Forest ecosystem photosynthesis is primarily driven by hydrothermal conditions. However, the effects of hydrothermal integration and synergy on carbon exchange across forest ecosystems are still not clear. We examined the divergence of carbon exchange over 16 forest ecosystems in eastern China. To explore the controls of hydrothermal change on gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem productivity (NEP), we developed two indices for hydrothermal integration (TP) and hydrothermal synergy (D) based on the copula function. Compared with traditional indices such as the water and thermal product index (K), aridity index (AI), and standardized precipitation evapotranspiration index (SPEI), TP and D demonstrated higher sensitivity and applicability in capturing seasonal and spatial variations in hydrothermal conditions. Vapor pressure deficit (VPD), soil water content (SWC), and AI responded nonlinearly to TP and D, with coordinated hydrothermal conditions enhancing SWC and uncoordinated or scarce conditions increasing drought risk. TP and D explained over 80% of the variability in GPP, Re, and NEP, which better captured hydrothermal controls on carbon exchange than temperature and precipitation alone. Carbon fluxes peaked at TP approximate to 1 and D slightly above 0, indicating that moderately water-dominated hydrothermal synergy provided optimal conditions for photosynthesis and respiration. Random forest analysis revealed that SWC was the primary driver of GPP, Re, and NEP, followed by D for GPP and NEP, indicating that forest carbon exchange is mainly regulated by soil water availability and atmospheric hydrothermal synergy. This study clarifies how hydrothermal conditions impact on carbon exchange in forest ecosystems and provides insights into assessing forest responses to climate change.

Plant spring phenology is influenced by multiple climatic variables. Climate warming has significantly advanced spring phenophases over recent decades. However, the differences in responses to climatic variables among various life forms of plants remain unclear. We analyzed spring phenophases of different life forms by integrating approximately 20 years of spring phenological observation data for 82 species from 29 sites in China from 1964 to 2022, combined with contemporary climate data. The results indicated that the correlation between spring phenophases and temperature of trees and shrubs was stronger than that of herbs. Most of the plant phenophases were significantly correlated with temperature, but showed no remarkable correlation with sunhours or humidity. However, some trees and shrubs displayed significant correlations with precipitation. Spring phenophases of trees and shrubs were more sensitive to mean temperature (ST = -3.62 and -3.59 days degrees C- 1, respectively), whereas those of herbs was more sensitive to minimum temperature (ST = -1.91 days degrees C- 1). Spring phenophases of trees and shrubs demonstrated higher SH values (0.48 and 0.43 %RH- 1, respectively), while those of herbs showed lower SH (0.31 days %RH- 1). Precipitation and sunhours had a significant impact on ST and SH of trees and shrubs. Wind speed had a greater effect on ST and SH of trees than on those of shrubs. In the northern sites, there were significant differences in the sensitivity of phenology to mean temperature between trees and herbs. Spring phenophases for herbs showed the strongest response to maximum temperature compared with those for trees and shrubs.

Abstract Background Plant phenology plays an important role in regulating carbon and water cycles in terrestrial ecosystems. Rising temperatures have a profound impact on vegetation phenology in the northern hemisphere, advancing spring phenology and delaying autumn phenology. However, the effects of daytime and nighttime warming on spring phenology are not well understood. Methods We investigated the response of leaf unfolding date (LUD) to daytime and nighttime temperatures over past 30 years by a total of 4,320 LUD records, including 10 deciduous tree species and 2 shrubs at 12 sites in China. We also compared the divergence of temperature sensitivity of woody LUD between early leaf unfolding species and late leaf unfolding species. Results LUD was mainly regulated by preseason minimum temperatures other than preseason maximum temperatures. Compared to maximum temperatures, minimum temperatures had more significant effects on LUD across all species during 1983–1997. LUD for early leaf unfolding species and late leaf unfolding species was sensitive to minimum temperatures and maximum temperatures during 2000–2014, respectively. Daytime and nighttime warming led to the advancement of LUD, whereas the sensitivity of leaf unfolding to nighttime temperatures decreased from the period 1983–1997 to 2000–2014. Decreased chilling requirements slowed down the advancement of LUD. The day-night-temperature GDD (DNGDD) model had higher values of R 2 (0.93) and lower RMSE (6.33 days) compared to the threshold (R 2 = 0.72, RMSE = 13.84 days) and GDD (R 2 = 0.81, RMSE = 7.96 days) models. Conclusions The DNGDD model performed better on estimating woody LUD than the threshold and GDD models. This study highlights the different responses of LUD for early leaf unfolding species and late leaf unfolding species to daytime and nighttime warming, which will help us better understand plant phenological processes.

