Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas

Domain: earth_science
Published: 2025-08-28
Editorial outcome: minor_revision

Abstract
Biogenic volatile organic compounds (BVOCs) are critical to biosphere-atmosphere interactions, profoundly influencing atmospheric chemistry, air quality and climate, yet accurately estimating their emissions across diverse ecosystems remains challenging. Here we introduce GEE-MEGAN, a cloud-native extension of the widely used MEGAN2.1 model, integrating dynamic satellite-derived land cover and vegetation within Google Earth Engine to produce near-real-time BVOC emissions at 10-30 m resolution, enabling fine-scale tracking of emissions in rapidly changing environments. GEE-MEGAN reduces BVOC emission estimates by 31% and decreases root mean square errors by up to 48.6% relative to MEGAN2.1 in human-disturbed forest edges, and reveals summertime BVOC emissions up to 25‑fold higher than previous estimates in urban areas such as London, Los Angeles, Paris, and Beijing. By capturing fine-scale landscape heterogeneity and human-driven dynamics, GEE-MEGAN significantly improves BVOC emission estimates, providing crucial insights to the complex interactions among BVOCs, climate, and air quality across both natural and human-modified environments.

Introduction
Biogenic volatile organic compounds (BVOCs) are critical drivers of biosphere-atmosphere interactions, significantly influencing ozone formation, secondary organic aerosol (SOA) production1,2, and broader atmospheric chemistry3,4. These processes have profound implications for air quality, climate, and human health. However, despite their significance, accurately quantifying BVOC emissions across diverse ecosystems remains a persistent challenge. This complexity stems from the intricate interplay of biological, climatic, and anthropogenic factors5,6, compounded by the dynamic nature of vegetation and environmental conditions.

Existing BVOC models, such as the widely used Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1)7, have made significant strides by providing a comprehensive framework for emission estimation. However, these models often rely on coarse spatial data and static parameters, which limit their ability to capture the heterogeneity of urban and natural landscapes, leading to inconsistent and inaccurate BVOC emission estimates across cities and regions8–12. Such limitations are particularly problematic in rapidly changing environments, such as urban areas13–15, where vegetation types, land use, and greening strategies are highly dynamic16–18. Furthermore, computational constraints hinder the application of high-resolution models over large geographic scales, leaving critical gaps in our understanding of BVOCs’ impacts on air quality and climate19,20.

To address these challenges, we introduce GEE-MEGAN, an innovative modeling framework (Fig. 1 and Fig. S1 in the Supplementary Information) that combines the strengths of MEGAN2.1 with the cloud-based computational power of Google Earth Engine (GEE)21,22. By integrating multi-source remote sensing data (e.g., MODIS, Landsat, Sentinel) and machine learning23,24, GEE-MEGAN dynamically refines critical model inputs (Fig. 1) such as leaf area index (LAI) and plant functional types (PFTs), enabling near-real-time, high-resolution BVOC emission estimates at spatial scales ranging from 10–30 m locally to global domains. These advancements in accuracy, resolution, and computational efficiency position GEE-MEGAN as a transformative tool for advancing research on biosphere-atmosphere interactions, particularly in understanding the complex feedback loops between BVOCs, climate and air quality.

Results
Better prediction of observed LAI and BVOC fluxes
GEE-MEGAN accurately captured both daily and monthly emission trends at regional scales compared to conventional models at identical spatial resolutions. For example, hourly isoprene emission rates for August 2010 across South America showed that daily average emissions closely aligned with trends derived from the inventory from MEGAN model coupled with the MOdel for Hydrocarbon emissions by the CANopy (MOHYCAN) and constraint by Ozone Monitoring Instrument (OMI) Formaldehyde (HCHO) observation (MEGAN2.1-MOHYCAN-OMI, details in Fig. S2a)25, achieving a Spearman Rank Correlation Coefficient of R=0.83 (p<0.001, N=31). Monthly regional emissions also exhibited minimal discrepancies when compared to the MEGAN default and MEGAN2.1 models for South America7,25,26, with differences ranging from 6.4% to 12% (Fig. S2c–h and Table S1). These results validate GEE-MEGAN’s consistency with the core dynamics of MEGAN2.1 across large temporal and spatial scales. Notably, GEE-MEGAN consistently produced isoprene emission estimates approximately 50% higher than those derived from OMI-MEGAN and Global Ozone Monitoring Experiment-2 (GOME2) based MEGAN model25,27. This discrepancy is consistent with recent studies suggesting that OMI-MEGAN underestimates emissions due to biases in the OMI formaldehyde (HCHO) column data28, which likely skew emission retrievals downward.

GEE-MEGAN utilizes high-resolution Landsat data for LAI estimation, outperforming conventional approaches that rely on coarser datasets, such as MODIS (Fig. 1E vs. Fig. 1B). When compared to observed LAI data (https://calvalportal.ceos.org/lpv-direct-v2.1), GEE-MEGAN’s Landsat-based LAI estimates reduced the mean absolute errors (MAE) by 12.5% at 500-m resolution and by 11.7% at a 3-km resolution relative to MODIS-based estimates (Fig. S3), and reduced the RMSE by 9.1% at 500-m resolution and by 3.6% at a 3-km resolution.

Based on these refined LAI inputs, GEE-MEGAN further improves BVOC emission estimates, as evaluated in Table S2. In comparison to observed BVOC fluxes across diverse ecosystems in North America, South America, Europe, and East Asia29–33 (Table S2), GEE-MEGAN outperformed MEGAN2.1, reducing RMSE by 21.6–48.6% and improving correlation coefficients (R) from 0.83 to 0.90. The most significant improvements were observed in regions with timely captured dynamic canopy changes.

Enhanced spatial resolution in original and disturbed forests
GEE-MEGAN captures greater spatial heterogeneity in BVOC emissions (Fig. 2a), especially at forest edges affected by human activities, containing emission hotspots and dead-zones, which are often fragmented or averaged out in coarser-resolution datasets34,35. Unlike conventional models that rely on static parameters and coarse spatial data, GEE-MEGAN dynamically integrates climatic norms and land use/cover change (LUCC) data with high-resolution satellite observations (Fig. 1D, Table S3), producing a more accurate and responsive emission factor map (EFMAP) capable of detecting abrupt changes.

In the Amazon rainforest, a critical BVOC source contributing over a quarter of global isoprene emission36, GEE-MEGAN reveals substantial discrepancies in BVOC emissions when compared to the MEGAN default with LUCC data from MODIS. Coarse models often fail to account for critical impacts of forest loss, introducing potential biases of Amazonian BVOC emissions. During the high-BVOC-emitting month of August in 2019, for example, GEE-MEGAN estimated 13% lower Amazonian isoprene emissions than the MEGAN default (Fig. 2a, b). While the discrepancies of 3.0–8.3% occurred deep inside the forest, they were especially pronounced at forest edges by forest degradation or deforestation (Fig. 2c). In these areas, the MEGAN default model overestimated emissions by up to 77% (average 31%) in the fifteen 2°×2° grid cells along the Arc of Deforestation35,37 (red line in Fig. 2c; e), while the overestimation became smaller, ranging 3–35% (average 9%) in the fifteen 2°×2° grids closer to the forest interior (blue line in Fig. 2c; d). Moreover, the MEGAN default model underestimated isoprene emission by up to 250% in some southeastern Brazil grids (yellow line in Fig. 2c; f), where recent reforestation or post-fire vegetation recovery occurred38,39.

In paired 2°×2° grids along the Arc of Deforestation (red grids) and approximately ~300 km into the inner forest (blue grids, Fig. 2c), the MEGAN default model showed an average isoprene emission difference of 6% (maximum 23%). In contrast, GEE-MEGAN estimated an averaged difference of 35% on average (maximum 68%), demonstrating its strength in capturing emission heterogeneity. Given that each grid cell represents an area of nearly 50,000 km2, such large under- or overestimation of reactive BVOC emissions can significantly misrepresent their roles and broader environmental impacts, especially at local and regional scales.

High-resolution BVOC emissions modeling in urban areas
GEE-MEGAN effectively captures the spatial variability of urban green spaces, enabling high-resolution BVOC emission modeling at 10–30 m resolution, resampled to 100 m. This ability is showcased in Fig. 3, where BVOC emissions for July 2019 in London, Paris, Los Angeles, and Beijing are quantified at finer urban scales by GEE-MEGAN, compared to the coarser 500-m resolution of the MEGAN default model. In Paris, the model identified key hotspots, and further evaluation using the BOXMOX chemical box model showed that GEE-MEGAN emissions achieved better agreement with observed isoprene concentrations than the default MEGAN model under identical ERA5 meteorological conditions9 (Fig. S4, Note S1 and Table S4). This result provides strong evidence that enhanced spatial detail in emission inputs improves model-observation consistency.

The high spatial heterogeneity captured by GEE-MEGAN, particularly in emission hotspots and high-value zones that are often fragmented or averaged in coarser-resolution models, consistently leads to higher emission estimates. For cities like London, Los Angeles, Paris, and Beijing, GEE-MEGAN estimated BVOC emissions for July 2019 to be 1.6-25 times higher than those predicted by the MEGAN default (Fig. 4). In Beijing, the larger discrepancy arises from fragmented vegetation being poorly resolved by lower-resolution land-cover inputs. GEE-MEGAN identifies an additional 2800.54 km² of vegetated area and a mean patch size of 0.67 km² within the same domain (Table S5), highlighting the critical role of spatial vegetation detail in driving urban BVOC emissions. This is primarily due to GEE-MEGAN’s incorporation of fine-scale vegetation components, such as isolated trees or smaller trees and shrubs40,41, which are often overlooked in coarser models (Table S6).

A more specific comparison of BVOC emissions in New York on July 2, 2018 revealed that GEE-MEGAN estimated total emissions at 27 tons, nearly 30% higher than the 21 tons predicted by NYC-MEGAN12. This discrepancy arises from GEE-MEGAN’s superior ability to account for the continuous urban-to-suburban coverage of green spaces, addressing limitations in vegetation coverage used in NYC-MEGAN.

