improve tracking accuracy by modeling the nonlinear spatiotemporal motion relationships of object trajectories. Extensive experimental results indicate that our HA-Tracker achieved outstanding performance, with multiple object tracking accuracy (MOTA) metrics of 44.76% and 52.22% and identity F1 scores (IDF1) of 60.33% and 72.34% on the Visdrone and UAVDT datasets, respectively. These results validate the effectiveness of HA-Tracker, which outperforms the existing MOT networks.

Coordinating ecosystem services (ESs) and socioeconomic development is crucial for sustainability. This study examined Hebei Province, China, a representative region within the Beijing–Tianjin–Hebei area with diverse ecosystems and sharp developmental contrasts. A comprehensive evaluation framework aligned with the Sustainable Development Goals (SDGs) was developed, integrating economic, social, and ES dimensions. The entropy weight-TOPSIS method was used for overall assessment, and self-organizing maps (SOM) were employed to analyze spatial coupling relationships. The results indicate that social and economic indicators often exhibit synergistic effects, whereas trade-offs dominate the relationship between socioeconomic indicators and ecosystem services. In the multidimensional coordinated development zone is coordinated, all three dimensions display integrated progress. Socioeconomic development in Hebei Province shows a “multi-sphere” pattern of spatial expansion, while ecosystem services reveal distinct “mountain–plain” contrasts. Ecological functions have undergone significant transformations across prefecture from 2005 to 2020, with the region as a whole demonstrating coordinated development—characterized by relatively stable ecosystem services alongside gradual improvement in the low socioeconomic development zone. This study clarifies the synergistic mechanisms between regional development and ES, providing a theoretical and methodological basis for differentiated sustainable development strategies under the SDG framework.

Little information is available about the ecosystem carbon (C) storage among coniferous and broadleaved plantations with similar stand ages in North China. The aim of the present research was to estimate the C storages of the components of plants, litter, and soil in two coniferous plantations (Pinus tabulaeformis and Larix principis-rupprechtii) and two broadleaved plantations (Betula platyphylla and Populus davidiana) on Yanshan Mountain, North China. Allometric equations of diameter at breast height (DBH) and height (H) were used to quantify the biomass of the tree organs. The C storage of trees, herbs, litter, and soil were estimated based on the measured C contents. The C storage varied from 24.0 to 51.9 Mg ha−1, 0.3 to 0.7 Mg ha−1, and 1.9 to 4.0 Mg ha−1 in the tree, herbs, and litter layers, respectively. The ecosystem C storages were as follows: B. platyphylla (164.1 Mg ha−1) > P. davidiana (150.4 Mg ha−1) > L. principis-rupprechtii (122.3 Mg ha−1) > P. tabulaeformis (106.7 Mg ha−1), 65.7%–75.6% of which was stored in the soil layer. Broadleaf plantations stored higher C than coniferous plantations in this study. These results indicate that ecosystem C storage varied among various plantation types, and broadleaf plantations had considerable ecosystem C sequestration potential with even-aged plantation stands.

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1 degrees spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1 degrees resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R-2 of 0.86 and a root-mean-square error (RMSE) of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. The source codes and dataset can be accessed at https://github.com/CUG-BEODL/DSTM.

Achieving coordinated development among social equity (SE), economic development (ED), and ecosystem health (EH) is central to resolving the sustainability trilemma. This study investigated the spatiotemporal evolution and driving forces of SE–ED–EH coordinated development in Hebei Province, China, from 2005 to 2020 using a 1 km grid dataset. A comprehensive analytical framework integrating the Coupling Coordination Degree (CCD) model, fuzzy C-means clustering, and interpretable machine learning (XGBoost–SHAP) was developed to quantify changes in coupling and coordination (CC) levels and reveal nonlinear threshold effects. Results show pronounced spatial heterogeneity: urban cores exhibit “high coupling degree (C)–high coordination degree (T)–high CC level,” southeastern plains show “high C–low T–medium CC level,” and northwestern mountainous areas present “low C–medium/high T–low CC level.” Six dominant temporal evolution types were identified. XGBoost–SHAP reveals that nighttime lights (NL), population density (POP), and elevation (DEM) are the dominant drivers, with clear threshold ranges (NL 500–1500 nits; POP threshold near 40 persons km−2 with diminishing returns beyond 100 persons km−2; DEM constraint at 1000–1250 m) and strong interaction effects. The results suggest that Hebei is entering a quality- and structure-oriented rebalancing stage, where threshold-based management is critical for avoiding marginal loss of coordinated development. This study demonstrates that interpretable machine learning provides a transferable paradigm for threshold calibration, spatial zoning, and policy optimization aligned with SDGs, particularly applicable for resource-constrained regions undergoing late industrial transition.