Crop production, nitrogen use efficiency (NUE), and environmental footprint are not only of great significance for ensuring food security, but also serve as key determinants for achieving the integrated governance of agricultural development and environmental protection. However, Iran is currently facing challenges such as production in an arid climate and on degraded land, low NUE, and associated ecological and environmental pollution. Current agricultural nitrogen (N) management research is mostly limited to single crops or dimensions, leaving a gap in integrated multi-crop, multi-dimensional spatiotemporal analyses and grid-scale high-resolution spatial assessments of regional heterogeneity. Therefore, from the perspectives of food, resources, and the environment, this study systematically assessed the sown area, yield, N application rate, NUE, N surplus, and greenhouse gas emissions (GHG emissions) of six major crops (wheat, rice, barley, maize, sugarcane, and cotton) in Iran for the years 2000, 2010, and 2020. The aim was to assess the current status and spatiotemporal evolution of cropland N management in Iran. The results of this study indicate that the total N application rate in Iranian cropland exhibited an overall upward trend from 2000 to 2020, increasing from 1.095 & times; 106 t to 1.1937 & times; 106 t over this period. The NUE improved in some regions but remained generally low, increasing from 31.7% to 41.8%. Provinces in northern and southern Iran were characterized by high N application rates, low NUE (20-40%), substantial N surplus accumulation, and high GHG emissions. The multi-dimensional comprehensive assessment framework proposed in this study provides a scientific basis for N management in regions aiming for coordinated governance of food security and the ecological environment.

Frequent typhoon landfalls inflict substantial losses of life and property. Their genesis, rapid intensification, and track changes remain three key challenges in typhoon research. High spatiotemporal resolution in situ observations spanning the whole typhoon life cycle are urgently required. We propose deploying the Marine Weather Observer (MWO), a mobile oceanic observing system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, for sustained networked typhoon observations. The experiment will deploy five to seven solar-powered MWOs in the South China Sea to measure marine surface and boundary layer meteorological parameters. This networked approach enables real-time in situ observations of typhoon internal dynamics and environmental conditions over the open ocean, supporting data assimilation and validation of numerical forecast models to improve predictions of typhoon tracks, intensity, and associated severe weather impacts.

Vegetation plays an important role in carbon sequestration in terrestrial ecosystems and is affected by climate change and human activities. As a major factor affecting vegetation growth, the role of soil moisture in the impacts of climate change on vegetation is not well understood. Therefore, the effects of climate change on net primary productivity (NPP) may be underestimated. In this study, we analyzed the spatial distribution of NPP and land use degree comprehensive index (LDCI) in China from 2001 to 2020. The actual and relative contributions of climate change and human activities to NPP variation were explored. The findings indicated that NPP trended upward in 73.12%, 66.78%, and 81.34% of woodland, grassland, and cropland areas, respectively. Most of the woodland and grassland showed a decreasing trend in LDCI, while 48.63% of the cropland showed an increasing trend. The positive joint effects of climate change and human activities increased the NPP of woodlands, grasslands, and croplands by 42.83%, 53.49%, and 45.22%, respectively. Human activities (55.04%) contributed more to NPP than did climate change (44.96%). Analyzing the response of NPP (woodlands, grasslands, and croplands) to climate change and human activities in China is conducive to taking more targeted measures for different land use types to increase carbon sinks in terrestrial ecosystems.

A comprehensive understanding of carbon emissions (CE) spatiotemporal dynamics is a prerequisite for achieving carbon dioxide emissions peak and carbon neutrality aim and taking measures to mitigate climate warming. Although the impact of urban morphology on CE is confirmed by prior studies, the differences of CE for each sector in constructing the CE estimation model were ignored leading to CE datasets with relatively coarse resolution. Here, we propose a new scheme to improve the resolution of CE products based on Local Climate Zones (LCZ) and landscape ecology theory using novel extra proxy data. We classify LCZ using the Google Earth Engine (GEE) platform, quantify urban morphology parameters, and analyze the correlation of CE with urban morphology, population density (Pd), and nighttime light imagery (NTL). Using a CE estimation model to generate 500 m CE data and explore its spatiotemporal dynamic. The results indicate that the LCZ classification accuracy is acceptable (OA & oline; = 86.59 %, Kappa coefficient > 80 %). CE is closely related to Pd, the Connectivity Index, the Largest Patch Index, the Splitting Index, and the Contagion Index. The simulation accuracy of residential and service CE (R-2 = 0.99) and industrial CE (R-2 = 0.83) is well. CE in the BTH shows an aggregation feature, and the gravity center of residential and service CE shifts southeastward, while the gravity center of industrial CE shifts eastward. This study provides a reference and guidance for estimating CE datasets and formulating carbon reduction strategies.

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m−2 under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography.

Coastal ecosystems, particularly mangroves, are essential for ecological stability and human livelihoods, yet they face significant degradation from natural and anthropogenic pressures. This study focuses on the Chaungkaphee Protected Public Forest (PPF) in the Tanintharyi region of Myanmar, which hosts diverse mangrove species critical for carbon storage. Between 2010 and 2020, mangrove forest cover in Myanmar decreased from 540,000 ha to 431,228 ha, resulting in a loss of 108,772 ha. This decline is primarily attributed to illegal logging and agricultural expansion. Our research aims to assess the structural characteristics, biomass, and carbon storage potential of mangrove ecosystems within the Chaungkaphee PPF. Field data collected in early 2024 applied non-destructive sampling methods to gather information on tree structure, species composition, and soil carbon stocks. We identified six dominant mangrove species, with Rhizophora apiculata Blume showing the highest biomass and carbon storage potential. The total biomass was measured at 493.91 Mg ha⁻1, yielding a carbon stock of 218.76 Mg C ha⁻1. Soil carbon assessments revealed an average organic carbon stock of 921.09 Mg C ha⁻1, underscoring the vital role of soil in carbon sequestration. Our findings highlight the significant contribution of mangrove ecosystems to climate change mitigation, emphasizing the urgent need for effective conservation strategies and community involvement in restoration efforts. This study enhances the understanding of mangrove resilience and sustainability, advocating for the protection of these crucial ecosystems amidst ongoing environmental challenges. By recognizing the ecological functions and services provided by mangroves, we can better address the threats they face and promote their restoration for future generations.

Vegetation phenology serves as an important indicator for climate change and plays a crucial role in affecting the terrestrial water, energy, and carbon cycles. The green chromatic coordinate (GCC) obtained from digital repeat photographs has been widely applied in estimating phenology from the perspective of greenness, while the performance of satellite derived GCC is not well understood. We used flux tower GPP from seven deciduous broadleaf forest (DBF) and three grassland (GRA) sites over the Northern Hemisphere. The aim was to compare phenological events with GCC (obtained from digital repeat photographs and satellite remote sensing (GCCMODIS)) and the enhanced vegetation index (EVI). Meanwhile, we also explored the performance of these three indices in simulating GPP utilizing the light use efficiency (LUE) model at the DBF and GRA sites. Phenology retrieved by GCC, GCCMODIS, and EVI was all significantly correlated with GPP-estimated values at all sites (P < 0.001). It indicates the comparable performance of GCC, GCCMODIS, and EVI in estimating phenological events. The RMSE values between the GPP and three indices-estimated phenological events revealed that the three indices excelled in estimating the start of growing season (SOS) compared to the end of growing season (EOS) and the length of growing season (GSL). In terms of GPP estimation performance, the R2 values of GCCMODIS and EVI-estimated GPP increased by 2 % and 1 %, respectively, compared to GCC-simulated GPP. Meanwhile, the RMSE values for GCCMODIS and EVI reduced by 0.08, and the bias values were reduced by 0.06 and 0.12, respectively. This study showed that GCC obtained from satellite remote sensing data could be utilized as an effective tool in extracting phenology and has a great potential to estimate GPP, at least across the DBF and GRA regions.

