Agricultural and Forest Meteorology最新文献

{"title":"Intensifying impacts of compound drought and heatwave events on water use efficiency in U.S. corn and soybean","authors":"Hua Yan, Yongfa You, Wenzhe Jiao, Naiqing Pan","doi":"10.1016/j.agrformet.2025.110873","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110873","url":null,"abstract":"The increasing frequency and intensity of compound drought and heatwave events pose a severe threat to agricultural water use efficiency (WUE), amplifying risks to global food security and water resource sustainability. While compound extremes can trigger unprecedented disruptions in agricultural water-carbon dynamics compared to single extreme events, their effects on WUE remain poorly understood. To address this gap, this study examines the spatiotemporal variations in U.S. corn and soybean WUE in response to compound drought and heatwave events from 1960 to 2018. Using superposed epoch analysis, we found that compound drought and heatwave events reduced WUE by 14.7% in corn and 11.3% in soybean compared to normal conditions. These compound events also resulted in greater reductions in yield and evapotranspiration than single drought or heatwave events. While the impacts of both single and compound extremes were generally short-lived, with WUE recovering in the following year, long-term trends reveal intensifying effects: compound drought and heatwave events have shown intensified adverse impacts on 37.5% of corn and 46.2% of soybean production areas. This intensification is closely linked to the role of temperature, which was consistently identified as the dominant climatic driver of WUE responses under drought, heatwave, and compound extremes. These findings underscore the urgent need to prioritize compound drought and heatwave events in agricultural impact assessments and develop targeted adaptation strategies to mitigate their intensifying effects on agricultural WUE.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"18 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diverging trends in plant phenology and productivity across European mountains in a warming world","authors":"Davide Andreatta, Nina Buchmann, Tommaso Jucker, Luca Belelli Marchesini, Michele Dalponte, Michele Scotton, Loris Vescovo, Damiano Gianelle","doi":"10.1016/j.agrformet.2025.110874","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110874","url":null,"abstract":"Global warming is affecting phenology and productivity of terrestrial ecosystems, with large implications for carbon cycling. However, how plant phenological trends are shifting in climatically heterogenous mountains, and how these trends affect ecosystem productivity remains unclear. Using moderate resolution satellite data (500 m), we analyzed differences in phenological trends and productivity between vegetation types and along elevation in Europe’s major mountain ranges between 2001 and 2023.End-of-season shifts outpaced start-of-season changes in broadleaved forests (+0.15 vs. -0.05 d y⁻¹), while patterns in natural grasslands were opposite (+0.03 vs. -0.23 d y⁻¹). The magnitude of these shifts varied significantly with elevation: grassland spring phenology consistently advanced more at high than at low elevations, while broadleaved forest spring phenology exhibited mountain range-specific elevation responses—advancing more at low than at high elevations in the Alps and in the Carpathians, but not in the Pyrenees and in the Scandinavian Mountains. Autumn phenology of broadleaved forests showed greater delays at high than at low elevations, likely due to spring and summer droughts. Climate anomalies, calculated as Z-scores across the 23-year time-series, predicted phenological anomalies well (max R² = 0.51), although trends in climate over 23 years and phenological variables were related only weakly (max R² = 0.27), suggesting that plants adjusted to long-term differently than to short-term climate change.Growing season length (GSL) was strongly coupled with productivity (max R² = 0.60), especially in “cold-limited” vegetation. Nonetheless, temporal trends in GSL and productivity were not related (R² < 0.03). In the last 23 years, GSL significantly increased in only 22 % of forest and 16 % of grassland pixels, but productivity in 20 % of forest and 53 % of grassland pixels. Our results suggested that factors beyond GSL affect ecosystem productivity, indicating that longer growing seasons will not necessarily translate into increasing productivity across European mountains.