Agricultural and Forest Meteorology最新文献

{"title":"Exploring the combined effects of drought and drought-flood abrupt alternation on vegetation using interpretable machine learning model and r-vine copula function","authors":"Lulu Xie ,&nbsp;Yi Li ,&nbsp;Ziya Zhang ,&nbsp;Kadambot H.M. Siddique ,&nbsp;Xiaoyan Song","doi":"10.1016/j.agrformet.2025.110568","DOIUrl":"10.1016/j.agrformet.2025.110568","url":null,"abstract":"<div><div>Global warming has significantly increased the frequency and intensity of extreme events, such as drought and drought-flood abrupt alternation (DFAA). Vegetation, a crucial component of terrestrial ecosystems, contributes greatly to agricultural production and economic development. Assessing the impacts of droughts and DFAA on vegetation is essential for ecological environment protection, food security, and economic development. This study combines the random forest model with Shapley Additive Explanation (SHAP) values to create an interpretable machine learning model, which is then coupled with the R-Vine Copula function to explore the combined effects of drought (using nonstationary drought indexes) and DFAA (using the SDFAI: short-cycle drought flood abrupt alteration index) on vegetation in the China-Pakistan Economic Corridor (CPEC) from 1981 to 2019. The key findings are as follows: (1) Drought events intensified, the risk of flood-to-drought decreased, the risk of drought-to-flood increased, and net primary productivity (NPP) showed an upward trend; (2) The relative contributions to NPP were NSPEI (21.0 %), SDFAI (31.4 %), and SSMI (47.6 %); (3) A strong upper tail dependence occurred between SSMI and NPP, and a strong lower tail dependence occurred between SDFAI and SSMI. When SSMI acted as an intermediary variable, the indirect correlation between SDFAI and NPP was strong; (4) In flood-to-drought events, the proportions of SHAP&lt;0 and SHAP&gt;0 were 24 % and 76 %, respectively, indicating an antagonistic role of flood-to-drought in promoting vegetation growth in the CPEC. In drought-to-flood events, the corresponding proportions were 73 % and 27 %, respectively, indicating a synergistic effect of drought-to-flood in inhibiting vegetation growth. This study enhances the understanding of the mechanisms by which DFAA impacts vegetation and provides a novel approach for exploring the combined effects of multiple extreme events on vegetation.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110568"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916314","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":"Warmer winter under climate change would reshape the winter subsurface drainage pattern in Eastern Canada","authors":"Ziwei Li ,&nbsp;Zhiming Qi ,&nbsp;Junzeng Xu ,&nbsp;Yuchen Liu ,&nbsp;Ward N. Smith ,&nbsp;Andrew VanderZaag ,&nbsp;Tiequan Zhang ,&nbsp;Birk Li ,&nbsp;Haomiao Cheng","doi":"10.1016/j.agrformet.2025.110602","DOIUrl":"10.1016/j.agrformet.2025.110602","url":null,"abstract":"<div><div>Subsurface drainage is a key loss pathway for water and nutrients from agricultural land in Eastern Canada. Winter is a dominant period of subsurface drainage and nutrient loss in cold climates. Under climate change, however, future winter drainage patterns may change significantly due to reductions in snow cover and soil freezing. This study evaluated the performance of four machine-learning (ML) models in simulating winter subsurface drainage for five sites in Eastern Canada. The calibrated/trained ML models were then applied to predicted future climate (high emission scenario: RCP8.5) from 1950 to 2100 to comprehend the potential alteration in winter drainage patterns under global warming. Among ML models, the Cubist and SVM-RBF models emerged as the most accurate, offering competing short-term simulation (≤7 years) capabilities compared to the RZ-SHAW model with lower computational demand. However, ML models’ long-term projections under climate change scenarios revealed inconsistencies from insufficient and unbalanced observed winter subsurface drainage data. Simulation by both the RZ-SHAW and ML models predict a significant increase in winter drainage volume by the end of the 21st century (1950–2005 vs. 2070–2100) (RZ-SHAW: 243 mm to 328 mm (+35 %); ML models: 250 mm to 425 mm (+70 %)). RZ-SHAW simulated a shift towards a more evenly spread drainage pattern throughout the winter months from baseline to the end of the century. This shift was driven by the simulated shorter snow coverage periods, advancement of snowmelt timing, and fewer days of freezing soil. Thus, the timing of peak and trough winter drainage is expected to reverse, with February becoming the peak month and April the lowest by the century's end.