Utilizing potential evapotranspiration (PET) for the estimation of actual evapotranspiration (AET) in cropland is highly valuable for determining agricultural water requirements and developing irrigation schedules. Discrepancies between anticipated and actual PET values may arise due to the limitations in previous reference standards employed for evaluation. A comprehensive evaluation of the accuracy of the PET models in cropland was conducted in this study. Non-water-stressed days with evaporation fraction (EF) exceeding the 95th percentile threshold were selected from cropland sites in the FLUXNET Tier 1 database as the basis for calibrating and validating 22 PET calculation models at the daily scale and 19 at the sub-daily scale. Results indicated that the Penman (1948), Penman (1963), and all radiation-based models showed strong performance, with R-2, RMSE, and NSE of 0.82-0.89, 0.58-0.86 mm day- 1 , and 0.54-0.77 at the daily scale, and 0.73-0.81, 0.04-0.05 mm 0.5 h- 1 , and 0.62-0.72 at the sub-daily scale, respectively. Four recently developed radiation-based models have exhibited remarkable accuracy on a daily basis. The relative model errors (RMSE/MEAN) tended to decrease as net radiation (Rn), temperature (Ta), and vapor pressure deficit (VPD) increased at both daily and sub-daily time scales. The models displayed strong accuracy in estimating daily PET when Ta > 15 degrees C or 0.4 < VPD < 1.0, with RMSE/MEAN values ranging from 0.10 to 0.22 and R-2 values ranging from 0.76 to 0.89. Higher accuracy in sub- daily scale PET estimates was achieved when Ta > 15 degrees C or 0.4 < VPD < 1.6, with RMSE/MEAN of 0.24-0.43 and R-2 of 0.54-0.85, respectively. Additionally, the time lag between PET and Rn, Ta, and VPD increased as Rn, Ta, and VPD increased at the sub-daily scale, which may contribute to the reduced precision of sub-daily PET estimates compared to daily scale estimates. These findings suggest that PET models with calibrated parameters have the potential to serve as the basis for estimating cropland AET, and can provide direction for future model improvement.

Cloudiness influences gross primary productivity and evapotranspiration in terrestrial ecosystems by increasing diffuse radiation and improving the photosynthesis of shaded leaves. However, the effect of cloudiness on energy partitioning in terrestrial ecosystems is not well understood. Using latent heat (LE) and sensible heat (H) datasets observed by the eddy covariance systems and meteorological variables, the regulation of biophysical factors on energy partitioning under different sky conditions was investigated at 6 plantation ecosystems (2 deciduous broadleaf and 4 coniferous plantations) in eastern China from 2019 to 2021. The averages of evaporative fraction (EF) during the growing season were 0.34±0.06–0.50±0.04. The EF values increased by 16−49% on cloudy days, which was attributed to the lower decrease in LE compared to H. On the half-hourly scale, EF was mainly driven by air temperature (Ta), relative extractable soil water (REW) and clearness index (CI) (i.e., a metric to quantify cloudiness). For plantation ecosystems, CI had an indirect effect on EF mainly through net radiation (Rn). Moreover, the positive effect of cloudy sky conditions on EF was higher under low Ta, vapor pressure deficit (VPD) and REW conditions. The increment of EF on cloudy days was weaker at high normalized difference vegetation index, which may be explained by higher Ta and VPD limiting canopy transpiration during the mid-growing season. Cloudiness can also influence energy partitioning by regulating stomatal behavior in plantation ecosystems. These results contribute to understanding the drivers of energy partitioning in response to cloudiness in plantation ecosystems.

Vegetation can reduce the adverse effect of climate change. Under the frequent droughts and global warming, it is critical to clear vegetation dynamics and response to climatic variables. However, it is unclear how drought and climatic variables affect vegetation dynamics. In this study, we used the vegetation condition index (VCI) to examine vegetation dynamics in China between 2001 and 2020. The average annual value of VCI had increased during the period of 2001 to 2020, and 90.63% of the study regions had a rising trend in VCI. A large portion of the area had a 3-month time lag between VCI and temperature and precipitation. The time-lag response of VCI to drought was mainly 9 months, with a cumulative effect on drought of 1 month and 12 months. The VCI of evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), deciduous broadleaf forests (DBF), and mixed forests (MF) in northern China displayed long time-lag response to drought. Except for DBF in the north and DNF in the central south regions, the cumulative effect of drought on other forest VCI was great in northern China. Except for DNF, DBF, and MF in the central south and DBF in the southwest regions, drought had less time-lag effects on VCI for ENF, evergreen broadleaf forests (EBF), DNF, and MF in southern China. The cumulative effect of drought on VCI of forests in southern China was short. The responses of various types of the grassland and shrubland to drought were similar as those of forests. However, the time-lag effect of drought on grassland and shrubland VCI was shorter than that of forests, making the grassland and shrubland more vulnerable to the short-term cumulative effects of drought. The result of this study can help us understand how the vegetation responds to climate change and drought under global warming scenarios.