GEE-MEGAN provides a more accurate representation of the roles of BVOC emissions in urban atmospheric chemistry. In West Los Angeles, while the MEGAN default model estimated zero BVOC emissions for most of the 251 communities in August 2019 (Fig. 5a), using compound-specific OH reactivity (OHR) coefficients and SOA yield factors20, GEE-MEGAN identified BVOC emissions that were, on average, about 23.0 times higher (Fig. 5b), along with 23.4 times greater OHR and 25.1 times higher SOA formation potential (SOAFP) (Fig. S5). More recent state-of-the-art airborne flux measurements in Los Angeles during summer 202119,20 revealed that biogenic terpenoids accounted for ~15% of VOC emissions, but contributed over 50% of OHR and ozone/SOA formation potentials. In comparison, GEE-MEGAN estimated these BVOCs contributed to 3.5% of VOCs, 26.3% of OHR, and 29.4% of SOAFP, while the MEGAN default model severely underestimated these contributions (1% of VOCs, ~15% of OHR and SOAFP; Fig. S6). Interestingly, significant gaps still exist between observations and GEE-MEGAN estimates. This could be due to biased emission factors or it may reflect the substantial contributions of volatile chemical products (VCPs) to terpenoid emissions in Los Angeles. This suggests that more accurate BVOC emission estimates also enable better quantification of the contribution of temperature-sensitive VCPs42 to total terpenoid emissions, and help disentangle their temperature-driven variability20.

Implications for air quality and climate
GEE-MEGAN provides a solid foundation for investigating the complex interplay between BVOCs and climate by offering near-real-time high-resolution emission modeling. BVOC emissions from forests are the largest source of reactive organic gases released into the atmosphere43,44, profoundly influencing radiative ozone (a potent greenhouse gas in the lower atmosphere) formation, aerosol growth, cloud formation, and atmospheric oxidative capacity, which controls the lifetime of methane (CH4) and thereby radiative forcing4,45–47. GEE-MEGAN captures BVOC emissions more accurately and efficiently across heterogeneous landscapes, particularly at forest edges where human disturbance is more pronounced. While GEE-MEGAN and the default MEGAN model yield similar isoprene emissions in pristine forests, large discrepancies emerge at forest edges, such as along the Brazilian Amazon’s Arc of Deforestation35. For example, during the high-BVOC-emitting month of August 2019, emission discrepancies are more pronounced in areas affected by forest degradation or deforestation (Fig. 2c), highlighting the need for high-resolution emission estimates, especially along global forest edges. Although pathways by which BVOCs influence climate are more complex than currently recognized48, emission estimation with high spatiotemporal resolution as provided by GEE-MEGAN is a prerequisite for better delineating human perturbations to atmospheric compositions and climatic impacts of BVOCs at local, regional and even global scales.

GEE-MEGAN’s high-resolution, near-real-time emission modeling capabilities can help refine climate predictions by providing insights into the strong and complex feedbacks between climate and BVOC36. As temperature-driven BVOC emissions increase under global warming20,49, GEE-MEGAN can offer critical insights into how ecosystems respond to and feedback on climate change43,50–52 by providing more accurate and timely estimates of BVOC emissions driven by both natural and anthropogenic forces, such as deforestation and afforestation. Additionally, GEE-MEGAN supports the assessments of climate resilience strategies53–55, such as urban greening and afforestation/reforestation initiatives56–58, ensuring that these efforts balance the cooling benefits of increased vegetation with minimization of BVOC-related warming and pollution. This dual focus on environmental and climatic impacts optimizes sustainability measures, helping mitigate air quality degradation while maximizing carbon sequestration and heat mitigation benefits.

GEE-MEGAN also equips urban planners and even non-experts with powerful tools to assess the air quality impacts of urban greening initiatives. As BVOCs increasingly influence urban atmospheric chemistry due to declining anthropogenic emissions and expanding greening spaces20,42,59, accurately capturing their emissions in urban areas, however, becomes challenging because of complex vegetation patterns and dynamic land-use changes. By resolving emissions at finer spatial scale, down to the street and community level, GEE-MEGAN substantially outperforms the 500-m resolution MEGAN default model. As illustrated by the examples from Los Angeles discussed above, GEE-MEGAN provides a more precise understanding of the contributions of biogenic emissions to total urban terpenoid emissions, which is essential for evaluating their roles in air pollution, including ozone and SOA formation19,20,42. This ability enables more nuanced exposure assessments by accounting for interactions between biogenic and anthropogenic sources across diverse finer-scale microenvironments in densely populated areas58,59. Additionally, GEE-MEGAN’s near-real-time modeling capability supports adaptive air-quality management in response to events such as heatwaves16,60–62 or rapid urbanization63, ensuring that green infrastructure projects maximize environmental benefits while minimizing unintended air pollution consequences.

Despite its advancements, GEE-MEGAN does not yet fully account for complex environmental factors influencing BVOC emissions64, such as soil moisture, nitrogen availability, evapotranspiration, and biotic stresses. Integrating these factors through GEE’s multisource, multi-scale data capabilities and advanced machine learning techniques could further refine its predictions. Although ERA5-Land hourly meteorology (~11 km) was used in this study to ensure global applicability and consistency, our results indicate that improving the spatial representation of vegetation yields substantial gains in BVOC emission accuracy. Nevertheless, incorporating high-resolution meteorological reanalysis products or AI-driven weather prediction models65,66 could further enhance GEE-MEGAN’s capacity to simulate urban-scale processes, offering deeper insights into BVOC-related dynamics in air quality and climate change.

In summary, GEE-MEGAN bridges critical gaps in BVOC emission modeling by combining scalability, precision, and real-time capabilities. These features make it a transformative tool for improving our understanding of roles played by biogenic emissions in a rapidly urbanizing and warming world, addressing both air quality and climate challenges. By providing actionable insights for sustainable urban planning and climate mitigation, GEE-MEGAN sets a benchmark in biosphere-atmosphere interaction modeling.

Methods
GEE-MEGAN model
The GEE-MEGAN framework integrates the MEGAN2.17 biogenic emission model with the computational capabilities of GEE, providing a flexible and scalable platform for estimating BVOC emissions across diverse spatial and temporal scales and bridging the gap between high-resolution emission modeling and large-scale atmospheric analyses.

Data source module. This module aggregates inputs from a range of data sources to enhance model accuracy and adaptability: Fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) climate data, MODIS-derived LAI, and reconstructed PFTs are used for large-scale analysis, and higher-resolution data such as Landsat imagery are employed for improving LAI precision, particularly in urban and regional studies (see Table S3 and Note S2). In addition to ERA5, GEE-MEGAN can incorporate other meteorological datasets such as Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System Version 2 (CFSv2), or the Global Land Data Assimilation System (GLDAS-2.1), and also support user-defined inputs such as WRF model output or CESM projections (Table S7).

Preprocessing module. To ensure consistency and reliability, this module processes the input through: (1) Temporal filtering: Aligning data to the required timeframes for simulations; (2) Spatial reprojection: Standardizing data projections to maintain spatial consistency; and (3) Resampling: Adjusting spatial resolution to match the scale of the analysis. Further details are provided in Note S3.

BVOC online calculation module. This module implements the core MEGAN2.1 computational framework within GEE, including canopy energy balance (Fig. S7) for simulating canopy conditions to estimate BVOC emissions, environmental correction factors for adjusting emissions based on environmental variables such as temperature and light, and standardized EFs7 for applying standardized emission rates for various vegetation types. Despite improving GEE-MEGAN computational efficiency and enabling near-real-time simulations, GEE-MEGAN produces emission activity factors and outputs consistent with MEGAN2.1 when using identical inputs (see Fig. S8 and Note S4).

Output and post-processing module. This module supports both online and local data processing, allowing data export for integration into atmospheric chemistry models. Outputs are available in formats compatible with subsequent analyses, with additional details outlined in Note S5.

Scalability and customization. GEE-MEGAN provides three operational scales tailored to different applications: local scale (10–30 m) that utilizes high-resolution remote sensing and machine learning (e.g., Random Forest algorithm) to refine land use and cover change (LUCC), enhancing accuracy in urban and regional estimates, regional scale (500 m) that balances resolution and computational efficiency for broader geographic analyses, and global scale (500–5000 m) that employs coarser datasets to support global-scale modeling of BVOC emissions (see Table S3).

Box modeling
To evaluate isoprene concentrations and validate GEE-MEGAN estimates, atmospheric simulations were performed using the BOXMOX model67, based on the Model for Ozone and Related Chemical Tracers (MOZART-4) chemical mechanism. The simulations focused on the Paris Rive Gauche site in southeastern Paris, where Proton-Transfer-Reaction Mass Spectrometry measurements of ambient isoprene concentrations were available for comparison with GEE-MEGAN outputs9. Key inputs for the BOXMOX simulations included meteorological data (2-m air temperature, boundary layer height, and photolysis rates derived from the ERA5 reanalysis data) and anthropogenic emissions (pollutant data such as nitrogen oxides, VOCs and other species obtained from Copernicus Atmosphere Monitoring Service).

Refinement of PFTs, emission factor maps (EFMAP) and LAI in GEE-MEGAN
PFTs (Table S8) in GEE-MEGAN were derived using a combination of the Köppen-Geiger climate classification map68 (Table S9) and the MODIS MCD12 land cover dataset69 (Fig. S9 and Table S10), following established methodologies used in the evolution of the Community Land Model (CLM)70. This integration leverages climate reanalysis data and MODIS land use categories to ensure accurate and consistent PFTs mapping. Detailed derivation approaches are provided in Table S11 and Note S6, with comparison of multiple PFTs classifications shown in Fig. S10.

For local modeling at 30 m and 10 m resolution, high-resolution datasets from Landsat and Sentinel satellites were used, with a supervised machine learning algorithm, specifically a random forest algorithm (ee.Classifier.smileRandomForest), to update near-real-time land cover mapping (Note S7 and S8). The random forest was configured with 50 decision trees, and the maximum depth and other parameters were set to default values. Training labels were generated using a combination of datasets, including MODIS MCD1269, GLC_FCS30D71, Global Sentinel-2 10m Canopy Height72, EAS 10m Annual Land Cover73, and Finer Resolution Observation and Monitoring of Global Land Cover 10m (FROM-GLC10) datasets74. Each dataset was initially mapped to the Annual International Geosphere-Biosphere Programme classification69, and a hybrid sampling approach was employed to generate training labels. This approach combines spatially uniform sampling, which selected 5000 points randomly across the target area, and stratified sampling, which selected 1000 points from each LUCC classification category.