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110602"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916313","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":"The stand age governs forest root: Shoot ratios across northeast China","authors":"Han Wu ,&nbsp;Zhenggang Du ,&nbsp;Lingyan Zhou ,&nbsp;Guiyao Zhou ,&nbsp;Giovanni Coco ,&nbsp;Jing Gao ,&nbsp;Xuhui Zhou","doi":"10.1016/j.agrformet.2025.110595","DOIUrl":"10.1016/j.agrformet.2025.110595","url":null,"abstract":"<div><div>Root: shoot (RS) ratios are widely used to estimate global and regional forest carbon stocks and to model the forest carbon cycle. However, limited knowledge is available regarding factors that determine RS spatial patterns, particularly in high-latitude temperate regions. Therefore, in this study, we compiled 189 measurements of forest RSs across Northeast China to evaluate the main drivers of RS patterns. An optimal machine learning model was selected to upscale the RS data and estimate belowground biomass carbon in Northeast China. The results showed that the stand age had the greatest impact on RS variation (contribution of 17.6 %), exceeding the influence of other predictors and increasing the coefficient of determination of the RS by 41 % in a structural equation model. Regional RS values decreased from 0.22 ± 0.02 to 0.16 ± 0.01 as the stand age increased from less than 20 years to over 60 years. Higher estimated RS values were found in both forests with a stand age of 40–60 years (19.3 %) and over 60 years (22.6 %) when the stand age was not considered. We also found that our RS estimates were lower (mean value = 0.21 ± 0.05) than Earth system models (0.25 ± 0.03) and remote sensing-based estimates (0.5 ± 0.05), resulting in 33.2 % and 62.7 % lower estimates of belowground biomass carbon in Northeast China, respectively. The results of this study highlight the importance of the stand age in forest carbon allocation, representing a factor that should be incorporated when estimating current and future carbon sequestration.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110595"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916312","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":"Spring factors controlling interannual CO2 flux variations in a subtropical humid alpine meadow on the southeastern Tibetan Plateau","authors":"Feng Zeng ,&nbsp;Ruowen Yang ,&nbsp;Huizhi Liu ,&nbsp;Qun Du ,&nbsp;Yang Liu ,&nbsp;Huancai Cun","doi":"10.1016/j.agrformet.2025.110603","DOIUrl":"10.1016/j.agrformet.2025.110603","url":null,"abstract":"<div><div>Understanding of the carbon cycling in subtropical humid alpine meadows is still limited due to the scarcity of observations in this area. It is crucial to quantify the temporal dynamics and factors influencing CO₂ fluxes in order to predict ecosystems CO₂ budgets accurately under climate change. This study utilized 11 years (2012–2022) of eddy covariance observation data to investigate CO₂ fluxes and its controlling factors in a subtropical humid alpine meadow on the southeastern Tibetan Plateau. Our findings indicate that this alpine meadow ecosystem consistently acted as a net CO₂ sink, with annual net ecosystem productivity (NEP) ranging from 137 g C <em>m</em>⁻² to 370 g C <em>m</em>⁻². Seasonal variations in CO₂ fluxes were primarily governed by soil water content, soil temperature (Ts), and leaf area index, with notable interactions observed among these variables. On an annual basis, the interannual variability (IAV) of NEP anomalies was predominantly linked to gross primary production (GPP) anomalies during the dry season. IAV of NEP was mainly driven by length of growing season, while that of GPP and ecosystem respiration were significantly affected by summer peak values. Additionally, spring environmental variables, i.e., Ts, vapor pressure deficit, and photosynthetically active radiation, were crucial in influencing the IAV of CO₂ fluxes through the regulation of phenological and physiological metrics. This comprehensive analysis provides valuable insights for future carbon modeling efforts on the Tibetan Plateau.