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

Solar-induced chlorophyll fluorescence (SIF) has been widely used in different area, such as estimating forest gross primary productivity (GPP), monitoring drought, estimating evapotranspiration and tracking vegetation phenology. Based on the Global OCO-2 SIF product (GOSIF) and the standardized precipitation evapotranspiration index (SPEI) at different temporal scales (1, 3, 6, and 12 months), we explored the responses of forest photosynthesis to dry-wet change over eastern monsoon China during 2001-2021. The results showed that there were differences in drought intensity and frequency among forests in different geographical regions. Forests in the North China and East China experienced higher drought intensity, while the southern part of Northwest China had lower drought intensity. Forests in the North China experienced more frequent droughts, while the Northeast China and Southwest China had lower drought frequencies. About 74.1% of the area where forest GOSIF was significantly and positively correlated with SPEI, and the response of photosynthesis to SPEI showed the most pronounced at the 1-month scale. In different geographical regions, photosynthesis in the Northeast China was the most sensitive to SPEI, whereas in the North China it was the least sensitive. The drought resistance of forests in the southern part of Northwest China exhibited the strongest, while in the Northeast China it was the weakest. Meanwhile, in different forest types, deciduous broad-leaved forests were the most sensitive to SPEI, followed by mixed forests, evergreen broad-leaved forests, evergreen needle-leaved forests and deciduous needle-leaved forests. Evergreen needle-leaved forests had the strongest resistance to drought stress, followed by deciduous needle-leaved forests, evergreen broad-leaved forests, deciduous broad-leaved forests and mixed forests. During the growing season (May-September), the response sensitivity of photosynthesis to SPEI was strongest in June and weakest in July. Dry-wet changes at the 1 and 3-month scales exerted the main impact on photosynthesis, while in the mid-season (June-August) and late season (September), the impact of dry-wet changes at the 6 and 12-month scales on photosynthesis increased.

The rice-wheat rotation system is a major agricultural practice in China as well as an important source of greenhouse gas (GHG) emissions. In this study, the developed mid-infrared laser heterodyne radiometer (MIR-LHR) was used for the remote sensing of atmospheric CH4 and N2O concentrations above the rice-wheat rotation system. From April 2019 to May 2022, the atmospheric column concentrations of CH4 and N2O above the rice-wheat rotation system were continuously observed in Hefei, China. The peak values of the N2O column concentration appeared 7~10 days after wheat seasonal fertilization, with additional peaks during the drainage period of rice cultivation. During the three-year rice-wheat crop rotation cycle, a consistent trend was observed in the CH4 column concentrations, which increased during the rice-growing season and subsequently decreased during the wheat-growing season. The data reveal different seasonal patterns and the impact of agricultural activities on their emissions. During the observation period, the fluctuations in the CH4 and N2O column concentrations associated with the rice-wheat rotation system were about 40 ppbv and 6 ppbv, respectively. The MIR-LHR developed for this study shows great potential for analyzing fluctuations in atmospheric column concentrations caused by GHG emissions in the rice-wheat rotation system.