The predictor variables used in the random forest model were derived from Landsat and Sentinel satellite bands (visible, infrared, and near-infrared) as well as from calculated indices, including the normalized difference vegetation index, enhanced vegetation index, and soil adjusted vegetation index75. Historical data were filtered to create training labels and corresponding attributes, which were then used to infer land use classification based on near-real-time remote sensing data. Finally, the predicted LUCC data were divided into PFTs based on canopy height and climate rules, and these were subsequently used as inputs for GEE-MEGAN.

Dynamic EFMAP. The dynamic EFMAP module adjusts BVOC emission factors in real-time to account for environmental fluctuations. This dynamic refinement enhances the accuracy of BVOC emission estimates, as detailed in Note S7 in the supplementary materials.

LAI. The LAI estimation was improved by combining MODIS-derived LAI data with Landsat-based LAI interpolated on matching dates (Note S9). This hybrid approach reduces temporal gaps and enhances spatial resolution, achieving 9.1% reduction in RMSE at 500-m resolution and 3.6% reduction in RMSE at 3-km resolution. The refined LAI method was validated against the global DIRECT ground-based LAI dataset (Fig. S3A), ensuring reliability and accuracy for both local and global applications.

Multiscale simulation framework
To balance near-real-time applicability across spatial scales, GEE-MEGAN uses consistent meteorological inputs from ERA5-Land hourly while tailoring land-use and vegetation inputs across 100 m, 500 m, and 5 km scales, with varying data sources for LUCC and LAI to reflect spatial heterogeneity in emissions. For scales smaller than 500 m, high-resolution Landsat data were utilized to calculate LUCC and PFTs, while the LAI was derived using an enhanced MCD15 method with Landsat observations. At the 500 m scale, LUCC data were sourced from MCD12Q1 dataset, and LAI data were obtained using the MODIS MCD15A3H product. At the coarser 5 km scale, LUCC inputs were extracted from the MODIS MCD12C1 dataset, and LAI was modeled using the LAI/FPAR Climate Data Record (CDR)76.

At coarser spatial resolutions (e.g., 500 m), urban green space may be underrepresented or misclassified as impervious surfaces, resulting in negligible urban BVOC emissions. Accurate estimation of emissions in urban areas therefore requires high-resolution LUCC and LAI inputs, which GEE-MEGAN supports. The spatial resolution of GEE-MEGAN outputs aligns with the resolution of the input datasets and can be flexibly selected depending on the application. For urban or rapidly changing landscapes, we recommend using high resolution inputs (10–30 m); for regional or global assessments, coarser inputs (500 m to 5 km) are more practical and computationally efficient.

Validation
The accuracy of GEE-MEGAN was evaluated by comparing emission estimates with those of MEGAN2.1 across multiple sites and scales. Validation metrics included R², root mean squared error (RMSE), MAE, and Pearson correlation, for a comprehensive assessment of the model’s performance. These metrics were also applied to assess improvements in LAI estimates.

The GEE-MEGAN framework (Fig. S1) significantly improves the computational efficiency and scalability of BVOC emission modeling by leveraging the cloud-based capabilities of GEE21,22. This innovation enables near-real-time, high-resolution simulations across large spatial domains while minimizing both computational demands and data transfer requirements. For instance, using a consistent input dataset for South America (60°S–13°N, 35–90°W) at a 500-m resolution and operating on a local Intel Xeon Gold 6230R CPU (2.1 GHz), MEGAN2.1 required 736.9 s per species to complete the simulation while GEE-MEGAN achieved comparable results in just 7.2 to 9.1 s per species, delivering an 81- to 102-fold increase in processing speed; moreover, GEE-MEGAN reduced data transfer by 97–99% compared to traditional models, facilitating efficient processing even for large-scale or high-resolution applications.