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110603"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916311","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":"Improved fire severity prediction using pre-fire remote sensing and meteorological time series: Application to the French Mediterranean area","authors":"Victor Penot ,&nbsp;Thomas Opitz ,&nbsp;François Pimont ,&nbsp;Olivier Merlin","doi":"10.1016/j.agrformet.2025.110588","DOIUrl":"10.1016/j.agrformet.2025.110588","url":null,"abstract":"<div><div>Fire severity, or how an environment is affected by fire, can be estimated over large areas using remotely sensed indices like the Relative Burnt Ratio (RBR). RBR predictions typically rely on data from a single date just before the fire. However, predicting RBR accurately in both time and space remains challenging. To improve RBR predictability, we developed new models using time series data spanning several months before the fire. These models use fuel proxies derived from optical remote sensing and meteorological data. We applied this approach to fires in the French Mediterranean area during the summers of 2016–2021. We used a Lagged Generalized Additive Model (LGAM) and a Functional Linear Model (FLM) to estimate the influence of variables up to several months before the fire on RBR. A GAM fed with immediate pre-fire predictors served as a benchmark. Training and prediction were conducted at the fire–land-cover spatial scale using a training dataset spatially independent of the test dataset. FLM achieved the best prediction accuracy on test data (R=0.68, RMSE=0.057), outperforming LGAM (R=0.60, RMSE=0.063) and the benchmark (R=0.52, RMSE=0.069). FLM accurately predicted the highest RBR values when the Normalized Difference Vegetation Index decreased faster than the average and when the Duff Moisture Code increased faster than the average over the 65 days before the fire. The 17% decrease in the RMSE of FLM predictions compared to GAM predictions shows that understanding fuel dynamics up to two months before a fire provides valuable information for ranking areas by fire severity.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"371 ","pages":"Article 110588"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922631","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":"Remote sensing assessment of invasive plant species impacts on microclimate and water stress in mediterranean coastal ecosystems","authors":"Giorgi Kozhoridze ,&nbsp;Eyal Ben-Dor ,&nbsp;Vítězslav Moudrý ,&nbsp;Marcelo Sternberg","doi":"10.1016/j.agrformet.2025.110606","DOIUrl":"10.1016/j.agrformet.2025.110606","url":null,"abstract":"<div><div>This study uses multi-source, multi-temporal remote sensing imagery to compare the effects of invasive <em>Heterotheca subaxillaris</em> and <em>Acacia saligna</em> and natural vegetation on microclimate conditions in Israeli coastal plain. The overall accuracy of the classification and mapping of invasive species and other land covers was 85 %, with optimal performance observed using late autumn imagery. Among the natural areas, 45 % were occupied by native vegetation, 30 % by <em>H. subaxillaris</em>, 15 % by <em>A. saligna</em> and 10 % by bare soil/sand.</div><div>Quantitative analysis revealed that <em>H. subaxillaris</em>, consistently elevated surface temperatures by 0.6 °C in spring, 1.8 °C in summer and 2.19 °C in autumn compared to native vegetation. This species also increased water vapor and potential evapotranspiration, while reducing soil evaporation and vegetation shading, resulting in both direct and indirect contributions to water stress. In contrast, <em>A. saligna,</em> provided localized cooling due to high vegetation density and shading, yet its high assimilation and transpiration rates led to elevated water vapor, daily total evaporation and PET indirectly amplifying water stress. Native vegeation moderated the local microclimate by decreasing temperature and water vapor, while maintaining stable evapotranspiration and low water stress throughout the dry season. This study highlights the complex interactions between invasive species and microclimate conditions, emphasizing the critical role of remote sensing techniques in monitoring and managing these species. By integrating remote sensing imagery with detailed microclimatic analysis, it provides novel insights into the contrasting ecological impacts of invasive species on temperature regimes, water stress, and evapotranspiration in Mediterranean coastal ecosystems.