The joint use of solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) has been shown to improve gross primary productivity (GPP) estimation across various plant functional types. However, the utility of PRI in combination with SIF for transpiration (T) estimation has not yet been explored. Additionally, current SIF-driven transpiration models including linear models, semi-mechanical models (combination of canopy conductance, gC, derived from a SIF and vapor pressure deficit, VPD, driven linear model with the Penman-Monteith model), and hybrid models (combination of gC derived from a SIF and VPD driven machine learning model with the Penman-Monteith model) have rarely been mutually assessed. Based on concurrent remotely sensed SIF and PRI, and eddy covariance flux measurements during one growing season for a winter wheat ecosystem in northern China, we investigated the mediating effect of PRI on SIF-driven T estimation under different VPD conditions and compared the performance of linear, semi-mechanical, and hybrid models in estimating T. Our results showed that the mediating effect of PRI on T described in the SIF-driven linear, semi-mechanistic, and hybrid models was significant under high VPD conditions rather than under low VPD conditions. Specifically, based on T partitioned using an underlying water use efficiency method as a benchmark, the root mean square error (RMSE) value of the PRI-mediated linear, semi-mechanistic, and hybrid models was 28.01 W/m2, 22.25 W/m2, and 28.71 W/m2 lower, respectively, than those of the corresponding models without PRI when VPD was >1.5 kPa. Based on T partitioned using a transpiration estimation algorithm as a benchmark, these three models also exhibited a significant reduction in RMSE under high VPD conditions after considering PRI. The main rationale behind the PRI improvement is that PRI can track photosynthetic dynamics under high VPD conditions. Based on the simulation results of the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, PRI can serve as an indicator for non-photochemical quenching (NPQ) within this ecosystem. Consequently, PRI can enhance the capability of SIF to characterize the energy dissipation of photosynthetically active radiation and help SIF to yield more accurate information on GPP and gc under high VPD conditions. Finally, the order of model performance in estimating T was generally hybrid model > semi-mechanistic model > linear model. Our findings show the effectiveness of PRI for improving SIF-driven transpiration estimation under high VPD conditions and provide a new hybrid model for estimating T from SIF.

Extreme climate events, particularly droughts, pose significant threats to vegetation, severely impacting ecosystem functionality and resilience. However, the limited temporal resolution of current satellite data hinders accurate monitoring of vegetation's diurnal responses to these events. To address this challenge, we leveraged the advanced satellite ECOSTRESS, combining its high-resolution evapotranspiration (ET) data with a LightGBM model to generate the hourly continuous ECOSTRESS-based ET (HC-ETECO) for the middle and lower reaches of the Yangtze River Basin (YRB) from 2015 to 2022. This dataset showed strong agreement with both ground-based and satellite observations. Utilizing the SPEI, we identified the significant drought period: September to November 2019 and August to September 2022. By integrating hourly Solar-Induced Chlorophyll Fluorescence (SIF) data, we observed that during drought period, the typical afternoon peak in SIF was absent. In contrast to non-drought period, morning photosynthesis and SIF-based Water Use Efficiency (WUESIF) anomalies were primarily driven by high Vapor Pressure Deficit (VPD), while the afternoon reductions were influenced by both high VPD and low Soil Moisture (SM) as the drought progressed. Our simulated HC-ETECO data revealed that ET in the middle and lower reaches of the YRB was consistently lower than normal during drought period. Attribution analysis indicated that this reduction was primarily driven by midday temperature increases and high VPD, suggesting that vegetation in the region copes with drought stress predominantly by limiting water loss. These findings highlight the utility of the generated high-resolution ET dataset in advancing our understanding of vegetation dynamics under drought climate conditions. This work provides critical insights for enhancing climate adaptation strategies and enhancing ecosystem management practices in the face of increasing climate variability.