GEE-MEGAN accurately replicates the core dynamics of MEGAN2.1 while significantly improving efficiency and scalability7. Simulations using input datasets from North America (https://bai.ess.uci.edu/megan/data-and-code/megan21, last retrieved September 14, 2024) revealed high consistency between MEGAN2.1 (Fig. S8a, e, i, m) and GEE-MEGAN (Fig. S8b, f, g, n), with a correlation coefficient of 1.0 and a total error of less than 0.1 across 16,576 data points. Minor discrepancies (Fig. S8d, h, l, p) were observed due to differences in computational environments but remained within acceptable margins (RMSE<3.0×10−2; Fig. S8 and Table S12).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Report for NCOMMS-25-26934-T\nIn this report I will briefly address the topics suggested by the Nature Communications \"Guidelines for Referees\", before\nproviding a list of more specific comments. Most of my comments are minor. Overall, I found that this study represents a\nsignificant positive development in BVOC emission modelling that will be of general interest to the atmospheric science\ncommunity. In particular, the model developments presented here will be very useful for researchers working on a broad\nrange urban air quality and global climate modelling problems.\nKey Results:\nThe authors have developed a new version of the widely-used BVOC emission model MEGAN which takes advantage of\nthe Google Earth Engine (GEE) cloud computing platform. This new GEE-MEGAN model improves on the original MEGAN\nmodel by incorporating high-resolution land-use / landcover data from multiple satellite sources in near real-time. The high-\nresolution emission factor maps in GEE-MEGAN can be used to estimate urban BVOC emissions at scales as fine as 10\nmetres, while the near real-time updates to these maps allows for the impact of forest disturbances such as wildfires on\nBVOC emissions to be quantified. The use of cloud-based computing with Google Earth Engine leads to significant\nperformance improvements compared to the default MEGAN model, with speedups of 80 – 100 times achieved by the\nauthors.\nValidity:\nIn my opinion, the manuscript does not contain major flaws that should prohibit its publication.\nOriginality and significance:\nGEE-MEGAN represents a significant step forward in BVOC emission modelling, and I believe it will be of interest to the\nglobal atmospheric composition / climate modelling community. In particular, the ability to efficiently obtain emission factor\nmaps at 10 – 30 metre resolution in urban areas will be extremely useful for urban air quality modelling and for assessing the\nair quality and climate impacts of land-use changes within cities (e.g., urban greening strategies). On a global scale, the use\nof dynamic emission factor maps that can be updated in near real-time will help prevent inconsistencies in global\natmospheric models due to outdated and / or coarse resolution emission factor maps, as the authors demonstrated for the\ncase of the South American wildfires in August 2019. From my subjective point of view, the ability to easily generate and\nexport high-resolution, up-to-date BVOC emissions for use as inputs in other models (such as high-resolution urban air\nquality models) represents the most interesting and useful part of GEE-MEGAN.\nData & methodology:\nThe methodology is explained clearly and is well-defended by the authors. The \"Methods\" section provides a fairly\nstraightforward overview of the methodology, while the \"Supplementary Information\" fills in many of the details. I see no\nobvious issues with the data presented in the paper. However, I have not been able to access the GitHub repository, so I am\nunable to assess the raw data or comment on its validity\nAppropriate use of statistics and treatment of uncertainties:\nThe statistics presented in this paper are simple and appropriate. Additional statistics on the performance of the machine\nlearning algorithm described on lines 251 – 261 (e.g., some discussion of training vs testing data, model validation) may be\nuseful for readers like myself who are not very experienced with these techniques, perhaps as an additional note in the\nSupplementary Information.\nConclusions:\nI find that the conclusions and data interpretation are robust, valid, and reliable.\nSuggested improvements:\nSome more discussion on the impact of weather / climate data resolution on GEE-MEGAN could be useful. The authors\nmention near the end of the paper that GEE-MEGAN could be extended with higher-resolution weather forecasts (e.g., AI-\ngenerated forecast products), but throughout the paper and supplementary material it seems that only ERA5 is actually used.\nI'm curious what impact this has on the high-resolution simulations presented in this paper. In particular, for the comparison\nwith observed isoprene concentrations at the PRG site using BOXMOX, does the model-observation agreement change\nwhen using higher-resolution weather input data to drive the model? In my view this is a fairly minor point that does not\nseriously compromise the quality of the paper.\nHowever, in the context of high-resolution urban emission modelling, the resolution of the weather input data should at least\nbe briefly discussed because this will determine whether local-scale emission effects due to urban meteorology (e.g., heat\nislands, street canyons, surface heating of roads vs green spaces) can be reliably simulated. Several of my specific\ncomments are related to this issue (see below).\nReferences:\nThis manuscript appropriately references previous literature. The references are sufficiently up-to-date so that this work can\nbe compared with other recent developments in BVOC emission modelling (e.g., the NYC-MEGAN model, recent validation\nof OMI satellite data).\nClarity and context:\nOverall, the paper is well-written and has a logical structure. The introduction and conclusions are appropriate. I think the\nabstract should make it more clear what the main difference is between GEE-MEGAN and the original MEGAN model.\nThroughout the text the authors emphasize that GEE-MEGAN can be run with very high resolution (10-30m) LUCC / LAI\ndata which can be updated in near-real time to obtain accurate emission factor maps. This seems to be one of the main\nadvantages of GEE-MEGAN, but this is not obvious from the description of GEE-MEGAN in the abstract (lines 24 – 26). For\nreaders who have not used the Google Earth Engine before, it's not clear from the abstract alone why this is a good thing.\nReviewer Expertise:\nI have only an introductory-level knowledge of machine learning techniques, so I do not feel sufficiently qualified to\nthoroughly evaluate the use of a random forest machine learning algorithm on lines 251 – 261. However, from my limited\nexperience with machine learning, I do not see any obvious issues with the authors' use of this technique.\n= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\nSPECIFIC COMMENTS\nMain Text:\n• Line 27: \"human-disturbed\"\n• Lines 66 – 68: I probably wouldn't call this an \"OMI-based MEGAN model\". The data you are talking about here is a top-\ndown inversion using OMI HCHO observations, with the MEGAN model used as the a priori emission estimate. So you\nshould probably specify that these emissions are the result of an inversion analysis, not simply a model output.\n• Lines 79 – 83: Figure S3 shows different LAI estimates derived from Landsat or MODIS compared with observations. It\ndoesn’t show a comparison between GEE-MEGAN and MEGAN2.1, so I’m not sure I understand this sentence. Are you\nsaying that the updated Landsat-based LAI estimates are in better agreement with observed LAI than the older MODIS\nmethod, or are you saying that the isoprene emission estimates using the Landsat-based LAI are more accurate? If you’re\ntalking about isoprene emission estimates you should show those in a figure, since they aren’t in Figure S3.\n• Lines 84 – 85: Same as previous comment, are you comparing LAI estimates with observations or are you comparing\nisoprene emission estimates between GEE-MEGAN and MEGAN2.1? In Table 1 you are comparing BVOC emission\nestimates at various sites, but in this paragraph and in Figure S3 you’re talking about different LAI estimates.\n• Lines 87 – 114: This is a very nice section. I especially like Figure 2.\n• Lines 119 – 120: Consider rephrasing this sentence. If I understand correctly, you don’t compare GEE-MEGAN with the\ndefault MEGAN model in Figure 3, you just show GEE-MEGAN at 2 different resolutions. But this sentence implies that\nFigure 3 shows GEE-MEGAN and the default MEGAN.\n• Lines 120 – 121: What meteorology data did you use to drive GEE-MEGAN for the comparison with the Paris\nmeasurements? In most of this section you are talking about the spatial variability of BVOC emissions, which makes sense\ngiven that high spatial resolution LUCC / LAI data seem to be one of the key advantages of GEE-MEGAN. But when you’re\ncalculating an emission time series like in Figure S4, I would expect that using coarser-resolution meteorology like ERA5 to\ndrive a high-resolution model could be a significant source of uncertainty. What happens to the model-observation\nagreement if you run GEE-MEGAN with higher-resolution weather inputs?\n• Lines 124 – 126: Do you know why the differences seen in Beijing were so much larger than in Los Angeles, London, or\nParis?\n• Lines 137 – 138: Are these numbers based on atmospheric box model calculations (e.g., BOXMOX), or is this simply the\ntotal OH reactivity due to BVOC emissions estimated with MEGAN?\n• Lines 146 – 147: I don’t quite understand this sentence. Please clarify how you might use GEE-MEGAN to better\nunderstand VCP terpenoid emissions, and how this is related to the temperature effects described in Pfanerstill et al (2024)\n(your reference #20).\n• Lines 182 – 184: Please clarify what you mean by \"a more precise understanding of urban terpenoid emission sources and\ntheir roles in air pollution...\". I think this is related to your point on line 146-147, which I did not understand.\n• Lines 193 – 196: I think you need to emphasize this a bit more. You don’t really talk about the resolution of weather /\nclimate input data in the main text, but for high-resolution urban modelling this is an important piece of the puzzle (e.g.,\naccurately modelling variations in temperature in urban areas due to landcover and street layouts). Especially if you want to\nuse GEE-MEGAN investigate things like urban greening initiatives or other local-scale climate mitigation strategies, is it\nreasonable to use a coarser data product like ERA-5?\n• Lines 210 – 211: Can GEE-MEGAN be easily used with other weather data (e.g., other reanalyses like MERRA2, model\noutput like WRF, or climate models like CESM)? Especially for urban modelling, it would be useful to use GEE-MEGAN with\nalternative weather inputs.\n• Lines 223 – 224: When you say “align with expectations”, do you mean that they agree with the default MEGAN2.1 model?\n• Lines 279 – 280: It appropriate to use ERA-5 meteorology when driving a 30 – 100 m resolution simulation?\n• Lines 316 – 318: I was unable to access the GitHub repository containing the GEE-MEGAN code. Please double-check\nthis link.\nFigures:\n• Figure 1: Spelling mistake in caption (\"MGEAN2.1\")\n• Figure 3: If possible, can you add another Google Earth imagery column for the zoomed out panels (a, d, g, and j)? It is\nhard to interpret the emission maps in those panels without a reference image, except for Los Angeles where the coastline\nand mountains are somewhat visible in panel (a).\n• Figure 4: I'm not sure I understand the figure caption. \" The bar depict the emissions of isoprene, monoterpenes, and\nsesquiterpenes for each city, focus on regions with nighttime light percentages above 80% (nighttime light data from the\nNational Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite).\" Does this mean the urban boundaries for\nthese cities are defined based on a nighttime light threshold of 80%, or does it mean something else?\nSupplementary Information:\n• Note S2: Spelling error (\"framwork\")\n• Note S4: Can you explain the steps described in Figure S7 in more detail here? For readers who are not familiar with\nMEGAN or canopy models, this section may not provide enough information for them to understand that figure.\n• Note S6: Several spelling errors (\"basedon\", \"distinquished\", \"regios\"); Repeated word (\"Retain the the MODIS\nMCD12Q1...