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"371 ","pages":"Article 110606"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922632","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":"A decade of lost growth in old trees: aging shapes the impacts of drought and late frost events on European beech","authors":"Álvaro Rubio-Cuadrado ,&nbsp;Isabel Dorado-Liñán ,&nbsp;Rosana López ,&nbsp;J․Julio Camarero","doi":"10.1016/j.agrformet.2025.110601","DOIUrl":"10.1016/j.agrformet.2025.110601","url":null,"abstract":"<div><div>Studying growth declines and the factors that cause them, such as droughts or late spring frosts, is key to understanding their influence on forest productivity. However, most of the currently used methodologies to assess these events have drawbacks that can lead to erroneous conclusions. The increasing frequency and importance of these growth declines is linked to a higher climate variability and thus requires more effort to find suitable approaches to quantify their impacts on long-term tree growth. Furthermore, dendroecology generally focuses its efforts on the study of growth relationships with prevailing climatic conditions, giving little weight to the effect of occasional and discrete climatic events on medium- and long-term growth dynamics. Here, we develop a new methodology that consists in: (I) analyzing the largest growth reductions, (II) characterizing climate in those years, (III) identifying the change points in the tree growth function using Bayesian regression, and (IV) quantifying the impact of climate on short-, medium- and long-term growth trends using relative growth and cumulative growth loss indices. We studied the drops in growth suffered by European beech (<em>Fagus sylvatica</em>), caused by both droughts and late frosts. The study was conducted in stands with contrasting structural features (diameter, age) at the southwestern species distribution limit in the central Iberian Peninsula. Our results indicate that extreme climate events have caused a decade of growth loss in old trees (age ca. 100–330 years), and are the factor responsible for the decline of tree vigor. However, the relationships between prevailing climate conditions and tree growth were not significant, highlighting the importance of occasional and discrete climate events as the main drivers of growth. Tree age, rather than tree diameter, shapes tree growth response to extreme climate events such as droughts and late frosts.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110601"},"PeriodicalIF":5.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912464","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":"When do clouds and aerosols lead to higher canopy photosynthesis?","authors":"Kevin H.H. van Diepen ,&nbsp;Elias Kaiser ,&nbsp;Oscar K. Hartogensis ,&nbsp;Alexander Graf ,&nbsp;Jordi Vilà-Guerau de Arellano ,&nbsp;Arnold F. Moene","doi":"10.1016/j.agrformet.2025.110597","DOIUrl":"10.1016/j.agrformet.2025.110597","url":null,"abstract":"<div><div>Clouds and aerosols can increase canopy photosynthesis relative to clear-sky values through changes in total and diffuse solar radiation: the diffuse fertilization effect (DFE). DFE varies across observational sites due to (a) inconsistent definitions and quantifications of DFE, (b) unexplored relationships between DFE and cloudiness type, and (c) insufficient knowledge of the effect of site characteristics. We showed that: DFE definitions vary, DFE quantifications do not connect to existing definitions or do not isolate the causal factor, and a systematic protocol to quantify DFE is lacking. A new theoretical framework served to clarify the relation between DFE definitions, and showed how DFE varies with cloudiness types and site characteristics. We proposed guidelines for a systematic DFE quantification across studies, and which aim to isolate the causal factor of DFE.