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"107 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving a parsimonious model for simulating urban tree transpiration using a water stress index based on within-canopy air temperature","authors":"Wenjing Yang ,&nbsp;Zhechen Zhang ,&nbsp;Xanthia Gleeson ,&nbsp;Huade Guan","doi":"10.1016/j.agrformet.2025.110867","DOIUrl":"10.1016/j.agrformet.2025.110867","url":null,"abstract":"<div><div>Urban trees play a pivotal role in urban ecosystems, effectively reducing urban surface temperature by transpiration-induced evaporative cooling. However, accurately estimating tree transpiration (<em>E</em>_c) in urban environments is challenging due to a lack of robust methods and the inherent complexities of soil moisture monitoring in root zones, especially during dry periods. In this study, a simplified mechanistic tree transpiration model (BTA, Buckley, Turnbull &amp; Adams) was improved by a new species-dependent normalized canopy temperature water stress index (NCTI), based on within-canopy air temperature (<em>T</em>_c) and reference temperature of an irrigated park (<em>T</em>_r). The performances of the new models (BTA-T_c) were tested using sap flow observations from two species (White Cedar: <em>Melia azedarach</em>; Grey Box: <em>Eucalyptus microcarpa</em>), with a total of seven trees in the City of Mitcham, South Australia. Results show that the BTA-T_c models reduced the overestimation of the BTA model noticeably during dry periods. Overall, the Nash–Sutcliffe efficiency (NSE) increase was 10 % for both White Cedar and Grey Box trees compared to the BTA model. Additionally, NCTI effectively captured continuous seasonal tree water stress patterns, determined by the morning air warming rate for White Cedar trees and by the afternoon average temperature for the Grey Box trees. The NCTI also revealed the benefit of a Water Sensitive Urban Design device in mitigating street tree water stress during the early dry season. Given its simple data requirements, the BTA-T_c model can be easily applied to other locations, species, or climates, offering a practical approach for monitoring urban tree water use and assessing green infrastructure performance under variable climate conditions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110867"},"PeriodicalIF":5.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic calibration of the Biome-BGC model with the PEST software to simulate the forest and farmland ecosystems of the Qinling Mountains in China","authors":"Kaiyuan Gong ,&nbsp;Zhuo Huang ,&nbsp;Linsen Wu ,&nbsp;Zhihao He ,&nbsp;Junqing Chen ,&nbsp;Zhao Wang ,&nbsp;Qiang Yu ,&nbsp;Hao Feng ,&nbsp;Jianqiang He","doi":"10.1016/j.agrformet.2025.110868","DOIUrl":"10.1016/j.agrformet.2025.110868","url":null,"abstract":"<div><div>Ecological models are important tools for quantifying and evaluating the carbon and water cycles of agricultural and forest ecosystems. However, quick determination of the values of parameters of a given model remains a big challenge for most model users, especially beginners. In this study, we coupled an independent automatic parameter optimization tool of PEST (Parameter ESTimation) with the Biome-BGC model through Python programming language, and finally developed a new Biome-BGC-PEST software package for automatic model optimization. The encapsulation of the optimization process for Biome-BGC model parameters has heavily simplified model operational steps and improved model calibration efficiency. With the Biome-BGC-PEST package, sensitivity analysis and optimization of physiological and ecological parameters of the Biome-BGC model were conducted based on combined remote-sensing products of GPP (Gross primary productivity) and ET (Evapotranspiration) for the agricultural and forest ecosystems in the Qinling Mountains of China. Compared with the traditional trial-and-error methods for parameter optimization, the influential parameters estimated by the Biome-BGC-PEST package were similar, mainly including atmospheric deposition of N, symbiotic and asymbiotic fixation of N, cuticular conductance, etc. However, they were dramatically different in their sensitivity magnitudes. This was mainly because the new method greatly enhanced the efficiency of parameter optimization through allowing simultaneously tuning all of the parameters related to carbon and water fluxes. Consequently, the simulation accuracy of the Biome-BGC model was dramatically improved for the agricultural and forest ecosystems in the Qinling Mountains after parameter optimization. The <em>R²</em> (Coefficient of determination) of general GPP simulations increased from 0.67 to 0.89 and the RMSE (Root mean square error) decreased by about 37 %. Similarly, the <em>R²</em> of general ET simulations increased from 0.57 to 0.86 and the RMSE decreased by about 55 %. In conclusion, the newly established Biome-BGC-PEST package demonstrated similar or better optimization efficiency and accuracy compared to the traditional methods, which could greatly promote the application of the Biome-BGC model in relevant research of agricultural and ecological modeling.