\")\n• Note S7: Missing word (\"...are employed by GEE-MEGAN...\")\n• Figure S1: Some of the text bubbles are unclear, especially in panel D. For example, “computational chemical” is not\nmeaningful. I understand from the caption you intend to export these emissions for use in atmospheric models, so this should\nbe more obvious in the figure.\n• Figure S2: Panels (c) – (h) need labels. Also, because the colour bar scale is different on each panel, it is hard to directly\ncompare the different emission estimates other than the spatial distribution. Consider remaking these panels using the same\ncolour bar so the differences are more obvious.\n• Figure S4: What is the large spike in simulated and observed isoprene on July 18th? It’s hard to see what’s happening in\nthe rest of the time series because the spike at the beginning stretches the y-axis scale. Consider remaking the figure\nwithout those data or explain what the large isoprene spike is in the caption.\n• Figure S8: Which gamma factor are we looking at here? In Figure S7 and Note S4 we are looking at gamma_T and\ngamma_P (temperature and sunlight activity factor). Is that what this figure shows, or are we also including the effects of the\ncanopy environment (e.g., gamma-LAI?)\n• Table S3: For the 500m resolution MCD12Q1 dataset, all the cities have zero vegetation coverage. Does this mean that if\nyou run GEE-MEGAN at 500m resolution, there will be no urban BVOC emissions? In the main text in the “Multiscale\nSimulation Framework” section, you say that you can run GEE-MEGAN with coarser LUCC and LAI inputs. But this table\nseems to imply that if you use the MCD12Q1 inputs, you will get zero urban BVOC emissions. If this is true, you should\nclearly explain this somewhere in the text, since accurate urban BVOC emissions seem to be one of the main advantages of\nGEE-MEGAN.", "reviewer_2": "Zhang et al. developed an innovative modeling framework, GEE-MEGAN, by integrating multiple remote sensing products\nwith machine learning–based tuning. Compared to the default MEGAN scheme, GEE-MEGAN offers significantly higher\nspatial resolution, enabling it to capture finer-scale features of BVOC emissions, particularly in disturbed forests and urban\nareas. This framework represents a valuable advancement, and such improvements in BVOC emissions are critical for\nbetter modeling of air pollution, especially in regions where air quality is highly sensitive to BVOC emissions.\nHowever, I have several concerns that I hope the authors can address to strengthen the manuscript:\nMajor Comments\n1. Evaluation of GEE-MEGAN is relatively weak\nThe comparison between GEE-MEGAN and the default MEGAN scheme appears more as a consistency or sanity check\nrather than a rigorous evaluation. Since GEE-MEGAN builds upon the default scheme but uses different input sources and a\nfiner spatial resolution, agreement between the two is expected. For example, the statement in lines 65–66, that GEE-\nMEGAN “accurately captures the daily and monthly trends” compared to the default MEGAN, is not particularly meaningful. If\nthe two products did not agree at regional scales, that would indicate a problem with GEE-MEGAN.\nA more meaningful evaluation involves comparison with observed isoprene fluxes (as presented in Table 1), which is far\nmore informative than comparison against MEGAN 2.1. That said, the performance of GEE-MEGAN in Table 1 is\nambiguous. In some cases, such as the BR-Sa1 and Qianyanzhou sites, MEGAN 2.1 shows lower RMSE and MAE than\nGEE-MEGAN. This contradicts the claim in lines 84–85 that GEE-MEGAN outperforms MEGAN 2.1.\n2. Lack of quantitative discussion on air quality and climate implications\nThe section “Implications for air quality and climate” lacks quantitative analysis. As the authors note, BVOC emissions\ninfluence ozone, aerosols, cloud formation, and atmospheric oxidative capacity. Given this context, I would expect the\nauthors to quantify these impacts. For instance, how much ozone or SOA production changes when switching from the\ndefault MEGAN to GEE-MEGAN. Without such estimates, the implications remain speculative.\nOther Comments\n• Note S1: Does the box model constrained by GEE-MEGAN agree better with observations than the version using default\nMEGAN? If so, this would strengthen the case that GEE-MEGAN improves BVOC emission accuracy, and I suggest\nemphasizing this point.\n• Spatial resolution inconsistencies: GEE-MEGAN is shown at several spatial resolutions (e.g., 500 m and 1500 m in Table\n1, and aggregated to 2° × 2° over the Amazon). What is the recommended or default resolution for GEE-MEGAN?\nPresumably, it should align with the resolution of the input datasets. The authors should clarify this.\n• Comparison to Pfannerstill et al. (2024, EST): That study presents a 4 km BVOC emission map over Los Angeles derived\nfrom flux measurements. How does GEE-MEGAN compare to those estimates?\n• Lines 139–147: This paragraph would be better placed in the “Implications for air quality and climate” section, where it\naligns more naturally with the discussion of impacts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors have developed a new cloud-native version of the BVOC emission model MEGAN which\nuses the Google Earth Engine (GEE) to incorporate high-resolution near real-time land-use / landcover data from multiple\nsatellite platforms. This approach allows for the creation of emission factor maps at resolutions as fine as 10 metres\n(essential for modelling in complex urban environments), while the use of near real-time data allows for the impact of land\ndisturbances like forest fires on BVOC emissions to be quantified.\nIn this revised version, the authors have improved the manuscript and addressed all of my concerns from the previous\nversion. I found the following changes particularly beneficial:\n- The GEE-MEGAN code has now been provided along with instructions for setting up a suitable computing environment\nand running the code. I was able to run the code on my own computer as well as using the provided \"Code Ocean\" link. The\ncode is well-commented and structured in a logical way, and I did not find any obvious faults with it.\n- The authors fully addressed my concerns about meteorological input data resolution in detail. Specifically, they performed\na sensitivity analysis to assess the impact of meteorological resolution using the BOXMOX model at the PRG measurement\nsite. They used both GEE-MEGAN and MEGAN2.1 with ERA-5 hourly meteorology (11km resolution), and they used GEE-\nMEGAN with meteorology from a high-resolution (1km) WRF simulation. The WRF simulation itself was validated against\nmeteorological observations at multiple sites in Paris, which helped mitigate additional uncertainties which can be\nintroduced by using weather model output to drive MEGAN. They showed that the improved spatial details in GEE-MEGAN\nhad a greater benefit on model-observation agreement at PRG than using high-resolution meteorology, consistent with\nprevious literature. In my opinion the inclusion of this sensitivity test sufficiently addresses the issue of meteorological input\nresolution.\n- The authors have provided a very clear explanation for why the differences between MEGAN2.1 and GEE-MEGAN were\nso much larger in Beijing compared to Paris, London, and Los Angeles. Specifically, they quantified the vegetation\ndistribution in Table S5 and showed that Beijing has many large patches of vegetation (urban forests, parks, green corridors)\nthat aren't properly captured in the default MEGAN inputs. This leads to a much larger landcover difference between the\ndefault MEGAN2.1 and GEE-MEGAN in Beijing compared to the other cities.\n- The authors have added additional statistical evaluations of the machine learning algorithm in the Supplementary\nInformation, which I found useful.\n- In response to Reviewer #2's comments, the authors have significantly improved the evaluation of GEE-MEGAN. In\nparticular, they have explicitly clarified that the purpose of the large scale comparison between GEE-MEGAN and\nMEGAN2.1 is a form of sanity check to verify the internal consistency and numerical stability of the model; they now use an\nobservationally-derived emission factor to evaluate GEE-MEGAN and MEGAN2.1 at the BR-SA1 site; they have added an\nurban site in Beijing to evaluate GEE-MEGAN and MEGAN2.1 in complex urban environments, showing clear reductions in\nmodel errors under these conditions; and they have provided a plausible explanation for why MEGAN2.1 seemingly\noutperformed GEE-MEGAN at the Qianyanzhou site in terms of RMSE, suggesting that the use of Relaxed Eddy\nAccumulation measurements instead of Eddy Covariance added additional variability and uncertainty to the observations. I\nfeel that with these changes, the evaluation of GEE-MEGAN is more robust.\nIn addition to these specific changes, the authors have addressed all of my other questions, concerns, and requests for\nclarifications. In particular, many of the figures have been improved, and unclear / ambiguous text has been clarified.\nOverall, I believe this manuscript has been significantly improved from the previous version. I thank the authors for the\nsignificant effort they have put into updating the manuscript and responding to my queries. It should be of broad interest to\nthe atmospheric science community, as the results are applicable on multiple scales from urban air quality to global climate\nperturbation impacts (e.g., large forest disturbances). I do not find any outstanding issues that should prevent publication of\nthis manuscript.\n(Remarks on code availability)\nI was able to run the code using the private Code Ocean link. I was also able to setup a Python environment and run the\ncode on my own computer. I have not found any obvious issues with the code or its output.\nOverall, I find the code to be well-commented and structured in a logical way. The included README file provides simple\ninstructions for setting up and running the code which I was able to follow without difficulty. I believe this code will be very\nuseable to anyone in the community who is familiar with Python. In particular, the inclusion of a straightforward namelist file\nand run script makes it very easy to play around with the model and adapt the workflow as needed without having to modify\nmost of the .py files.", "reviewer_2": "The review response and revised manuscript have fully addressed the concerns I raised in the first round. I have no further\ncomments.\n(Remarks on code availability)"}, "author_response": "Response to Reviewers’ Comments\nThank you again for submitting your manuscript \"Improved modelling of biogenic emissions\nin human-disturbed forest edges and urban areas\" to Nature Communications. We have now\nreceived reports from 2 reviewers and, based on their comments, we have decided to invite a\nrevision of your work. Your revision should address all the points raised by our reviewers (see\ntheir reports below).\nBoth reviewers, while highly positive, make numerous constructive comments which will\nstrengthen your manuscript. In particular we will need to see a more rigorous comparison\nbetween GEE-MEGAN and MEGAN including improved quantification of the implications of\nwhat GEE-MEGAN reveals, and improved discussion on the effects that the resolution of inputs\nwill have on the reliability of your approach. Please note that Nature Communications has a\nmore generous word limit than the journal you initially submitted to (5000 main text, 10\nfigures/tables, unlimited methods and supplementary information) which should allow you to\nexpand on all points necessary.\nWhen resubmitting, you must provide a point-by-point response to the reviewers’ comments.\nPlease show all changes in the manuscript text file with track changes or colour highlighting.\nIf you are unable to address specific reviewer requests or find any points invalid, please explain\nwhy in the point-by-point response.\nImportant: In addition to the above, you must comply with the following editorial requests;\nwe will not be able to proceed with your revised manuscript otherwise. Please also see\nthe Nature Communications formatting instructions, which you may find useful while\npreparing your revised manuscript.\nResponse: We sincerely thank you for the opportunity to revise our manuscript titled\n“Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas.”