</div><div>Applying our framework to observations of canopy photosynthesis, solar radiation and cloudiness types we quantified DFE at daily and sub-daily time scales. We showed for the first time how DFE varies with cloudiness type, due to the varying trade-off between diffuse radiation and total solar radiation. Using an observation-driven canopy photosynthesis model, we showed that the DFE varies with site characteristics and time of day. The DFE responded strongly to leaf area index, canopy nitrogen distribution, leaf orientation and leaf transmittance, with leaf area index and leaf orientation driving DFE occurrences at our site. Our study emphasizes the importance of quantifying the DFE systematically and accurately across observational sites and highlights the need for information on cloudiness climatology and site characteristics.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110597"},"PeriodicalIF":5.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912622","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":"Reversal of the sensitivity of vegetation productivity to precipitation in global terrestrial biomes over the recent decade","authors":"Weirong Zhang ,&nbsp;Zehao Fan ,&nbsp;Chuan Jin ,&nbsp;Yue Jiao ,&nbsp;Kai Di ,&nbsp;Ming Feng ,&nbsp;Yifei Lu ,&nbsp;Kun Zhao ,&nbsp;Hongxian Zhao ,&nbsp;Shaorong Hao ,&nbsp;Zhongmin Hu","doi":"10.1016/j.agrformet.2025.110598","DOIUrl":"10.1016/j.agrformet.2025.110598","url":null,"abstract":"<div><div>The sensitivity of vegetation productivity to precipitation (<em>S</em>_ppt) is crucial for grasping how vegetation responds to changing precipitation and forecasting future shifts in ecosystem function. However, comprehensive assessment of <em>S</em>_ppt globally is limited by specific technical defects or objective limitations, leading to a poor understanding of its spatial distribution and temporal variations. In this study, we examined the spatial patterns and temporal changes o.ff <em>S</em>_ppt across global terrestrial ecosystems from 2001 to 2021 using a change-based method and various satellite observations, including solar-induced fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Additionally, we obtained various high-resolution global datasets and applied <em>extreme gradient boosting</em> (<em>XGBoost</em>) along with SHapley Additive Explanations (SHAP) to explain how key climatic, topographic, edaphic, and vegetation variables regulate <em>S</em>_ppt. Spatially, <em>S</em>_ppt exhibited positive values in most regions, particularly in arid areas, while lower values were found in mesic regions. Temporally, <em>S</em>_ppt shifted from a declining to an increasing trend in most regions over the past two decades, with the breakpoint occurring primarily between 2011 and 2015. This shift could be related to the fertilization effect of elevated CO₂, intensified drought caused by increased vapor pressure deficit, and atmospheric nitrogen deposition. In forest ecosystems, radiation, temperature, and soil nutrients were found to be critical in regulating <em>S</em>_ppt, whereas leaf functional traits demonstrated relatively greater importance in grasslands and shrublands. Negative regulatory relationships were shown to exist between land slope and forest age with <em>S</em>_ppt. Overall, this research contributes to a deeper understanding of the mechanisms that drive vegetation productivity in the context of changing precipitation patterns.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110598"},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906557","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":"In vitro plant spectral response reveals dust stress","authors":"Ali Darvishi Boloorani ,&nbsp;Saham Mirzaei ,&nbsp;Hossein Ali Bahrami ,&nbsp;Masoud Soleimani ,&nbsp;Najmeh Neysani Samany ,&nbsp;Ramin Papi ,&nbsp;Maryam Mahmoudi ,&nbsp;Mohsen Bakhtiari ,&nbsp;Alfredo Huete","doi":"10.1016/j.agrformet.2025.110599","DOIUrl":"10.1016/j.agrformet.2025.110599","url":null,"abstract":"<div><div>Early-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (<em>i</em>) reduction of leaf chlorophyll and water contents; (<em>ii</em>) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (<em>iii</em>) decrease of spectral reflectance at 530–590, 740–1200 nm; (<em>iv</em>) decrease the slope and height of the red edge; (<em>v</em>) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (<em>vi</em>) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (<em>vii</em>) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110599"},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906454","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}