Climate change influence on the economy and environment in the Beijing-Tianjin-Hebei (BTH) region. This study examined the variations of extreme high temperature days (EHDs) over the BTH region in summer during 1982 to 2021. The trend and frequency of EHDs has increased significantly during the last 40 years. The spatial and temporal variations of EHDs also indicate a significant increase by using the rotated empirical orthogonal functions (REOF) method. Furthermore, the singular value decomposition (SVD) method and correlation analysis are used to indicate the relationship between the EHDs in June-July-August (JJA) in the BTH region and North Atlantic sea surface temperature anomalies (SSTA) in the March-April-May (MAM) during 1982-2021. The result shows that there was positive relationship between the North Atlantic SSTA in MAM and the EHDs in JJA in the BTH region. Composite analysis suggests that rela-tionship between EHDs and SSTA are connected by associated atmospheric circulation through the wave activity flux.

Ecosystem water use efficiency (WUE) is an indicator of carbon-water interactions and is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it is currently unclear how WUE responds to atmospheric and soil drought events in terrestrial ecosystems with different dryness conditions. Additionally, the contributions of GPP and ET to the WUE response remain poorly understood. Based on measurements from 26 flux tower sites distributed worldwide, the binning method and random forest model were employed to separate the sensitivities of daily ecosystem WUE, GPP, and ET to vapor pressure deficit (VPD) and soil water content (SWC) under different dryness conditions (dryness index = potential evapotranspiration/precipitation, DI). Results showed that the sensitivity of WUE to VPD was negative at humid sites (DI < 1), while the sensitivity of WUE to SWC was positive at arid sites (DI > 2). Furthermore, the contribution of GPP to VPD-induced WUE variability was 63 % at humid sites, and the contribution of ET to SWC-induced WUE variability was 68 % when SWC was less than the 60th percentile at arid sites. Consequently, one increasing VPD-induced decrease in GPP was generally linked to a decrease in WUE at humid sites, and one drying soil moisture-caused decrease in ET was linked to a WUE increase under low SWC conditions at arid sites. Finally, VPD had a stronger effect on WUE than SWC when VPD was less than the 90th percentile or SWC was greater than the 50th percentile. Our findings underscore the importance of considering ecosystem dryness when investigating the impacts of VPD and SWC on ecosystem carbon-water coupling.

Accurate phenological extraction is important for estimating carbon uptake in terrestrial ecosystems under climate change. The emergence of remotely sensed vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) provides multiple approaches for extracting land surface phenology. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. Therefore, the advantage of combining VIs and SIF to estimate more accurate phenology requires exploration. In this study, we combined the advantages of the normalized difference, enhanced, green-red, near-infrared reflectance vegetation indices from MCD43A4 data set, and SIF from CSIF data set to estimate hybrid phenology at 20 eddy flux sites in North America. Results showed that the hybrid phenology derived from the best-performing start (SOS) and end (EOS) of the growing season among multiple VIs and SIF for each plant functional type and site were both more consistent with those derived from gross primary production (GPP). Specifically, the R-2 of hybrid phenology increased by 0.11-0.4 (0.04-0.4) for SOS, 0.01-0.24 (0.09-0.22) for EOS, 0.01-0.7 (0.05-0.34) for the length of the growing season (LOS) based on Gaussian (logistic) method. Moreover, hybrid phenology can improve the explanation of the seasonal and annual variations in GPP. The explanatory power of hybrid phenology for GPP variations increased by 0.05-0.15 (0.02-0.23) for SOS, 0-0.36 (0.11-0.27) for EOS, 0.01-0.51 (0.03-0.4) for LOS, 0.04-0.18 (0.04-0.16) fo....r LOS x seasonal GPP maximum based on Gaussian (logistic) method. These findings highlight the potential of combining high-spatiotemporal structural and coarse-spatiotemporal physiological vegetation indicators in tracking phenology and GPP.