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110868"},"PeriodicalIF":5.7,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global patterns and trends of vegetation water use efficiency inferred from solar-induced chlorophyll fluorescence from 2001 to 2020","authors":"Zhichao Chen ,&nbsp;Yuefei Huang ,&nbsp;Xingan Chen ,&nbsp;Chong Nie ,&nbsp;Jiazheng Wan ,&nbsp;Shuo Zhang","doi":"10.1016/j.agrformet.2025.110865","DOIUrl":"10.1016/j.agrformet.2025.110865","url":null,"abstract":"<div><div>This study introduces a novel satellite-based framework that integrates solar-induced chlorophyll fluorescence (SIF) with plant physiological theory to simultaneously estimate gross primary productivity (GPP) and transpiration (T_r). This integrated approach enables global assessment of vegetation water use efficiency (WUE_T = GPP/T_r) and ecosystem water use efficiency (WUE = GPP/evapotranspiration) from 2001 to 2020. Site-scale validation using flux tower data shows strong agreement for WUE (R² = 0.615) and WUE_T (R² = 0.573). Globally, WUE_T increased from 2001 to 2010 due to CO₂ fertilization but stabilized from 2011 to 2020 as rising vapor pressure deficit (VPD) offset physiological gains. In contrast, WUE continued to rise driven by increased T_r/ET linked to vegetation greening (R² = 0.776 with LAI). Attribution analysis shows that CO₂ and VPD together explained over 70 % of interannual variation in WUE_T. Future projections using CMIP6 models indicate that WUE_T will closely follow changes in the CO₂-to-VPD ratio (C_a/VPD), while WUE is expected to increase through 2100 under high-emission scenarios (SSP370 and SSP585), supported by continued CO₂ rise and LAI-driven increases in T_r/ET. These results highlight the utility of SIF for large-scale carbon–water coupling assessment and underscore the need to jointly consider atmospheric dryness and vegetation structure in evaluating ecosystem responses to climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110865"},"PeriodicalIF":5.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microclimate vulnerability under coupled effects of changing climate and forest structure","authors":"Joanna Lumbsden-Pinto ,&nbsp;Tim Green ,&nbsp;Kathy Schwager ,&nbsp;Martin Dovciak","doi":"10.1016/j.agrformet.2025.110853","DOIUrl":"10.1016/j.agrformet.2025.110853","url":null,"abstract":"<div><div>Understanding how forest structure affects microclimate is crucial amidst global warming, especially in biodiversity hotspots such as the North American Coastal Plain (NACP). We contrasted the effects of forest canopy openness and forest type on key microclimatic variables (daily temperature means, minima, maxima, amplitude) relative to their long-term temporal trends to better understand how forest structure may buffer the effects of changing climate in an important NACP ecosystem (the Long Island Central Pine Barrens). We analyzed 75 years of summer temperature trends (1949–2023) to reveal a dramatic regional warming by 2.3 °C on average, with daily minima increasing much more (by 3.3 °C) than daily maxima (by 1.4 °C), leading to the decline in daily temperature variability (amplitude) by ∼2 °C. We then quantified how late summer temperatures in forest understories (at ∼1 m above ground, in 2020) varied spatially with forest structure to reveal that increasing canopy openness (from ∼0 % to ∼80 %) increased daily maxima (by 2.5 °C) while decreasing daily minima (by -2.7 °C), thus considerably increasing daily temperature amplitude (by 5.1 °C). This effect was particularly pronounced in open-canopy pine-dominated ecosystems. Since forest canopies are expected to become more open under warmer climate due to drought-related tree mortality, lower tree recruitment, and increased fire, future summer daily temperature maxima in forest understories are likely to increase considerably more than predicted from regional temporal trends, while regional changes over time in daily minima (increase) and amplitude (decrease) may be offset in forest understories by changing forest structure.