Agricultural and Forest Meteorology最新文献

{"title":"Separating leaf area index from plant area index using semi-supervised classification of digital hemispheric canopy photographs: A case study of dryland vegetation","authors":"Jake Eckersley ,&nbsp;Caitlin E. Moore ,&nbsp;Sally E. Thompson ,&nbsp;Michael Renton ,&nbsp;Pauline F. Grierson","doi":"10.1016/j.agrformet.2025.110395","DOIUrl":"10.1016/j.agrformet.2025.110395","url":null,"abstract":"<div><div>Leaf area index (<em>LAI</em>) describes the main plant surface area for gas exchange. Accurate <em>LAI</em> measurements are integral to effective hydrological, ecological, and climate modelling. <em>LAI</em> is commonly modelled using canopy gap fraction measurements from optical sensors. In woody vegetation, however, the wood to total plant area ratio (<span><math><mi>α</mi></math></span>) must also be estimated to convert plant area index (<em>PAI</em>) to <em>LAI</em>. Historically, estimating <span><math><mi>α</mi></math></span> required destructive harvests and is a potential source of <em>LAI</em> error. In this study, we present a theoretical framework for estimating <em>LAI</em> from digital hemispheric canopy photography by correcting for <span><math><mi>α</mi></math></span> within each image using semi-supervised pixel classification. We apply this framework to 201 images collected in semi-arid Australian vegetation (overstorey <em>LAI</em> range 0–5) to explore potential sources of error from: image classification, <em>LAI</em> model implementation, and differences in <span><math><mi>α</mi></math></span> among vegetation types. Leaf, wood, and canopy gap (sky) pixels were classified using a random forest (RF) algorithm with 87.7 ± 0.01 % accuracy (mean ± standard error) under overcast skies but 81.3 ± 0.01 % under clear sky conditions where leaf and wood pixel classification was inconsistent. <em>LAI</em> estimates using the proposed approach had a strong linear relationship to <em>PAI</em> (<em>r²</em> ≥ 0.97). However, the proportional contribution of woody material to canopy gap fraction was zenith angle dependent. Allowing <span><math><mi>α</mi></math></span> to vary by zenith and azimuth angle when calculating <em>LAI</em> resulted in estimates 10–17 % higher than widely used <em>PAI</em> conversion methods. The zenith angle distribution of <span><math><mi>α</mi></math></span> also differed among co-occurring vegetation types. Allowing the <em>PAI</em> to <em>LAI</em> regression slope to vary based on the dominant genus reduced <em>PAI</em> conversion error by ∼2 % (<em>p</em> &lt; 0.001). Quantifying <span><math><mi>α</mi></math></span> variability within canopies and between vegetation types using the method outlined here can reduce on-ground <em>LAI</em> measurement uncertainty.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110395"},"PeriodicalIF":5.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dominant grasses buffer the fluctuation of plant productivity to long-term grazing pressure in a desert steppe grassland","authors":"Feng Zhang,&nbsp;Shaoyu Li,&nbsp;Jiahua Zheng,&nbsp;Bin Zhang,&nbsp;Jing Wang,&nbsp;Jirong Qiao,&nbsp;Jiaqing Xing,&nbsp;Zhongwu Wang,&nbsp;Zhiguo Li,&nbsp;Guodong Han,&nbsp;Mengli Zhao","doi":"10.1016/j.agrformet.2025.110420","DOIUrl":"10.1016/j.agrformet.2025.110420","url":null,"abstract":"<div><div>Grazing by livestock can influence the diversity and productivity of plants in an ecosystem, as well as the relationship between productivity and diversity. Furthermore, these effects or their relationship can be strongly influenced by variation in the intensity of grazing as well as external environmental conditions, such as rainfall amount. We used observations over an 18-year period in a desert steppe grassland in Inner Mongolia to evaluate how different intensities of grazing influenced productivity, diversity and the underlying mechanism of their relationship through time. Increasing intensity of grazing led to decreased species richness, primarily via the loss of subordinate and rare species, and a decrease in aboveground net primary productivity [ANPP: g m^-2], primarily due to a reduction in dominant species (especially the forb species, <em>A. frigida</em>). We found a positive association between diversity and productivity in most experimental years (14 out of 18 years), with the slope being strongest in wetter years. This suggests that their positive relationship may be affected by precipitation. We used a random forest model to show that variation in ANPP was mainly driven by variation in dominant species, not species richness. Dominant species may be the key driver in regulating plant primary productivity in these species-poor, water-limited grassland ecosystems, and that less intense grazing may be an appropriate management regime to balance ecosystem functions and herder's income.