\nWe are grateful for the thoughtful and constructive feedback provided by both reviewers.\nIn the revised manuscript, we have carefully addressed all reviewer comments in a detailed\npoint-by-point response. In particular, we have substantially strengthened the comparison\nbetween GEE-MEGAN and MEGAN2.1, including additional evaluation against observed\n1\nisoprene fluxes across multiple sites, incorporation of site-specific emission factors to improve\nrealism, expanded discussion of methodological uncertainty, particularly with respect to spatial\nresolution, and clarified implications of high-resolution emissions for urban air quality and\nclimate, with quantitative context drawn from recent literature.\nWe have also revised the manuscript text to improve clarity and expanded relevant sections, in\nline with the more generous word limit provided by Nature Communications.\nWe thank you again for your guidance and the opportunity to improve our manuscript.\nThe reviewer’s comments are shown in black and our responses are given in blue. All changes\nin the manuscript are highlighted in red in this response file and the track-changes file.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nReport for NCOMMS-25-26934-T\nIn this report I will briefly address the topics suggested by the Nature Communications\n\"Guidelines for Referees\", before providing a list of more specific comments. Most of my\ncomments are minor. Overall, I found that this study represents a significant positive\ndevelopment in BVOC emission modelling that will be of general interest to the atmospheric\nscience community. In particular, the model developments presented here will be very useful\nfor researchers working on a broad range urban air quality and global climate modelling\nproblems.\nKey Results:\nThe authors have developed a new version of the widely-used BVOC emission model MEGAN\nwhich takes advantage of the Google Earth Engine (GEE) cloud computing platform. This new\nGEE-MEGAN model improves on the original MEGAN model by incorporating high-\nresolution land-use / landcover data from multiple satellite sources in near real-time. The high-\nresolution emission factor maps in GEE-MEGAN can be used to estimate urban BVOC\n2\nemissions at scales as fine as 10 metres, while the near real-time updates to these maps allows\nfor the impact of forest disturbances such as wildfires on BVOC emissions to be quantified.\nThe use of cloud-based computing with Google Earth Engine leads to significant performance\nimprovements compared to the default MEGAN model, with speedups of 80 – 100 times\nachieved by the authors.\nValidity:\nIn my opinion, the manuscript does not contain major flaws that should prohibit its publication.\nOriginality and significance:\nGEE-MEGAN represents a significant step forward in BVOC emission modelling, and I\nbelieve it will be of interest to the global atmospheric composition / climate modelling\ncommunity. In particular, the ability to efficiently obtain emission factor maps at 10 – 30 metre\nresolution in urban areas will be extremely useful for urban air quality modelling and for\nassessing the air quality and climate impacts of land-use changes within cities (e.g., urban\ngreening strategies). On a global scale, the use of dynamic emission factor maps that can be\nupdated in near real-time will help prevent inconsistencies in global atmospheric models due\nto outdated and / or coarse resolution emission factor maps, as the authors demonstrated for the\ncase of the South American wildfires in August 2019. From my subjective point of view, the\nability to easily generate and export high-resolution, up-to-date BVOC emissions for use as\ninputs in other models (such as high-resolution urban air quality models) represents the most\ninteresting and useful part of GEE-MEGAN.\nData & methodology:\nThe methodology is explained clearly and is well-defended by the authors. The \"Methods\"\nsection provides a fairly straightforward overview of the methodology, while the\n\"Supplementary Information\" fills in many of the details. I see no obvious issues with the data\npresented in the paper. However, I have not been able to access the GitHub repository, so I am\nunable to assess the raw data or comment on its validity\nThe statistics presented in this paper are simple and appropriate. Additional statistics on the\nperformance of the machine learning algorithm described on lines 251 – 261 (e.g., some\ndiscussion of training vs testing data, model validation) may be useful for readers like myself\nwho are not very experienced with these techniques, perhaps as an additional note in the\nSupplementary Information.\nConclusions:\nI find that the conclusions and data interpretation are robust, valid, and reliable.\nreply：\nSuggested improvements:\nSome more discussion on the impact of weather / climate data resolution on GEE-MEGAN\ncould be useful. The authors mention near the end of the paper that GEE-MEGAN could be\nextended with higher-resolution weather forecasts (e.g., AI-generated forecast products), but\nthroughout the paper and supplementary material it seems that only ERA5 is actually used. I'm\ncurious what impact this has on the high-resolution simulations presented in this paper. In\nparticular, for the comparison with observed isoprene concentrations at the PRG site using\nBOXMOX, does the model-observation agreement change when using higher-resolution\nweather input data to drive the model? In my view this is a fairly minor point that does not\nseriously compromise the quality of the paper.\nHowever, in the context of high-resolution urban emission modelling, the resolution of the\nweather input data should at least be briefly discussed because this will determine whether\nlocal-scale emission effects due to urban meteorology (e.g., heat islands, street canyons, surface\nheating of roads vs green spaces) can be reliably simulated. Several of my specific comments\nare related to this issue (see below).\nThis manuscript appropriately references previous literature. The references are sufficiently up-\nto-date so that this work can be compared with other recent developments in BVOC emission\nmodelling (e.g., the NYC-MEGAN model, recent validation of OMI satellite data).\nClarity and context:\nOverall, the paper is well-written and has a logical structure. The introduction and conclusions\nare appropriate. I think the abstract should make it more clear what the main difference is\nbetween GEE-MEGAN and the original MEGAN model. Throughout the text the authors\nemphasize that GEE-MEGAN can be run with very high resolution (10-30m) LUCC / LAI\ndata which can be updated in near-real time to obtain accurate emission factor maps. This seems to\nbe one of the main advantages of GEE-MEGAN, but this is not obvious from the description\nof GEE-MEGAN in the abstract (lines 24 – 26). For readers who have not used the Google\nEarth Engine before, it's not clear from the abstract alone why this is a good thing.\nReviewer Expertise:\nI have only an introductory-level knowledge of machine learning techniques, so I do not feel\nsufficiently qualified to thoroughly evaluate the use of a random forest machine learning\nalgorithm on lines 251 – 261. However, from my limited experience with machine learning, I\ndo not see any obvious issues with the authors' use of this technique.\nResponse: We sincerely thank the reviewer for your hard work in reviewing our manuscript,\nand for your positive and constructive feedback. Below, we provide a detailed, point-by-point\nresponse to your specific comments. In particular, we have addressed the impact of\nweather/climate data resolution on model performance, and included additional statistical\nevaluation of the machine learning algorithm (Newly added in Note S8 in the Supplementary\nInformation). We have also revised the abstract to more clearly highlight the main differences\nbetween GEE-MEGAN and the original MEGAN model (Lines 25-29). After adding new\nreferences and supporting figures and tables throughout the manuscript, they have been updated\n5\naccording to their sequence of appearance in the main text. All changes have been incorporated\ninto the revised manuscript and are highlighted using track changes.\nRevised Text for Abstract (Line 25-29): “Here we introduce GEE-MEGAN, a cloud-native\nextension of the widely used MEGAN2.1 model that integrates dynamic satellite observations\nof land cover and vegetation within Google Earth Engine (GEE) to generate near real-time\nBVOC emission estimates at 10-30 m resolution, enabling fine-scale tracking of emissions in\nrapidly changing environments.”\nSPECIFIC COMMENTS\nMain Text:\n• Line 27: \"human-disturbed\"\nResponse: We thank the reviewer for identifying this typographical error. The word “distrubed”\nhas been corrected to “human-disturbed” in line 30 of the revised manuscript.\n• Lines 66 – 68: I probably wouldn't call this an \"OMI-based MEGAN model\". The data you\nare talking about here is a top-down inversion using OMI HCHO observations, with the\nMEGAN model used as the a priori emission estimate. So you should probably specify that\nthese emissions are the result of an inversion analysis, not simply a model output.\nResponse: We thank the reviewer for this important clarification. We agree that referring to the\nemissions as from an “OMI-based MEGAN model” is potentially misleading, as it does not\nproperly convey the top-down methodology involved. As noted, these emission estimates were\nderived from an inversion analysis using OMI HCHO observations, with MEGAN2.1 serving\nas the a priori emission field.\nTo address this, we have revised the main text (lines 71-73) to clearly state that the emissions\nare from the MEGAN2.1–MOHYCAN–OMI inventory, which represents MEGAN output\nconstrained by OMI-derived HCHO through inversion. This term is consistent with the\n6\nnomenclature used in the original reference (Bauwens et al., 2016)1.\nRevised Text (Line 71-73):\n“For example, hourly isoprene emission rates for August 2010 across South America showed\nthat daily average emissions closely aligned with trends derived from the inventory generated\nby MEGAN2.1 coupled with the MOdel for Hydrocarbon emissions by the CANopy\n(MOHYCAN) and constraint by Ozone Monitoring Instrument (OMI) Formaldehyde (HCHO)\nobservation (MEGAN2.1-MOHYCAN-OMI, details in Figure S2a)25, achieving a Spearman\nRank Correlation Coefficient of R=0.83 (p<0.001, N=31).\nReferences:\n1. Bauwens, M. et al. Nine years of global hydrocarbon emissions based on source inversion\nof OMI formaldehyde observations. Atmos. Chem. Phys. 16, 10133–10158 (2016).\n• Lines 79 – 83: Figure S3 shows different LAI estimates derived from Landsat or MODIS\ncompared with observations. It doesn’t show a comparison between GEE-MEGAN and\nMEGAN2.1, so I’m not sure I understand this sentence. Are you saying that the updated\nLandsat-based LAI estimates are in better agreement with observed LAI than the older MODIS\nmethod, or are you saying that the isoprene emission estimates using the Landsat-based LAI\nare more accurate? If you’re talking about isoprene emission estimates you should show those\nin a figure, since they aren’t in Figure S3.\nResponse: Thank you for this helpful comment. We acknowledge that the original wording was\nambiguous and may have suggested that Figure S3 referred to isoprene emission estimates,\nwhich it does not.\nTo clarify: Figure S3 compares LAI estimates derived from Landsat-based and MODIS-based\nmethods against ground-based observations. Our intended point was that GEE-MEGAN’s use\nof Landsat-based LAI results in significantly better agreement with observations\ncompared to traditional MODIS-derived LAI. This improved agreement is expected to lead to\n7\nmore accurate BVOC emissions, but the figure specifically addresses the LAI comparison, not\nemissions.\nAccordingly, we have revised the manuscript (lines 85-87) for clarity:\n“…, GEE-MEGAN’s Landsat-based LAI estimates reduced the mean absolute errors (MAE) by\n12.5% at 500-meter resolution and by 11.7% at a 3-km resolution relative to MODIS-based\nestimates (Figure S3), …”\n• Lines 84 – 85: Same as previous comment, are you comparing LAI estimates with\nobservations or are you comparing isoprene emission estimates between GEE-MEGAN and\nMEGAN2.1? In Table 1 you are comparing BVOC emission estimates at various sites, but in\nthis paragraph and in Figure S3 you’re talking about different LAI estimates.\nResponse: We thank the reviewer for this careful check. You are absolutely right that our\noriginal text lacked clarity in distinguishing between the comparison of LAI estimates (Figure\nS3) and the comparison of BVOC estimates (Table 1, now Table S2 in the Supplementary\nInformation).\nTo clarify: Figure S3 presents the comparison between Landsat-based and MODIS-based LAI\nestimates against ground-based observations, demonstrating the improved accuracy of the\nLandsat-derived LAI used in GEE-MEGAN; Table 1 (now Table S2 in the Supplementary\nInformation) independently compares BVOC (isoprene) flux estimates produced by GEE-\nMEGAN and MEGAN2.1 against flux observations across multiple field sites.\nTo resolve this confusion, we have revised lines 85-89 in the revised manuscript to make the\nrelationship between the improved LAI input and downstream emission modelling more\nexplicit. The revised sentence now reads:\n“…GEE-MEGAN’s Landsat-based LAI estimates reduced the mean absolute errors (MAE) by\n12.5% at 500-meter resolution and by 11.7% at a 3-km resolution relative to MODIS-based\nestimates (Figure S3), and reduced the RMSE by 9.1% at 500-meter resolution and by 3.6% at\n8\na 3-km resolution. Based on these refined LAI inputs, GEE-MEGAN further improves BVOC\nemission estimates, as evaluated in Table S2.” (Lines 85-89)\n• Lines 87 – 114: This is a very nice section. I especially like Figure 2.\nResponse: We thank the reviewer for the positive feedback. We are pleased that you found this\nsection and Figure 2 effective in demonstrating the spatial advantages of GEE-MEGAN,\nparticularly in capturing emission heterogeneity along forest edges.\n• Lines 119 – 120: Consider rephrasing this sentence. If I understand correctly, you don’t\ncompare GEE-MEGAN with the default MEGAN model in Figure 3, you just show GEE-\nMEGAN at 2 different resolutions. But this sentence implies that Figure 3 shows GEE-MEGAN\nand the default MEGAN.\nResponse: We thank the reviewer for identifying this inconsistency between the description in\nthe text and the content of Figure 3. You are absolutely right: the original version of Figure 3\nshowed only GEE-MEGAN results at two spatial resolutions, yet the sentence in lines 119-120\nimplied a comparison with the default MEGAN model. We did not show the emissions from\nthe default MEGAN model in the original Figure 3 since isoprene fluxes in all the zoom-in\npanels were zero from the default MEGAN model.\nIn response, we have revised Figure 3 (Lines 582-588). Specifically:\n1) A new column has been added to Figure 3 to display BVOC emission maps from the\ndefault MEGAN model at 500 m resolution for the same four cities;\n2) All panels are now aligned to cover identical geographic windows across GEE-MEGAN,\nMEGAN-default, and satellite imagery;\n3) The zoom-in insets have been repositioned to the upper-right of each primary panel for\nimproved readability and consistency.\nThis revised figure allows for a more direct and meaningful visual comparison between GEE-\nMEGAN and MEGAN-default:\nBeijing simulations for July 2019. (a, d, g, j) Monthly mean isoprene emissions modelled by\nGEE-MEGAN, resampled to 100 m resolution; each panel includes an upper-right inset\nshowing a magnified view of a representative urban sub-region. (b, e, h, k) Corresponding 500\n10\nm resolution isoprene fluxes estimated by the default MEGAN model, plotted over the same\nspatial extents and with identical inset areas for comparison. (c, f, i, l) Google Earth satellite\nimagery for the same regions (Maps data ©2019 Google). All panels in each city share a\ncommon geographic frame. The Paris Rive Gauche (PRG) site, marked in panel (j), is located\non the seventh floor of the Lamarck B building at Université Paris Cité (30 m above ground\nlevel) and is equipped with Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) for\nambient isoprene monitoring.\nThe revised figure now includes MEGAN-default for direct comparison.\n• Lines 120 – 121: What meteorology data did you use to drive GEE-MEGAN for the\ncomparison with the Paris measurements? In most of this section you are talking about the\nspatial variability of BVOC emissions, which makes sense given that high spatial resolution\nLUCC / LAI data seem to be one of the key advantages of GEE-MEGAN. But when you’re\ncalculating an emission time series like in Figure S4, I would expect that using coarser-\nresolution meteorology like ERA5 to drive a high-resolution model could be a significant\nsource of uncertainty. What happens to the model-observation agreement if you run GEE-\nMEGAN with higher-resolution weather inputs?\nResponse: We thank the reviewer for this thoughtful and important question. For the Paris Rive\nGauche (PRG) simulations, GEE-MEGAN was driven by ERA5-Land hourly\nmeteorological data, with a spatial resolution of approximately 11 km. While ERA5-Land\nhourly is widely used in BVOC emission modeling due to its global coverage and high temporal\nresolution, we acknowledge that it does not fully resolve intra-urban microclimates, such as\nlocalized heating, shading effects, and fine-scale radiative gradients influenced by urban\nmorphology.\nTo assess the impact of meteorological resolution on model performance, we conducted\nsupplementary simulations using the BOXMOX box model to compare modeled isoprene\nconcentrations under three emission configurations (see Table R1, now Table S4 in the\nSupplementary Information): 1) GEE-MEGAN driven by ERA5-Land hourly meteorology\n11\n(Case 1); 2) MEGAN default also driven by ERA5-Land hourly meteorology (Case 2); and 3)\nGEE-MEGAN driven by 1 km resolution WRF meteorology from ERA5 (Case 3).\nWRF simulation setup and evaluation:\nWe conducted a 27-day, 1 km WRF simulation centered on Paris from UTC July 13, 2022,\nusing a three-tier nested grid (see Figure R1, now Figure S11 in the Supplementary Information:\n9 km3 km 1 km), with 45 vertical levels and high-resolution land surface inputs. The inner\ndomain incorporated MODIS-derived Local Climate Zones (LCZ)1 and the Noah single-layer\nurban canopy model2 to represent urban morphology and surface energy fluxes (Table R2, now\nTable S13 in the Supplementary Information). Evaluation against 7 weather stations in Paris\n(Figure R1, Table R3, now Table S14 in the Supplementary Information) showed strong\nagreement (R≥0.83-0.94), and the temperature MAE satisfied the U.S. EPA criterion of < 2k3.\nComparison of emission-driven isoprene simulations:\nAs shown in Table R1, BOXMOX simulations using GEE-MEGAN emissions outperformed\nthe MEGAN default under the same ERA5-Land hourly meteorology. The evaluation was\nconducted using three metrics: Pearson correlation coefficient (R), Root Mean Square Error\n(RMSE), and Normalized Mean Square Error (NMSE).\n1) GEE-MEGAN + ERA5-Land hourly (Case 1): R = 0.45, RMSE = 0.30 ppbv, NMSE =\n0.87\n2) MEGAN default + ERA5-Land hourly (Case 2): R = 0.30, RMSE = 0.32 ppbv, NMSE =\n1.03\nInterestingly, GEE-MEGAN + WRF (Case 3) achieved a slightly lower correlation (R = 0.40),\nthough with identical RMSE (0.30) and slightly higher NMSE (0.90) when compared to GEE-\nMEGAN + ERA5-Land hourly (Case 1). This suggests that while WRF resolved finer-scale\nmeteorological parameters, it also overestimated peak isoprene concentrations, likely due to\nstronger diurnal radiation and temperature variability. Nonetheless, the degradation between\nCases 1 and 3 was substantially smaller than that between Cases 1 and 2.\nNotably, the GEE-MEGAN + ERA5-Land hourly configuration even outperformed the 1 km\nWRF-CHIMERE system used by Maison et al. (2024) 4, which reported a lower correlation (R\n12\n= 0.42) and significantly higher NMSE (5.95).\nThese results suggest that under the current conditions, enhancing spatial detail in BVOC\nemissions (i.e., vegetation structure and LUCC) has a greater impact on improving model-\nobservation agreement than increasing meteorological resolution alone. This conclusion aligns\nwith earlier work showing that land cover inputs often dominate urban BVOC emission\nuncertainty over meteorological inputs5,6.\nWe fully agree that urban-scale meteorology is critical, especially for modeling episodic\nevents or urban feedbacks. GEE-MEGAN is built on a modular architecture, supporting\naccess to multiple meteorological datasets beyond ERA5-Land hourly (e.g., MERRA-2, GLDAS) and the\ningestion of user-defined, high-resolution weather inputs (e.g., WRF\noutput), as demonstrated in Case 3. This flexibility enables future coupling with high-resolution\nforecasts or urban canopy models for refined urban simulations.\nRevised Text (Line 127-130):\n“In Paris, the model identified key hotspots, and further evaluation using the BOXMOX\nchemical box model showed that GEE-MEGAN emissions achieved better agreement with\nobserved isoprene concentrations than the default MEGAN model under identical ERA5\nmeteorological conditions9 (Figure S4, Note S1, and Table S4).”\nSupplementary Response Tables and Figures:\nTable R1 Comparison of ambient isoprene concentrations simulated by BOXMOX using different BVOC\nemission inputs and meteorological configurations, evaluated against observations at the PRG site.\nscenario Meteorological input BVOC model R RMSE NMSE\nCase 1 ERA5-Land hourly GEE-MEGAN 0.45 0.30 0.87\nCase 2 ERA5-Land hourly MEGAN default 0.30 0.32 1.03\nCase 3 ERA5+WRF GEE-MEGAN 0.40 0.30 0.90\nTable R2 Physical parameterization schemes used in the innermost domain of the WRF simulation\nmp_physics Microphysics Thompson graupel scheme (8)\nra_lw_physics Longwave radiation RRTMG scheme (4)\nra_sw_physics Shortwave radiation RRTMG scheme (4)\nsf_sfclay_physics Surface layer Monin–Obukhov similarity (1)\nsf_surface_physics Land surface model Noah LSM (2)\nbl_pbl_physics Planetary boundary layer YSU scheme (1)\ncu_physics Cumulus parameterization Kain–Fritsch (1 in domains 1–2; off in domain 3)\nsf_urban_physics Urban canopy model Noah single-layer UCM (1 in domains 1–2)\nTable R3 Validation of WRF 1 km simulation outputs against the seven Météo-France meteorological stations\nin the Paris region\nVariable Unit R RMSE MAE\nTemperature K 0.91 2.3 1.72\nsolar radiation W/m2 0.94 128.8 71.6\nRelative humidity % 0.83 13.1 9.7\nPressure hPa 0.87 1.3 1.2\nFigure R1:\nlocations. Panel A shows the full nesting configuration (9 km, 3 km, and 1 km domains), and\npanel B is the zoomed-in view of the innermost 1 km domain, with seven Météo-France stations\n(blue mark; https://meteo.data.gouv.fr/datasets/6569b4473bedf2e7abad3b72) used for\nvalidations: Montsouris (48.8217°N, 2.3378°E), Longchamp (48.8548°N, 2.2337°E), Melun\n(48.6103°N, 2.6795°E), Trappes (48.7743°N, 2.0098°E), Versailles (48.8033°N, 2.0900°E),\nOrly (48.7180°N, 2.3970°E), and Roissy (49.0152°N, 2.5343°E).\nRevised Figure S4:\nFigure S4 Observed and BOXMOX-simulated ambient isoprene concentrations (pptv) at\n15\nthe PRG (Paris Rive Gauche) site. PTR-MS observations were compared to BOXMOX\nmodel results driven by GEE-MEGAN (using ERA5-Land hourly data and WRF meteorology\nderived from ERA5) and MEGAN default emission estimates (also using ERA5-Land hourly\ndata). All simulations were conducted within a 20 km radius of the PRG site and assumed no\natmospheric transport. Panel A shows the whole time series, including a pronounced spike on\nJune 18th, attributed to an extreme heatwave event. To improve the visibility of variability across\nthe remainder of the study period, Panel B provides a zoomed-in view starting from June 21st.\nReferences:\n1. Demuzere, M. et al. A global map of local climate zones to support earth system modelling\nand urban-scale environmental science. Earth Syst. Sci. Data 14, 3835–3873 (2022).\n2. Kusaka, H., Kondo, H., Kikegawa, Y. & Kimura, F. A Simple single-layer urban canopy\nmodel for atmospheric models: Comparison with multi-layer and slab models. Boundary-Layer\nMeteorology 101, 329–358 (2001).\n3. Emery, C., Tai, E. & Yarwood, G. Enhanced Meteorological Modeling and Performance\nEvaluation for Two Texas Ozone Episodes: Final Report. Report prepared for The Texas\nNatural Resource Conservation Commission (Work Assignment No. 31984-1), ENVIRON\nInternational Corporation, Novato, CA, USA (2001).\n4. Maison, A. et al. Significant impact of urban tree biogenic emissions on air quality estimated\nby a bottom-up inventory and chemistry transport modeling. Atmos. Chem. Phys. 24, 6011–\n6046 (2024).