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110853"},"PeriodicalIF":5.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Historical spatiotemporal trends in global mangrove productivity and its response to the environment: Perspectives from multiple satellite-based productivity proxies","authors":"Qian Liu ,&nbsp;Luoma Wan ,&nbsp;Fei Xu ,&nbsp;Ruikun Gou ,&nbsp;Guanghui Lin ,&nbsp;Xiaolin Zhu","doi":"10.1016/j.agrformet.2025.110871","DOIUrl":"10.1016/j.agrformet.2025.110871","url":null,"abstract":"<div><div>Mangroves, recognized as highly productive ecosystems, play a crucial role in global carbon cycle despite covering only 2 % of coastal ocean area. Understanding the historical trends of mangrove productivity and its responses to the external environment is crucial. However, the global and regional trends in mangrove productivity and the applicability of productivity proxies in mangrove ecosystems remained unclear. Leveraging eight productivity proxy datasets, including four Gross Primary Productivity (GPP) products, two Solar-Induced Fluorescence (SIF) products, the Near-Infrared Reflectance of vegetation (NIRv), and Leaf Area Index (LAI), this study conducted a comprehensive evaluation on spatiotemporal trends of global mangrove productivity. Through comparison with flux tower observations at three temporal scales (yearly, monthly, and 8-day), the Global OCO-2 SIF (GOSIF), global spatially contiguous SIF (CSIF) and GOSIF-based GPP (GOGPP) products outperformed other productivity proxies in indicating mangrove ecosystem productivity, showing stronger consistency (R²: 0.37–0.70), while NIRv and LAI performed worse (R²: 0.07–0.50). Despite uncertainties in the datasets, collective evidence from all eight products revealed an overall increasing trend in global mangrove productivity since 1982, with a decelerated pace post-2000, reducing to roughly 50 % of the earlier rate. Simulation-based sensitivity and contribution analyses indicated that air temperature (AirT), sea surface temperature (SST), and atmospheric CO₂ were primary contributors to the marked increase in global mangrove productivity, while rising vapor pressure deficit (VPD) and sea surface salinity (SSS) were significant factors leading to a decrease, albeit offsetting only a small portion of the increase. This study provided constructive insights for developing mangrove productivity models and hold significant implications for coastal ecosystem conservation.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110871"},"PeriodicalIF":5.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of crop growth simulations under different drip irrigation modes by jointly assimilating UAV multimodal data into crop models","authors":"Hao Dong ,&nbsp;Yulian Xia ,&nbsp;Ruoqing Hu ,&nbsp;Shikun Sun ,&nbsp;Yakun Wang ,&nbsp;Youtian Zhang","doi":"10.1016/j.agrformet.2025.110870","DOIUrl":"10.1016/j.agrformet.2025.110870","url":null,"abstract":"<div><div>Accurate crop monitoring is essential for agricultural planning and food security. This study developed a coupling framework of unmanned aerial vehicle (UAV) multimodal data and crop models based on a sequential data assimilation method, offering technical support for crop growth simulation and precision management under drip irrigation modes in the Hexi Corridor of Northwest China. Multispectral and thermal infrared image data of spring maize at different growth stages were acquired via UAVs. The UAV-derived leaf area index (LAI) and soil moisture (SM) were assimilated into the WOFOST model using the ensemble Kalman filter (EnKF). Three assimilation schemes including (a) LAI, (b) SM, and (c) LAI+SM were compared to explore the effects of different mulching treatments (mulched vs. non-mulched) and irrigation gradients on assimilation performance under drip irrigation modes. Our results showed that the fusion of UAV-based multispectral and thermal infrared multimodal data enabled accurate retrieval of LAI and SM, with a maximum <em>R²</em> of 0.85. The three assimilation schemes exhibited significant differences, and the joint assimilation of LAI and SM outperformed the others. This may be since LAI and SM, as key indicators of crop growth and development, undergo dynamic changes throughout the growth period, and their joint assimilation fully captures the temporal variability of crops and soil. In addition, the proposed framework demonstrated marked variations in simulation accuracy across different drip irrigation modes. Overall, the performance for shallow buried drip irrigation (SBDI) was superior to that for surface drip irrigation (SDI) and film-mulched drip irrigation (FDI). This may be attributed to the direct influence on soil evaporation and evapotranspiration under the latter two modes, which in turn modifies crop growth and development processes and ultimately affects the model's simulation accuracy.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110870"},"PeriodicalIF":5.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering tree drought responses across species: linking leaf water potentials with remote sensing greenness and photoprotection dynamics","authors":"Petra D’Odorico ,&nbsp;Dominic Fawcett ,&nbsp;Richard Peters ,&nbsp;David Steger ,&nbsp;Tobias Zhorzel ,&nbsp;Günter Hoch ,&nbsp;David Basler ,&nbsp;Christian Ginzler ,&nbsp;Michael Eisenring ,&nbsp;Gaétan Glauser ,&nbsp;Roman Zweifel ,&nbsp;Arthur Gessler ,&nbsp;Ansgar Kahmen","doi":"10.1016/j.agrformet.2025.110856","DOIUrl":"10.1016/j.agrformet.2025.110856","url":null,"abstract":"<div><div>Monitoring forest drought stress requires indicators that capture tree water relations across species and scales. Remote sensing enables large-scale assessment of drought vulnerability, but species-specific water and light use strategies complicate data interpretation, underscoring the need for mechanistic insights into remotely sensed signals in mature trees. We investigated drought responses of seven common European tree species (<em>Abies alba, Picea abies, Pinus sylvestris, Acer pseudoplatanus, Fagus sylvatica, Carpinus betulus, Quercus</em> sp.) at a temperate forest throughfall exclusion site during the 2023 peak growing season, integrating drone-based multispectral imagery with measurements of leaf water potential, turgor loss point, and leaf pigments. Our goal was to assess whether drone-derived greenness and photoprotection indicators capture species-specific variation in tree water status and contribute to a mechanistic interpretation of remote sensing signals over seasonal and diurnal timescales. We found that the photochemical reflectance index (PRI) strongly correlated with leaf water potentials, capturing both drought-induced declines and post-rainfall recovery, while the normalized difference vegetation index (NDVI) mostly detected greening losses in <em>A. pseudoplatanus, F. sylvatica, C. betulus</em>, but failed to reflect recovery. A model combining NDVI-derived greenness and PRI-derived photoprotection accounted for 65–70 % of the variance in leaf water potential dynamics across the site, particularly at midday as a function of species-specific stomatal control. We further found that species experiencing higher stress on their hydraulic system (i.e., lower water potentials) and characterized by lower drought tolerance based on their climatic distributions, generally showed higher engagement of their xanthophyll cycle. This was reflected in higher photoprotection activation rates in the morning (PRI_rate) and wider daily operating ranges (PRI_range), driven by diffusional and non-diffusional limitations on photosynthesis. By integrating hydraulic and photoprotective functioning, this study highlights both the insights gained and the inherent complexity in explaining interspecific differences in drought vulnerability, underscoring the potential to refine early-warning systems and enable species-specific drought monitoring.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110856"},"PeriodicalIF":5.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China","authors":"Kaiqi Du ,&nbsp;Guilong Xiao ,&nbsp;Jianxi Huang ,&nbsp;Xia Jing ,&nbsp;Xiaoyan Kang ,&nbsp;Jianjian Song ,&nbsp;Quandi Niu ,&nbsp;Haixiang Guan ,&nbsp;Xuecao Li ,&nbsp;Yelu Zeng","doi":"10.1016/j.agrformet.2025.110869","DOIUrl":"10.1016/j.agrformet.2025.110869","url":null,"abstract":"<div><div>Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m⁻² sr⁻¹ nm⁻¹ year⁻¹). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R² ranging from 0.324 (<em>p</em> &lt; 0.01) to 0.947 (<em>p</em> &lt; 0.001). A strong correlation with tower-based GPP (R² = 0.55, <em>p</em> &lt; 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF’s superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at <span><span>https://doi.org/10.6084/m9.figshare.27075145</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"375 ","pages":"Article 110869"},"PeriodicalIF":5.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}