In recent decades, China has been a hotspot for reactive nitrogen (N) deposition due to intensive fossil fuel burning and increased agricultural activities. The Chinese government has implemented active measures to protect air quality and reduce N deposition. In this study, we combined monthly measurements of monitoring sites and satellite data to construct random forest (RF) models for the estimation of monthly N deposition in China during 2008-2020. This provides N deposition estimation in latest years and has a finer temporal resolution. The RF models can address more complicated relationships between variables and performed well in estimating N deposition compared to the measurements with average correlation coefficients of 0.79 and 0.77 for dry and wet N deposition, respectively. We found that mean annual total N deposition was 22.0 +/- 0.8 kg N ha(-1) yr(-1) during 2008-2020, which accounted for 64% (+/- 4%) of reactive N emissions in China. Although the total N deposition was high, it has stabilized and decreased slightly over the past decade, with dry and wet N deposition contributing equally to the total N deposition. NHx deposition (F-NHx) still slowly increased (0.09 kg N ha(-1) yr(-2)) during 2015-2020, when N fertilizer application and livestock started to decrease. This is due to the rapid decline in NOx and SO2 emissions during the same period. Our results provide a new perspective on the spatiotemporal variations in the total N deposition in China. It is necessary to optimize other emission reduction strategies to mitigate air pollution and N deposition.

Renewable energy is widely used as an alternative source of energy, and climate change has a significant impact on its variation and harnessing. This study conducted a monthly scale analysis of global temperature and wind-speed variations and their correlations using Theil-Sen's median linear regression and Mann-Kendall test from 1989 to 2021. The results revealed that the global temperature averagely increased by 0.34 degrees C/10 a from 1989 to 2021, showing clearly increasing trends in most months but with significant differentiation in each month. The regions of East Europe Plain, Central Siberia, Central Asia, and Greenland showed the most significant increasing temperature trends ranging from 0.17 to 0.49 degrees C/10 a. Additionally, the regions in the East Europe Plain, Siberia, and North America showed distinctly decreasing temperature trends ranging from 0.27 to 0.51 degrees C/10 a. The global average wind speed changed slightly, with slopes of -0.026, -0.014, and 0.019 m/(s.10 a) for eastward, northward, and synthesis wind, respectively. However, the decreasing trends were relatively evident in regions across central Europe via the south of the Eastern European Plain to the north of Central Asia with slopes ranging from -0.13 to -0.3 m/(s.10 a). These monthly variations in eastward, northward, and synthesis wind speeds showed significant correlations with temperature, and the correlation coefficients (CCs) differed significantly for each month. For positive CCs higher than 0.34 and negative CCs lower than -0.35 (p < 0.05), the land coverage areas reached 72.3 million km(2), accounting for 48% of the global land area. In addition, the synthesis wind speed showed significant positive and negative CCs with temperature, especially from November to March of the following year. The highly positive CCs of global temperature and synthesis wind speed are primarily distributed across land areas from Europe to Siberia, North America, and North Africa in the Northern Hemisphere. The areas with high negative CCs are distributed across South America, South Africa, and Australia in the Southern Hemisphere. Climate change has a substantial impact on wind-speed variation, which should be considered during wind source harnessing. Monthly variation analysis of global temperature and wind speed and their correlation could provide key scientific support for climate change adaptation and wind resource harnessing.

Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km(2)) environmental monitoring and small-area (<1 km(2)) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for Quercus wutaishanica and 93.86% for Juglans mandshurica. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of Quercus liaotungensis. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs.