Urban blue and green spaces play a critical role in mitigating the urban heat island (UHI) effect, yet the comparative air-temperature-based cooling performance of green versus blue spaces, their scale dependency, and their response to heat waves (HWs) remain poorly quantified. This study analyzed air temperature data from 81 meteorological stations in Shenzhen to quantify the cooling effects of urban blue and green spaces. Results show that green space consistently exhibits strong, negative correlations with UHI intensity across all spatial scales and times of day, whereas blue space provides significant cooling only at micro-scales (<250 m) and may contribute to nocturnal warming at larger scales due to thermal inertia. The cooling efficiency of green space exhibits a power-law scaling relationship with analysis scale, characterized by a rapid increase at smaller scales followed by progressively smaller gains at larger scales, eventually approaching a stable level. While HWs enhance green space cooling efficiency across all scales, the scale‐dependent cooling pattern appeared to remain generally consistent during the observed heat-wave events from 2012 to 2017. These findings demonstrate the significant, widespread, and relatively stable cooling capacity of urban green space, highlight the localized yet limited effect of blue space, and provide guidance for scale-aware, climate-adaptive urban planning under extreme heat conditions.

Urban evapotranspiration (ET) plays a critical role in regulating urban heat and governing water–energy exchanges. However, high-resolution ET estimation across entire cities remains challenging due to pronounced surface heterogeneity, which limits the applicability of physical models originally developed for homogeneous natural surfaces. Moreover, satellite-derived ET estimates are often spatially and temporally fragmented due to their dependence on clear-sky conditions and long revisit intervals. To address these limitations, we propose a transformer-based framework for generating spatiotemporally seamless, daily urban ET maps at 10 m resolution. The framework integrates 10 m vegetation greenness from Sentinel-2 NDVI, Google satellite embedding data, and gap-free daily meteorological variables (precipitation, air temperature, and radiation etc.). A key advantage of the proposed method is its use of a group-to-token transformer encoder, which learns nonlinear interactions among atmospheric demand, surface structure, and vegetation dynamics without requiring explicit urban parameterization. The model is trained on time-series observations from 64 globally distributed FluxNet towers and an urban flux site in Shenzhen, where the flux data are gap-free and suitable for continuous daily modeling. The trained model performs well, with an overall R² of 0.92 and RMSE of 0.37 mm d⁻¹. Comparative analyses further indicate that the proposed framework reduces RMSE by approximately 16% and improves spatial discrimination in heterogeneous urban settings. The trained model was applied to Shenzhen, China, generating a spatiotemporally seamless daily urban ET dataset at 10 m resolution for 2017–2024. Validation confirms the reliability of the approach for urban ET mapping, with a mean R² of 0.56 and RMSE of 0.45 mm d⁻¹. Overall, this study establishes a practical pathway for producing cloud-gap-free, 10 m daily urban ET estimates by integrating routinely available reanalysis data with satellite-derived geospatial embeddings. The framework robustly captures intra-urban heterogeneity, supports seasonal to interannual analyses and pixel-wise trend detection, and enables scalable applications in urban hydroclimate research and planning.

Mitigating pollutants and greenhouse gas (GHG) emissions is a core strategic imperative for China, necessitating integrated technological solutions. Satellite remote sensing (SRS) has emerged as a pivotal tool for environmental monitoring, enhancing our understanding of pollutants and GHG emissions from space and supports policy-making. Here we summarize the recent research advances regarding the role of SRS in monitoring pollutants and GHG emissions in China, focusing on three key applications: 1) pollutant emission tracking, 2) carbon emission quantification, and 3) synergistic monitoring of pollution-carbon interactions. Current analysis identifies substantial progress achieved over the past decades, but critical limitations remain. From a scientific perspective, both ground observation networks and professional sensors are lacking for improving retrieval accuracy. Moreover, artificial intelligence (AI)-based data-mining theory is inadequate. From a practical perspective, high-resolution satellite provides insufficient coverage for nationwide carbon monitoring and data deficiencies hinders intelligent monitoring systems. To address these challenges, we provide the following strategic priorities: 1) establishing integrated Space-Air-Ground observation systems, 2) accelerating the deployment of next-generation monitoring satellites and developing related retrieval algorithms, and 3) developing AI-based monitoring systems through multisource data integration. This review enhances the understanding of SRS applications and provides directions for future research aimed at mitigating pollutant and GHG emissions, thereby supporting China’s dual carbon goals and sustainable development worldwide.

Rocky desertification is a major ecological issue in karst regions of China. Scientifically determining the minimum quadrat area for vegetation surveys is crucial for assessing ecological restoration effectiveness. We set up one broad-leaved forest plot and one shrub forest plot in Lechang City, a typical rocky desertification area in nor-thern Guangdong Province. The size of each plot was 100 m×100 m. We conducted community survey and constructed the database of species composition. We used the nested sampling method for secondary sampling statistics of vegetation, and established a species-area curve. The fitting effects of power function, logarithmic function, and logistic model were compared to analyze the relationship between sampling area and species composition and diversity. The results showed that the power function was the best-fitting model (R2>0.996). To include 50%-90% of the species, the minimum required plot areas were 2105-7511 m2 for the broad-leaved forest and 2417-7886 m2 for the shrub forest, corresponding to side length of 50-90 m. As the area increased, estimated species richness approached the true values more closely, with reduced standard error. Moreover, the relative abundance of dominant species and diversity indices in shrub communities were more sensitive to changes in plot size. For example, the relative abundance of Loropetalum chinense increased markedly from 34% to 54% in the shrubland. The relative abundance of dominant species Castanopsis jucunda in the broad-leaved forest remained stable at around 29% (variation ≤±3%). Given the co-occurrence of vegetation sparsity and patchiness in rocky desertification areas, we recommended that sampling plot size should be rationally set based on community type and research objectives in vegetation surveys to improve sampling representativeness and assessment accuracy.

Digital elevation models (DEMs) are recognized as fundamental to numerous geospatial applications, yet the spatial resolution of widely available DEMs is often insufficient for fine-scale analysis. Deep learning-based super-resolution (SR) has been developed as a powerful technique for enhancing DEM resolution. However, challenges remain in modeling complex, long-range topographic dependencies and in effectively fusing guidance information from auxiliary data, such as high-resolution (HR) optical imagery, which can result in the loss of fine details and the generation of geomorphologically inconsistent surfaces. In this study, an attention-transformer-guided terrain SR (ATTSR) is proposed for high-fidelity, image-guided DEM SR. The ATTSR framework is designed with a dual-branch Swin Transformer backbone to capture global spatial context, and a novel topographic-aware attention (TAA) module is introduced for the adaptive fusion of image and DEM features. Furthermore, a terrain gradient constraint is incorporated into a collaborative loss function to explicitly preserve topographic structure. The performance of ATTSR is evaluated on three diverse datasets for SR tasks from 30 to 10 m and from 90 to 30 m. Compared to state-of-the-art methods, the elevation root-mean-square error (RMSE) is reduced by 7%-36%, with corresponding improvements observed in slope and aspect fidelity. These results demonstrate that ATTSR provides a state-of-the-art solution for image-guided DEM SR, advancing the generation of accurate, large-scale HR terrain products.

Surface energy balance (SEB) models are widely employed for remote-sensing-based evapotranspiration estimation. A critical parameter in most SEB models is the surface temperature of wet or dry boundaries where sensible heat (H) or latent heat (LE) equals 0, which is difficult to measure or estimate. The wide application of SEB models is seriously limited due to this challenge. Therefore, this study introduces 'critical canopy temperature ( Tcc)', defined as the canopy temperature at which LE equals 0, corresponding to the dry boundary in SEB models. We develop a physics-constrained machine learning (ML) model (hybrid model) that conserves the SEB equation to predict Tcc using meteorological measurements from 103 eddy-covariance (EC) stations combined with remote-sensing data. The predicted Tcc is integrated into the Surface Energy Balance Algorithm for Land (SEBAL) model to replace the dry boundary, thereby to improve the estimation of LE estimation. Results demonstrate that the hybrid model effectively captures canopy temperature anomalies during stomatal closure and achieve better generalization than pure ML approaches in LE estimation, particularly under extreme conditions. Compared with conventional dry-boundary selection scheme without SEB constraints, incorporating Tcc significantly improve SEBAL performance, reducing the root mean square error for LE from 119.33 to 81.71 W m-2 against EC observations (at 31.52% reduction). At regional scales, the hybrid model enables pixel-level estimation of Tcc, addressing the long-standing challenge of dry-boundary underrepresentation. Overall, the Tcc hybrid model provides a robust and accurate framework for predicting theoretical dry-boundary temperatures while conserving the SEB, supporting improved monitoring of vegetation physiological status and enhancing the accuracy of SEB models.