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110420"},"PeriodicalIF":5.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031201","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":"Simplified mechanistic model for estimating leaf wetness","authors":"Rajkumar Dhakar ,&nbsp;Basavaraj R. Amogi ,&nbsp;Gajanan S. Kothawade ,&nbsp;Lav R. Khot","doi":"10.1016/j.agrformet.2025.110399","DOIUrl":"10.1016/j.agrformet.2025.110399","url":null,"abstract":"<div><div>This study aimed to develop a simplified mechanistic leaf wetness estimation model (SMLW) to support risk assessment of insect pests and diseases in irrigated specialty crops grown in the state of Washington, and around globe. Employing the energy balance and water budget approach, two variants of SMLW models (M1: SMLW and M2: SMLW_DPD) were developed using historical data from ten randomly selected automated weather stations in the Washington State University — AgWeatherNet ecosystem. Both variants simulate dewfall, rainfall interception, and evaporation processes; however, SMLW_DPD incorporates dew point depression (DPD) as an additional constraint. Both models enabled estimation of leaf wetness (LW, mm) and leaf wetness duration (LWD, h, defined as the duration when LW &gt; 0). The model input parameters were optimized through the Bayesian method and Morris's sensitivity index. For comparison, LWD was also estimated following existing empirical approaches based on constant DPD (M3) and relative humidity (M4). The LWD estimates from all four models were finally evaluated against actual historical leaf wetness sensor data. Results indicated that SMLW and SMLW_DPD outperform M3 and M4 in estimating daily LWD. With relatively higher precision and recall, SMLW_DPD exhibited higher coefficient of determination (0.84) with root mean squared (RMSE) and absolute error (MAE) of 1.13 h and 0.34 h, respectively. Whereas, both M3 and M4 had MAE of 10.16 h and 7.03 h, respectively. Overall, SMLW_DPD model could be a viable option to reliably estimate leaf wetness using typical weather variables and reducing reliance on intricate inputs such as net radiation and leaf area index. Tied with other weather variables like degree days, LWD estimated using SMLW_DPD can be an effective decision support for growers in determining optimal timing and frequency of sprays to manage insect pests and disease pressure.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110399"},"PeriodicalIF":5.6,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar-induced chlorophyll fluorescence and its relationship with photosynthesis during waterlogging in a maize field","authors":"Yunfei Wu ,&nbsp;Zhaoying Zhang ,&nbsp;Linsheng Wu ,&nbsp;Yongguang Zhang","doi":"10.1016/j.agrformet.2025.110404","DOIUrl":"10.1016/j.agrformet.2025.110404","url":null,"abstract":"<div><div>Solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for estimating gross primary production (GPP). However, the mechanism linking SIF to GPP under waterlogging stress remains unclear. Here, we investigated the GPP-SIF relationship and their responses to waterlogging stress using three years of continuous ground measurements in a maize field. Our results revealed a significant decoupling in the GPP-SIF relationship under waterlogging stress, as evidenced by a decline in R² values from 0.87 and 0.79 (non-waterlogging years: 2020 and 2021) to 0.20 (waterlogging year:2022), consistent with SCOPE model simulations. We examined the underlying mechanisms independently regulating SIF and GPP fluctuations. Our analysis suggested a considerable transition in dominating factors influencing both parameters, shifting from photosynthetically active radiation (PAR) under non-waterlogging conditions to soil water content (SWC) under waterlogging stress. Notably, we quantified the impact of elevated SWC on GPP and SIF, finding that the effect was more pronounced on GPP (62.41% reduction) than on SIF (54.3% reduction). We observed the weakened significance of SIF muti-scattering components induced by alterations in soil background spectra due to increased SWC affecting SIF radiative transfer processes. Complemented by SCOPE simulations, our analysis suggested that the significant decoupling of SIF and GPP physiological components, along with asymmetrical responses to SWC, collectively contribute to the reduced GPP-SIF relationship under waterlogging stress. Overall, our study provides valuable insights into GPP and SIF dynamics under waterlogging stress in a maize field, emphasizing the effectiveness of radiative transfer models for understanding plant photosynthetic responses to waterlogging stress.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110404"},"PeriodicalIF":5.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992143","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 impact of wildfire on the land surface parameters of a semi-arid grassland in the southwestern U.S.","authors":"Tilden P. Meyers ,&nbsp;Praveena Krishnan ,&nbsp;Mark Heuer ,&nbsp;John Kochendorfer ,&nbsp;Howard J. Diamond","doi":"10.1016/j.agrformet.2025.110390","DOIUrl":"10.1016/j.agrformet.2025.110390","url":null,"abstract":"<div><div>Just before the installation of a long-term energy/carbon flux tower site at the 10,000-acre Appleton-Whittle Research Ranch in southern Arizona, a wildfire burned nearly 90 % of this semi-arid grassland area in the region and there was little vegetation remaining on the surface. Wildfires in this region occur roughly every 7 - 15 years. The establishment of the site allowed for the determination of recovery time for this semi-arid grassland ecosystem. We examined how the wildfire altered the land surface characteristics such as broadband and photosynthetically active radiation (PAR) albedo, surface roughness, and the seasonal net ecosystem exchange. This information was also used to determine the amount of time it took this ecosystem to recover back to a quasi-baseline state. We found that it took approximately 3 years for the surface roughness to increase from 0.04 to 0.12 m, and then stay near that level in the following years. The monsoon season (July - September) net ecosystem exchange (NEE) was positive (source) for two years following the wildfire and did not become a significant net carbon sink until 2005. The ecosystem recovery from this wildfire event may have occurred sooner but the accumulated rainfall for the 2 years following the fire were both well below the 20-year average of 254 mm. We were also able to determine that immediately after the wildfire, the soil shortwave and PAR albedo was 0.20 and 0.13, respectively. However, with the onset of the monsoon and rainfall, both the shortwave and PAR albedos decreased to 0.10 and 0.07, respectively. The albedos reverted to the original values after several days of no additional rainfall.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110390"},"PeriodicalIF":5.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992144","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 model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States","authors":"Rongzhu Qin ,&nbsp;Kaiyu Guan ,&nbsp;Bin Peng ,&nbsp;Feng Zhang ,&nbsp;Wang Zhou ,&nbsp;Jinyun Tang ,&nbsp;Tongxi Hu ,&nbsp;Robert Grant ,&nbsp;Benjamin R K Runkle ,&nbsp;Michele Reba ,&nbsp;Xiaocui Wu","doi":"10.1016/j.agrformet.2025.110407","DOIUrl":"10.1016/j.agrformet.2025.110407","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Cotton (&lt;em&gt;Gossypium hirsutum&lt;/em&gt; L.) cultivation contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is thus crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, &lt;em&gt;ecosys&lt;/em&gt;, and a deep learning-based Model-Data Fusion (MDF) approach to quantify the magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the performance of the process-based model in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) values at production-scale farm sites. We then apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the &lt;em&gt;ecosys&lt;/em&gt; model to generate the holistic carbon budget of cotton cropland at the county level across the U.S. from 2008 to 2019. Validation at the three EC sites indicates that the &lt;em&gt;ecosys&lt;/em&gt; model achieves R&lt;sup&gt;2&lt;/sup&gt; values of 0.9 and 0.8 for the simulated versus the EC daily GPP and respiration, respectively, and 0.9 for the simulated versus the experimentally measured leaf area index. The R&lt;sup&gt;2&lt;/sup&gt; at county level in our framework is 0.8 for both cotton lint yield and GPP: the simulated versus survey-based cotton lint yield, and the simulated versus satellite-based monthly GPP. The spatio-temporal patterns of the simulated cotton lint yield, GPP, and their responses to climate factors (average temperature, average vapor pressure deficit (VPD), and cumulative precipitation during the growing season) are consistent with the observations, indicating that our framework approach captures the underlying processes relating environmental conditions to cotton growth. Our analysis shows that cotton productivity (lint yield and GPP) decreased with increasing average VPD during the growing season, especially under rainfed conditions. It also shows that the carbon budget terms, including predicted net primary productivity, crop yield, and soil heterotrophic respiration, decreased as the VPD increased. Conversely, the predicted change in soil organic carbon was less influenced by climate, which decreased with increasing initial soil organic carbon content and cation exchange capacity, and increased with increasing soil bulk density. The variable impacts of crop management practices, climatic factors, and soil characteristics on carbon budgets highlight the intricate interactions among these factors that shape carbon dynamics in cotton agroecosystems, and further emphasize the necessity of accurately simulating the carbon budgets of cotton agroecosyste","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110407"},"PeriodicalIF":5.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terrestrial laser scanning-derived canopy storage capacity improves the performance of the revised Gash model in temperate forests","authors":"Yue Yu ,&nbsp;Jiaojun Zhu ,&nbsp;Tian Gao ,&nbsp;Zhihua Liu ,&nbsp;Lifang Liu ,&nbsp;Fengyuan Yu ,&nbsp;Jinxin Zhang","doi":"10.1016/j.agrformet.2025.110398","DOIUrl":"10.1016/j.agrformet.2025.110398","url":null,"abstract":"<div><div>Rainfall interception loss (<em>I</em>) by forest canopy is a crucial hydrological process in forest ecosystems, and thus its accurate modeling is essential for understanding water balance. The revised Gash model is commonly employed in <em>I</em> modeling; however, its performance is affected by the accuracy of canopy storage capacity (<em>S</em>), which is identified as one of the most sensitive parameters. Consequently, optimizing the estimation of <em>S</em> and then cascading application in the revised Gash model warrants further attention. In this study, we measured gross rainfall, throughfall, and stemflow for the larch (<em>Larix kaempferi</em>) plantation forest (LPF) and the Mongolian oak (<em>Quercus mongolica</em>) forest (MOF) in Northeast China in 2018 and 2019. Terrestrial laser scanning (TLS) was introduced to derive <em>S</em> (<em>S_ex</em>). <em>S_ex</em> was then compared with values calculated from two commonly regression-based methods (<em>S_mean</em> and <em>S_mini</em>). Finally, the revised Gash model was run using the three types of <em>S</em>, and the model performances were evaluated. As a result, <em>I</em> of LPF (27.9 %) was higher than that of MOF (20.1 %). For LPF and MOF, <em>S</em> calculated from <em>S_ex</em> was the largest (1.45 and 0.51 mm), followed in descending order by <em>S_mean</em> (0.98 and 0.32 mm) and <em>S_mini</em> (0.29 and 0.13 mm). Compared with models run with <em>S_mean</em> and <em>S_mini, S_ex</em> improved the model performance, regardless of whether the Penman-Monteith equation or a linear regression method was used to calculate the evaporation rate (another sensitive parameter of the revised Gash model). Moreover, the model using <em>S_ex</em> particularly enhanced the model's accuracy at middle and heavy rainfall levels. In conclusion, the TLS-derived <em>S</em> improves the model performance in temperate forests in Northeast China. Meanwhile, in contrast to previous studies, which emphasized the contribution of evaporation rate/rainfall intensity (<em>E/R</em>) in modelling larger rainfall events, this study suggests the role of <em>S</em> should not be overlooked.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110398"},"PeriodicalIF":5.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990774","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":"Variations and drivers of CO2 fluxes at multiple temporal scales of subtropical agricultural systems in the Huaihe river Basin","authors":"Kaidi Zhang ,&nbsp;Yanyu Lu ,&nbsp;Chunfeng Duan ,&nbsp;Fangmin Zhang ,&nbsp;Xinfeng Ling ,&nbsp;Yun Yao ,&nbsp;Zhuang Wang ,&nbsp;Xintong Chen ,&nbsp;Shaowei Yan ,&nbsp;Yanfeng Huo ,&nbsp;Yuan Gong","doi":"10.1016/j.agrformet.2025.110394","DOIUrl":"10.1016/j.agrformet.2025.110394","url":null,"abstract":"<div><div>Understanding of the crop carbon balance across different time scales and corresponding responses to abiotic and biotic factors is crucial for improving carbon cycle models in the context of future climate change and management practices. In this study, we employed the Random Forest (RF) algorithm, Kolmogorov-Zurbenko filtering method and structural equation modeling (SEM) to quantify the effects of abiotic and biotic factors on CO₂ fluxes at various time scales based on 7-years measurements. Our results revealed that O₃ primarily manifested indirect effects on NEE and GPP via altering LAI on the daily and monthly scale, and that overall regulatory effect on CO₂ fluxes developed greater as the time scale increased. Net radiation (Rn) was the most critical abiotic factor altering net ecosystem exchange (NEE) and gross primary productivity (GPP) at the half-hourly, daily, and monthly scales, with the exception of photosynthetically active radiation (PAR) controlling daily NEE and GPP in the rice system. It was innovatively found that LAI had little control on detrended daily CO₂ fluxes, which was much lower than the monthly CO₂ fluxes. Air temperature (Ta) was the most important abiotic factor for ecosystem respiration (Reco) at half-hourly and daily scale. For NEE, Reco, and GPP, the maximum explanation of SEM models was 70.10 %, 79.60 % and 76.20 %, respectively. The SEM results indicated that at multiple time scales, Rn exerted significant direct and indirect effects on both NEE and GPP. LAI only showed a strong direct leading effect on NEE and GPP on the monthly scale. The findings we reported have the potential to further develop carbon cycle models of cropland ecosystems under climate change by clarifying the influence path of O₃ on CO₂ fluxes and highlighting the factors that dominate CO₂ fluxes on various time scales.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110394"},"PeriodicalIF":5.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989112","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":"Chlorophyll content estimation in radiata pine using hyperspectral imagery: A comparison between empirical models, scaling-up algorithms, and radiative transfer inversions","authors":"Tomas Poblete ,&nbsp;Michael S. Watt ,&nbsp;Henning Buddenbaum ,&nbsp;Pablo J. Zarco-Tejada","doi":"10.1016/j.agrformet.2025.110402","DOIUrl":"10.1016/j.agrformet.2025.110402","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Radiata pine (&lt;em&gt;Pinus radiata&lt;/em&gt; D. Don) is a widely planted tree species. Fertilizers, especially those containing leaf nitrogen (N) and phosphorous (P), are essential for maximizing growth. Nutrient deficiencies and excessive fertilization can limit growth, so monitoring is crucial. Leaf pigments such as chlorophyll &lt;em&gt;a&lt;/em&gt;+&lt;em&gt;b&lt;/em&gt; (C&lt;sub&gt;a+b&lt;/sub&gt;) can be used to assess plant nutrition, specifically leaf N. Remote sensing approaches can be used to monitor forest condition by estimating C&lt;sub&gt;a+b&lt;/sub&gt; content as a proxy for leaf N. Conventional methods for C&lt;sub&gt;a+b&lt;/sub&gt; estimation are based on empirical relationships using sensitive spectral indices or inversions of Radiative Transfer Models (RTMs). However, the structural complexity of tree crowns composed of multiple layers of clumped leaves/needles and background and shadow effects challenge the use of the indices proposed for both leaf C&lt;sub&gt;a+b&lt;/sub&gt; and leaf nitrogen assessment. This study compares the accuracy of methods for C&lt;sub&gt;a+b&lt;/sub&gt; estimation in radiata pine using hyperspectral data collected from a greenhouse experiment over the growing season and from a field trial representing a stand with a complex structure. The methods used to predict needle C&lt;sub&gt;a+b&lt;/sub&gt; from tree-crown spectra included: 1) empirical relationships between C&lt;sub&gt;a+b&lt;/sub&gt; measurements and hyperspectral indices; 2) scaling-up of hyperspectral index-based C&lt;sub&gt;a+b&lt;/sub&gt; predictive relationships through RTM simulations; and 3) RTM inversions of C&lt;sub&gt;a+b&lt;/sub&gt; content. These methods were tested over two different segmentation strategies, including sunlit-vegetation and full-crown spectra, to assess the effects of the increased structural complexity.&lt;/div&gt;&lt;div&gt;Predictions of C&lt;sub&gt;a+b&lt;/sub&gt; from the greenhouse experiment were generally higher for empirical models that used TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil-Adjusted Vegetation Index) and CI (Chlorophyll index) hyperspectral indices when looking at full-crown rather than sunlit-vegetation pixels. RMSE measurements for full-crown models based on TCARI/OSAVI and CI across the three seasons ranged between 3.60 and 8.71 µg/cm&lt;sup&gt;2&lt;/sup&gt; and between 3.70 and 7.86 µg/cm&lt;sup&gt;2&lt;/sup&gt;, respectively. Using the scaling-up methodology, the TCARI-OSAVI-derived models were more stable across different methods of pixel extraction than the CI-derived models were, showing the smallest variations across measurement dates. Predictions of C&lt;sub&gt;a+b&lt;/sub&gt; in the field trial showed that PRO4SAIL2, which combines the PROSPECT-D model with the 4SAIL2 model and accounts for clumping and a more complex tree structure, was more accurate than PRO4SAIL, which couples PROSPECT-D with the original 4SAIL model, across both crown segmentation methods. Using PRO4SAIL2, predictions were more accurate for the full-crown spectra (R² = 0.82; RMSE = 3.35 µg/cm²) than for the sunlit-vegetation pixels (R² = 0","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110402"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap in carbon cycle studies: Meteorological station-based carbon flux dataset as a complement to EC towers","authors":"Wenqiang Zhang ,&nbsp;Geping Luo ,&nbsp;Rafiq Hamdi ,&nbsp;Xiumei Ma ,&nbsp;Piet Termonia ,&nbsp;Philippe De Maeyer ,&nbsp;Anping Chen","doi":"10.1016/j.agrformet.2025.110397","DOIUrl":"10.1016/j.agrformet.2025.110397","url":null,"abstract":"<div><div>The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p &lt; 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110397"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989111","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}