Aims Under the background of global warming,the plantation of Pinus tabuliformis is highly sensitive to climate change.However,the impacts of climate change on the radial growth of the earlywood and latewood are still less understood.Therefore,it is important for predicting the productivity and vegetation dynamics of the plantations to understand the responses of radial growth of earlywood and latewood to climate change. Methods Based on dendrochronology,we established the standard chronology of earlywood and latewood tree rings from five sampling sites in the northern and northwest China.Furthermore,we analyzed the relationship between annual ring width index and climate variables,and investigated the relative influence of climate variables on the growth of P.tabuliformis. Important findings Air temperature showed a significant increasing trend from 1980 to 2020,and regional climate was becoming warmer and drier.The ring widths of P.tabuliformis at Kangle(KL),Tianshui(TS)and Lingshou(LS)were higher than those at Xunyi(XY)and Chunhua(CH).Compared with CH and XY,the total ring width and earlywood width at TS,KL and LS showed a smaller decreased trend.The chronology of earlywood of P.tabuliformis showed positive correlations with precipitation in last September and during the pre-growing season of current year.The latewood largely showed a positive correlation with air temperature throughout the whole year.The radical growth in P.tabuliformis was positively correlated with averaged air temperature and maximum air temperature from March to April,especially in LS,KL and TS.It is indicated that the radial growth of earlywood and latewood was significantly correlated with climate factors during the growing season.The responses of radial growth to climate variables between earlywood and latewood were different.The relative influence of air temperature on the width change of the latewood increased by 21.89％,8.63％,3.31％and 7.25％compared to the earlywood in LS,CH,TS and KL,respectively.The latewood was more sensitive to air temperature than earlywood.Therefore,considering the difference in response to climate change between early and late wood chronology is helpful for improving the quality of regional climate reconstruction in the future.

The performance of an active near-infrared laser heterodyne system using a supercontinuum light source as the light signal for trace species detection is experimentally demonstrated. The characteristics of the supercontinuum light source and the data processing methods used in heterodyne detection are described. The measurement of methane (CH4) and carbon dioxide (CO2) absorption spectra was carried out in the laboratory to evaluate the active laser heterodyne system, and high-resolution absorption spectra of CO2 and CH4 were recorded simultaneously. The stability of the active laser heterodyne system is analyzed using Allan variance analysis of long-term observation data. The active near-infrared laser heterodyne detection system demonstrated in this paper has great potential for the development of industrial parks' gas monitoring.

Laser heterodyne radiometer (LHR) exploits the heterodyne signal generated by solar radiation beated with local oscillator (LO) light to extract information on atmospheric species in the atmospheric column. Most of currently reported LHRs are affected by LO-induced relative intensity noise (RIN), which poses a challenge for high- sensitivity detection. In this work, a near-infrared RIN-suppressed LHR is proposed for simultaneous detection of water vapor and HDO in the atmospheric column. The operability of a home-made sun tracker can be obtained from coarse tracking and fine tracking, which offers an excellent opportunity for unattended observation. A tunable distributed feedback (DFB) diode laser centered at 1552.9 nm is employed as LO, where LO-induced RIN is suppressed by a semiconductor optical amplifier (SOA) operating in the dynamic gain saturation regime. By locking LO power and suppressing LO-induced RIN, the SOA-assisted LHR is dominated by 1.27 times shot-noise and the signal-to-ratio (SNR) of heterodyne signal is increased by 5 times. Based on atmospheric transmission spectra measured with the RIN-suppressed LHR in Hefei, China, the total column abundances of water vapor and HDO are retrieved to be 2050 ppm and 0.6 ppm, respectively, with uncertainty of 2 %.

Quantifying potential reductions in environmental impacts for multi-crop agricultural production is important for the development of environmentally friendly agricultural systems. To analyze the spatial differences in the potential reduction in nitrogen (N) use, we provided a framework that comprehensively assesses the potential of improving N use efficiency (NUE) and mitigating environmental impacts in Hubei Province, China, for multiple crops including rice, wheat, maize, tea, fruits, and vegetables, by considering N and its environmental indicators. This framework considers various sources such as organic N fertilizers and synthetic fertilizers, along with their respective environmental indicators. We designed different scenarios assuming varying degrees of improvement in the NUE for cities with a low NUE. By calculating the N rate, N surplus, N leaching, and greenhouse gas (GHG) emissions under different scenarios, we quantified the environmental mitigation potential of each crop during the production process. The results showed that when the NUE of each crop reached the average level in Hubei Province, the improvement in environmental emissions is favorable compared to other scenarios. The N rate, N surplus, N leaching, and GHG emissions of grain (cash) crops could be reduced by 25.87% (41.26%), 36.07% (38.90%), 49.47% (36.14%), and 51.52% (41.67%), respectively. Overall, improving the NUE in cash crops will result in a greater proportionate reduction in environmental impacts than that in grain crops, but grain crops will reduce the total amount of GHG emissions. Our method provides a robust measure to assess the reduction potential of N pollution and GHG emissions in multi-crop production systems.