Drought propagation from meteorological to soil drought marks a critical phase in regulating vegetation water-carbon dynamics, yet the response trajectories of water-use efficiency (WUE) during these events remain poorly understood. Here, combining global flux tower observations with simulations from Earth System Models (ESMs), we quantified the spatiotemporal patterns of drought propagation characteristics, identified WUE response trajectories for characteristic-specific droughts, and investigated their dominant drivers. We found that 58% of soil droughts follow meteorological droughts. Most sites experience drought propagation events with increasing intensity, faster propagation, and shorter intervals. Among all identifiable trajectories, nonmonotonic patterns account for approximately 60%. WUE response trajectories generally follow continuous and nonmonotonic patterns, dominated by a rise-then-fall pattern. This pattern reflects a process in which vegetation functions regulated by stomatal behavior are initially stressed and then partially recover. Intra-site variability is extremely pronounced, mainly driven by event-specific thermal factors such as air temperature and net radiation. ESMs reproduce broad site-level prevalence of nonmonotonic patterns, but show discrepancies in the relative importance of drivers due to the coupling of different land surface models. These findings challenge the notion of fixed vegetation functional responses and highlight the dynamic variability of response trajectories in relation to event-specific characteristics, and provide concrete diagnostics to guide model improvements.

In early 2008, an extreme ice storm struck southern China, including the subtropical Guangdong Province, causing substantial ecological damage and economic losses. Previous evaluations of vegetation damage during this event primarily focused on the immediate physical structure damage caused by ice, overlooking the delayed physiological damage from extreme low-temperature stress, especially in adjacent non-frozen regions. Combining remotely-sensed (i.e., MODIS Gross Primary Productivity (GPP) and Enhanced Vegetation index (EVI)) and in-situ data, we conducted a more complete assessment of subtropical vegetation damage and recovery following the 2008 Chinese ice storm by considering time-lag effects. We found vegetation damage and subsequent recovery exhibited distinct spatial patterns correlated to ice storm severity. Assessments that accounted for time-lag effects were more aligned with ground truth, revealing that vegetation damage signal typically lagged the event onset by 1-2 months. The time-lag effect showed distinct patterns in non-frozen regions experiencing secondary low-temperature stress (without direct ice storm exposure). Physiological damage dominated these areas, reducing GPP by 62 %. In contrast, physical structural damage caused a comparatively smaller decline (51 %) in EVI. We also found a positive correlation between frozen time and the severity of vegetation damage, with 37 % of vegetation damaged in less frozen zone versus 70 % in severe frozen zone. Subsequent recovery of GPP and EVI to pre-ice storm conditions took 4-9 months, with GPP recovering faster than EVI, especially in severe frozen forests. Such positive correlation also existed between damage severity (or recovery time) and elevation and slope, but the pattern varied across different freezing zones. Our findings highlight the delayed physiological damage from extreme low-temperature stress and provide new insights into subtropical vegetation dynamics following extreme ice storms.

Evapotranspiration (ET) is a critical component of the hydrological cycle and surface energy budget, and its accurate measurement is essential for elucidating land-atmosphere interactions and the dynamics of water and energy exchange. The Bowen ratio energy balance (BREB) method is a widely utilized approach for observing ET, offering a more economical and less labour-intensive alternative to the eddy covariance (EC) technique, yet its accuracy in humid forested regions remains insufficiently studied. This study evaluates the performance of the BREB method in measuring ET in two subtropical humid forest sites in China (Dinghushan [DHS] and Qianyanzhou [QYZ]), using concurrent EC measurements at intraday and daily scales. The validity of the Bowen ratio (beta) was analysed first, with over 90% of days meeting a data validity rate of > 90%. Results show strong correlations between BREB- and EC-derived daily ET (R-2 > 0.74), yet systematic BREB overestimation persists (MAE: 0.76-0.79 mm/day), particularly during summer high-radiation periods. XGBoost-SHAP analysis reveals that net radiation (Rn) dominates ET discrepancies (feature importance: 0.33-0.37). High Rn values cause the eddy diffusivity ratio (K-h/K-w) to exceed unity or become negative when beta(EC) > 0, thereby overestimating BREB measurements. Conversely, precipitation (P) usually induces beta anomalies, resulting in underestimation of ET, especially in autumn-winter precipitation periods. During midday (11:00 AM-1:00 PM), the BREB method systematically overestimates 30-min mean ET by 0.05 mm (representing 33% overestimation) relative to flux tower measurements, whereas in evening periods (after 5:00 PM), it underestimates ET by similar to 0.025 mm (42% underestimation). These findings highlight the potential and limitations of the BREB method in humid forested regions, providing insights into its performance for measuring ET under varying climatic conditions.

Global warming significantly impacts forest growth. However, commonly used spatially interpolated gridded air temperature datasets may not fully capture these effects due to their coarse spatial resolution and because air temperature may not accurately reflect the conditions that influence the tree growth process. Although finer spatial resolution land surface temperature (LST) datasets may capture more detailed temperature variations, their potential to assess forest growth responses to global warming has not been thoroughly explored. We evaluated the performance of air temperature and LST datasets with various spatial resolutions, including Climatic Research Unit gridded Time Series (CRU), TerraClimate, the land component of the fifth-generation European ReAnalysis (ERA5-Land), and MODIS LST (MOD11A2), in capturing the relationships between tree radial growth and temperature variations across 555 sites in the Northern Hemisphere. Our results showed that the finer spatial resolution MOD11A2 significantly outperformed the widely used CRU air temperature in modeling tree radial growth, with mean and maximum temperatures increasing the coefficient of determination (R2) by 16.32 % and 18.14 %, respectively. This improvement was especially apparent in high-elevation areas where R2 increased by 35.70 % and 36.97 %. We suggested that commonly used spatially interpolated gridded air temperature datasets (e.g., CRU and TerraClimate) may underestimate the impact of rising temperatures on forest growth. Our findings highlight the necessity of integrating high-resolution LST to accurately model forest growth responses to global warming.

Study region: The study was conducted in the middle reach of the Heihe River Basin, located in the Hexi Corridor of Gansu Province, Northwest China. Study focus: Accurate partitioning of evapotranspiration (ET) into soil evaporation (LE) and plant transpiration (LT) is essential for water resource management, particularly in arid and semi-arid regions. However, multi-scale ET partitioning remains challenging due to landscape heterogeneity. In this study, we applied the three-temperature (3T) model, a resistance-free method requiring minimal inputs, to partition ET over the heterogeneous oasis-desert landscape using aerial (3 m), ASTER (90 m), and MODIS (1000 m) thermal remote sensing data. The model's performance was validated by isotope-based measurements and compared across multi-scales. New hydrological insights for the region: The 3T model showed good agreement with isotope-based measurements in oasis croplands (MAE = 3.0 %). A key contribution of this study is demonstrating the consistent performance of the 3T model across three spatial resolutions. While finer-resolution data captured greater spatial variability in LE and LT, mean values remained relatively stable across scales. The strong consistency in LE and LT values between aggregated high-resolution and native coarse-resolution images (R-2 = 0.59-0.88, MAE < 50 W m(-2)) highlights the potential of the 3T model for regional and global ET assessments using moderate-to coarse-resolution satellite data.

Water use efficiency (WUE) is a critical ecosystem function and a key indicator of vegetation responses to drought, yet its temporal trajectories and underlying drivers during drought propagation remain insufficiently understood. Here, we examined the trajectories, interdependencies and drivers of multidimensional WUE metrics and their components (gross primary production (GPP), evapotranspiration, transpiration (T), and canopy conductance (Gc)) using a conceptual drought propagation framework. We found that even though the carbon assimilation efficiency per stomata increases during drought, the canopy-level WUE (represented by transpiration WUE (TWUE)) declines, indicating that stomatal regulation operates primarily at the leaf level and cannot offset the drought-induced reduction in WUE at the canopy scale. A stronger dependence on T and TWUE indicates that the water-carbon trade-off relationship of vegetation more inclines toward water transport than carbon assimilation. Gc fails to prevent the sharp decline in GPP during drought and has limited capacity to suppress T, as reflected by the reduction magnitude and the threshold (the turning point at which a component shifts from a normal to drought-responsive state). The primary drivers of the water-carbon relationship under drought propagation include vapor pressure deficit and hydraulic traits. Among plant functional types, grasslands show the strongest water-carbon fluxes in response to drought, whereas evergreen broadleaf forests exhibit the weakest response. These findings refine our comprehensive understanding of multidimensional ecosystem functional dynamics under drought propagation and enlighten how the physiological response of vegetation to drought affects the carbon and water cycles.

It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving thermal infrared (TIR) temperature from heterogeneous surfaces with varied LSE, such as those covered in vegetation. Corrections can be made using the Normalized Vegetation Index (NDVI). However, commercial thermal imagers provide only red (R), green (G), and blue (B) bands without a near infrared band so NDVI cannot be calculated on the same instrument and is commonly not available. We propose an alternative method to estimate LSE using RGB-based vegetation index. Thereafter the estimated LSE was used to correct the TIR temperature derived from the fixed LSE. An experiment was conducted to validate the proposed correcting method. The results show that 1) the corrected LST values were closer to the ground truth, with a mean absolute error (MAE) of 0.41 ± 0.34 °C, whereas the MAE was 0.75 ± 0.56 °C for the uncorrected LST; 2) the more heterogeneous the surface, the greater the difference between the corrected and uncorrected LST values, indicating the necessity of LST correction when applying thermal imagers over heterogeneous surface.

China leads in the greening of the world, with a nearly doubled increase in its forest area since the 1980 s revealed by the National Forest Inventory (NFI). However, a significant challenge persists in the absence of consistent and reliable remote sensing data that align with the NFI, hindering a comprehensive understanding of the spatiotemporal patterns of terrestrial ecosystem changes driven by afforestation and reforestation efforts over recent decades in China. Moreover, conventional binary thematic maps and land use and land cover (LULC) maps encounter difficulties in providing a thorough assessment of canopy cover at the subpixel level and trees extending beyond officially designated forest boundaries. This limitation creates substantial gaps in our comprehension of their invaluable contributions to ecosystem services. To confront these challenges, this study presents a systematic framework integrating time-series Landsat satellite imagery and random forest-based ensemble learning techniques. This framework aims to generate China's inaugural annual tree cover dataset (CATCD) spanning from 1985 to 2023 at a 30 m spatial resolution. Evaluation against multisource reference data shown high correlations ranging from 0.70 to 0.96 and reasonable RMSE values ranging from 5.6 % to 25.2 %, highlighting the reliability and precision of our approach across different years and data collection methodologies. Our analysis reveals that China's forested area has doubled, expanding from 1.04 million km2 in 1985 to 2.10 million km2 in 2023. Notably, 33 % of this growth can be attributed to a shift from non-forest to forest land categories, primarily observed in the three-north and southwest regions. However, the majority, contributing 67 %, results primarily from crown closure in central and southern China. This realization underscores the limitations of conventional binary thematic maps and LULC maps in accurately quantifying forest gain in China. Furthermore, China's tree population structure has undergone a transformative shift from 83 % forest trees and 17 % non-forest trees in 1985 to 92 % forest trees and 8 % non-forest trees in 2023, signifying a transition from afforestation to established forests. Our study not only enhances the understanding of tree cover variations in China but also provides valuable data for ecological investigations, land management strategies, and assessments related to climate change.

Knowledge of spatio-temporal patterns of global land surface evapotranspiration (ET) is essential for understanding the exchanges of energy, water and carbon between the earth surface and atmosphere and responses to human activities, climate changes, and extreme weather events. This paper comprehensively reviewed accuracies and spatio-temporal patterns of 25 state-of-the-art global datasets of land surface ET from 2001 to 2018, where the global and/or local distributions of annual mean, interannual variability, and annual trend for both ET and its components [canopy transpiration (Ec), soil evaporation (Es), and interception evaporation (Ei)] were investigated. Overall, 23 out of the 25 ET products exhibited good consistency with monthly eddy covariance observations at 83 globally distributed sites, with the root mean square error (RMSE) ranging from similar to 19 mm/month to similar to 29 mm/month and the coefficient of determination (R-2) ranging from 0.45 to 0.76. Comparatively, almost all ET products were in good agreement with the water balance-based observations at 137 large basins worldwide, with the R-2 of >0.60 and the RMSE ranging from similar to 200 mm/yr to similar to 260 mm/yr. In general, remote sensing-based, machine learning-based, and hybrid-based ET products generally agreed better with site observations than reanalysis-based products. Global annual ET ranged from 530 mm/yr to 700 mm/yr with the ensemble mean of 628 +/- 44 mm/yr (excluding areas of the urban, barren, wetlands, permanent snow and ice, and water bodies). Most products captured consistent global spatial distributions of annual mean, interannual variability, and annual trends of land surface ET and its components. Both mean annual ET and its components generally decreased with increasing latitudes though with an increasing trend in time. Interannual variability also decreased with increasing latitudes and with an increasing trend in time, but only in the Northern Hemisphere; these signals were not clear in the Southern Hemisphere. Most of the 25 products presented unimodal shapes of frequency curves, and displayed a sequenced multi-year mean of seasonal ET of JJA > MAM > SON > DJF. The Ec exhibited the largest mean annual value, in most areas followed successively by the Es and Ei. The ET products displayed a relatively high consistency in the spatial distribution of the Ec/ET with higher estimates of Ec/ET in the Southern Hemisphere than in the Northern Hemisphere. This up-to-date review could benefit the understanding of the states and advances of global ET datasets, afford scientific instruction for the development and improvement of ET models and products, and serve as a basis for the choice of ET datasets for specific use.

The grapevine is one of important economic crops.Accurate measurements of daily grapevine evapotranspiration(ET)measurements are essential for improving the understanding of crop water demand and optimizing water consumption efficiency.Unfortunately,there is a relative scarcity of daily grapevine ET values in China.This dataset presents daily ET values obtained through using the Bowen ratio energy balance method and the surface renewal method in Lanshan Jiaozi Vineyard on the outskirts of Yinchuan City,spanning the period from 2017 to 2019.Abnormal Bowen ratio values have been removed during the estimation of ET and the quality of each daily ET measurement has been thoroughly assessed.The dataset is freely available,with the primary goal of establishing a foundation for the efficient use of agriculture water resources.Additionally,it is aimed to assess the applicability of the surface renewal method in a highly heterogonous vineyard.

Climate change has critical adverse impacts on human society and poses severe challenges to global sustainable development. Information on essential climate variables (ECVs) that reflects the substantial changes that have occurred on Earth is critical for assessing the influence of climate change. Satellite remote sensing (SRS) technology has led to a new era of observations and provides multiscale information on ECVs that is independent of in situ measurements and model simulations. This enhances our understanding of climate change from space and supports policy-making in combating climate change. However, it remains challenging to remotely retrieve ECVs due to the complexity of the climate system. We provide an update on the studies on the role of SRS in climate change research, specifically in monitoring and quantifying ECVs in the atmosphere (greenhouse gases, clouds and aerosols), ocean (sea surface temperature, sea ice melt and sea level rise, ocean currents and mesoscale eddies, phytoplankton and ocean productivity), and terrestrial ecosystems (land use and land cover change and carbon flux, water resource and hydrological hazards, solar-induced chlorophyll fluorescence and terrestrial gross primary production). The benefits and challenges of applying SRS in climate change studies are also examined and discussed. This work will help us apply SRS and recommend future SRS studies to mitigate and adapt to global climate change.

精准的蒸散发观测是解释和理解水与能量交换过程及其机理的重要途经,亦可为蒸散发遥感产品验证提供数据支持.基于表面更新理论的高频温度法对蒸散发物理过程的描述,不同于经典的涡动相关法,是一种新兴的蒸散发观测手段,其观测精度媲美涡动相关系统,且高频温度观测系统成本较低(低于波文比系统),在欧美等地获得推广应用.通过梳理高频温度法理论基础与国外近30年的研究进展,结合国内初步观测与研究成果,探讨了高频温度法应用中面临的问题与挑战,为推动国内蒸散发观测方法多源化并从多角度理解蒸散发过程及其机理提供方法支撑.

新工科人才培养是高等教育的重大工程.课程思政是培养德才兼备的社会主义接班人、落实立德树人的重要途径.针对新工科学科交叉融合程度高、新兴学科体系尚不完善,相应的本科专业课程教学内容设置与课程思政高效高质融合面临重大挑战,该文以中山大学水文与水资源工程专业生态水文学为例,提出新兴交叉学科专业课程教学内容与思政元素凝练设计的思路与方法,即在梳理生态水文学发展与教学现状基础上,从学科框架体系与核心研究内容、学科发展前沿与生态文明建设等国家战略需求出发,结合专业定位与授课教师相关的科学研究,自然融入党的生态发展观、生态战略布局等思政元素,设置具有系统性、前沿性和实践性的教学内容.最后探讨该课程在高等教育大类培养趋势下未来教学内容改革方向,以期为新工科中新兴交叉学科本科教学内容设置、高质量课程思政与教学改革提供参考.