\n5. Guenther, A. B. et al. The Model of Emissions of Gases and Aerosols from Nature version\n2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions.\nGeosci. Model Dev. 5, 1471–1492 (2012).\n6. Zhang, Y. et al. Isoprene mixing ratios measured at twenty sites in China during 2012–2014:\ncomparison with model simulation. J. Geophys. Res.: Atmos. 125, e2020JD033523 (2020).\n16\n• Lines 124 – 126: Do you know why the differences seen in Beijing were so much larger than\nin Los Angeles, London, or Paris?\nResponse: We thank the reviewer for this insightful question regarding the notably larger\ndifferences observed in BVOC emissions for Beijing compared to Los Angeles, London, and\nParis.\nTo explore this, we conducted a detailed spatial analysis of vegetated areas that were\nexclusively identified by GEE-MEGAN but absent from the default MEGAN land cover\ninputs. To quantify the structure characteristics of these vegetation distributions, we adopted\ntwo common landscape-level metrics from ecological literature: patch density (PD, the\nnumber of vegetation patches in a landscape area1,2) and mean patch area (MPA, the average\nsize of vegetation patches3), which are defined as:\nn\ni (1)\nPD = ×(10000)×(100)\nA\nMPA=mean(AREA[Patch ]) (2)\ni\nwhere n is the number of vegetation patches, and A is the total landscape area in square meters.\ni\nAREA[Patch] is the area of each patch in square kilometers.\ni\nAs shown in the newly added Table R4 (now Table S5 in the Supplementary Information),\nBeijing had the second-highest patch density (0.48 patches/100 ha), indicating moderate\nlandscape fragmentation. However, it exhibited the largest mean patch area (0.67 km²) and the\nlargest total newly identified vegetated area (2800.54 km²), nearly double that of Los Angeles.\nThis suggests that GEE-MEGAN successfully captured large, contiguous green zones in\nBeijing, such as urban forests, parks, shelterbelts, and peri-urban green corridors, which were\nabsent and underrepresented in the default MEGAN inputs. The combination of low\nfragmentation and large-scale green infrastructure resulted in a greater correction to total\nvegetated area, and thus a more pronounced difference in BVOC emission estimates.\nResponse Table R4\nTable R4 Characteristics of vegetation patches exclusively identified by GEE-MEGAN compared to\nMEGAN default across the four cities\nCity\n(/100ha) Patches (km²) (km²)\nBeijing 0.48 4189 0.67 2800.54\nLos Angeles 0.80 8360 0.17 1450.05\nLondon 0.38 3574 0.42 1513.13\nParis 0.44 3788 0.36 1366.11\nWe have updated the main text accordingly and now cite the relevant table and references to\nclarify this point:\nRevised Text (Line 136-139):\n“For cities like London, Los Angeles, Paris, and Beijing, GEE-MEGAN estimated BVOC\nemissions for July 2019 to be 1.6-25 times higher than those predicted by the MEGAN default\n(Figure 4). In Beijing, the larger discrepancy arises from fragmented vegetation being poorly\nresolved by lower-resolution land-cover inputs. GEE-MEGAN identifies an additional 2800.54\nkm² of vegetated area and a mean patch size of 0.67 km² within the same domain (Table S5),\nhighlighting the critical role of spatial vegetation detail in driving urban BVOC emissions.”\nReferences:\n1. Forman, R. T. T. & Godron, M. Landscape Ecology. (John Wiley & Sons, 1986).\n2. McGarigal, K. & Marks, B. J. FRAGSTATS: Spatial Pattern Analysis Program for\nQuantifying Landscape Structure. USDA Forest Service Pacific Northwest Research Station,\nPNW-GTR-351 (1995).\n3. Ma, J., Li, J., Wu, W. & Liu, J. Global forest fragmentation change from 2000 to 2020.\nNat Commun 14, 3752 (2023).\n• Lines 137 – 138: Are these numbers based on atmospheric box model calculations (e.g.,\nBOXMOX), or is this simply the total OH reactivity due to BVOC emissions estimated with\nMEGAN?\nreactivity (OHR) and SOA formation potential (SOAFP) presented in this section are not\nderived from atmospheric box model simulations such as BOXMOX. Instead, they were\ncalculated by applying compound-specific OH reactivity coefficients and SOA mass yield\nfactors to the BVOC emissions estimated by GEE-MEGAN and the default MEGAN model.\nThis method follows the approach used in Pfannerstill et al. (2024)1, and assumes immediate\nchemical reactivity based on emitted species without accounting for atmospheric transport or\nsecondary chemical aging processes.\nWe have clarified this in the revised manuscript and have cited the appropriate reference to\navoid confusion with modeled atmospheric concentrations.\nRevised Text (Line 149-150):\n“In West Los Angeles, while the MEGAN default model estimated zero BVOC emissions for\nmost of the 251 communities in August 2019 (Figure 5a), using compound‐specific OH\nreactivity coefficients and SOA yield factors20, GEE-MEGAN identified BVOC emissions that\nwere, on average, about 23.0 times higher (Figure 5b), along with 23.4 times greater OH\nreactivity (OHR) and 25.1 times higher SOA formation potential (SOAFP) (Figure S5).”\nReferences:\n1. Pfannerstill, E. Y. et al. Temperature-dependent emissions dominate aerosol and ozone\nformation in Los Angeles. Science 2024, 1324-1329.\n• Lines 146 – 147: I don’t quite understand this sentence. Please clarify how you might use\nGEE-MEGAN to better understand VCP terpenoid emissions, and how this is related to the\ntemperature effects described in Pfannerstill et al (2024) (your reference #20).\nResponse: We thank the reviewer for raising this important question. The total terpenoid\nemissions observed in urban areas are typically a combination of BVOC emissions from\nvegetation and anthropogenic emissions from volatile chemical products (VCPs), such as\n19\ncleaning agents, personal care products, and solvents.\nSince VCP emissions are often estimated as the residual after subtracting biogenic emissions\nfrom the total observed values, having accurate biogenic estimates is crucial for correctly\nattributing and quantifying the VCP contribution. GEE-MEGAN improves the spatial and\ntemporal precision of BVOC estimates, particularly their temperature response, which in turn\nreduces attribution uncertainty when interpreting field observations.\nThis also ties into the findings of Pfannerstill et al. (2024)1, who showed that both BVOC and\nVCP emissions in Los Angeles exhibit strong temperature dependence. If the biogenic\ntemperature response is misrepresented, it becomes difficult to distinguish how much of the\nobserved temperature-driven increase in terpenoids is from vegetation versus VCPs. GEE-\nMEGAN helps isolate the biogenic temperature signal, thereby improving the attribution of\nremaining variability to VCPs.\nRevised Text (Line 160-162):\n“This suggests that more accurate BVOC emission estimates also enable better quantification\nof the contribution of temperature-sensitive VCPs42 to total terpenoid emissions, and help\ndisentangle their temperature-driven variability20.”\nReferences:\n1. Pfannerstill, E. Y. et al. Temperature-dependent emissions dominate aerosol and ozone\nformation in Los Angeles. Science 2024, 1324-1329.\n• Lines 182 – 184: Please clarify what you mean by \"a more precise understanding of urban\nterpenoid emission sources and their roles in air pollution...\". I think this is related to your point\non line 146-147, which I did not understand.\nResponse: We thank the reviewer for raising this important point and drawing the connection\nto our earlier discussion on lines 146-147. The phrase \"a more precise understanding of urban\nterpenoid emission sources\" refers to GEE-MEGAN’s capability to generate high-resolution\n20\nbiogenic emission maps, which are essential for distinguishing biogenic sources from\nanthropogenic VCPs in urban areas. This source attribution is particularly important because\nboth BVOCs and VCPs contribute to total urban terpenoid emissions, and their relative\ncontributions are temperature-sensitive. Moreover, the chemical reactivity and secondary\norganic aerosol (SOA) yields of terpenoids can vary significantly between biogenic and\nanthropogenic sources depending on their molecular composition and atmospheric chemistry1,2.\nBy improving the accuracy of biogenic emission estimates, GEE-MEGAN enables better\ndifferentiation and quantification of the VCP contribution. This, in turn, enhances our ability\nto model ozone and SOA formation pathways in complex urban environments.\nRevised Text (198-200):\n“As illustrated by the examples from Los Angeles discussed above, GEE-MEGAN provides a\nmore precise understanding of the contributions of biogenic emissions to total urban terpenoid\nemissions, which is essential for evaluating their roles in air pollution, including ozone and\nSOA formation19,20,42.”\nReferences:\n1. Seltzer, K. M. et al. Reactive organic carbon emissions from volatile chemical products.\nAtmos. Chem. Phys. 21, 5079–5100 (2021).\n2. Coggon, M. M. et al. Volatile chemical product emissions enhance ozone and modulate\nurban chemistry. Proc. Natl. Acad. Sci. U.S.A. 118, e2026653118 (2021).\n• Lines 193 – 196: I think you need to emphasize this a bit more. You don’t really talk about\nthe resolution of weather / climate input data in the main text, but for high-resolution urban\nmodelling this is an important piece of the puzzle (e.g., accurately modelling variations in\ntemperature in urban areas due to landcover and street layouts). Especially if you want to\nuse GEE-MEGAN investigate things like urban greening initiatives or other local-scale climate\nmitigation strategies, is it reasonable to use a coarser data product like ERA-5?\nmeteorological resolution is a key factor in urban-scale modeling, especially when\ninvestigating the impacts of local interventions, such as urban greening, afforestation, or land-\ncover changes.\nOur current implementation of GEE-MEGAN relies on ERA5-Land hourly (~11 km resolution)\nfor meteorological inputs due to its global availability, high temporal resolution and consistency.\nHowever, we acknowledge that ERA5-Land hourly may not resolve fine-scale temperature\ngradients, urban heat island effects, or microclimatic variability due to complex urban\nmorphology.\nTo assess the implications of meteorological resolution, we performed a supplementary\nevaluation using BOXMOX model in Paris (see response to Lines 120-121). Results show\nthat enhancing vegetation detail (via high-resolution LAI and LUCC) had a greater impact\non improving model-observation agreement than refining meteorological inputs alone (see\nFigure R1 and Table R1). For instance, GEE-MEGAN driven by ERA5-Land hourly\noutperformed both the MEGAN default and an even more complex 1 km WRF-CHIMERE\nmodel or 1km WRF-driven simulation, demonstrating spatial detail in emissions is currently\nthe dominant source of uncertainty.\nWe fully agree that high-resolution meteorological inputs will become increasingly critical as\nGEE-MEGAN is extended to support fine-scale policy-relevant analysis, community-level air\nquality planning, and dynamic urban climate modeling.\nERA5-Land hourly currently offers the best compromise for global consistency and data quality.\nHowever, WRF simulations, including the 1 km WRF outputs in this study, can complement\nERA5 when local meteorological detail is essential. These simulations, when valuable, still\nexhibit biases relative to in situ observations and must be interpreted accordingly.\nImportantly, GEE-MEGAN is designed with a modular architecture that supports the\ningestion of alternative meteorological reanalysis datasets (e.g., RTMA, MERRA-2) and the\nintegration of user-supplied high-resolution meteorological field (e.g., WRF,\nweather forecasting models). This flexibility ensures that GEE-MEGAN can evolve alongside\nfuture advances in urban-scale meteorological modeling and be readily adapted to specific use\ncases.\nRevised Text (Line 209-215):\n“Although ERA5-Land hourly meteorology (~11 km) was used in this study to ensure global\napplicability and consistency, our results indicate that improving the spatial representation of\nvegetation yields substantial gains in BVOC emission accuracy. Nevertheless, incorporating\nhigh-resolution meteorological reanalysis products or AI-driven weather prediction models65,66\ncould further enhance GEE-MEGAN’s capacity to simulate urban-scale processes, offering\ndeeper insights into BVOC-related dynamics in air quality and climate change.”\n• Lines 210 – 211: Can GEE-MEGAN be easily used with other weather data (e.g., other\nreanalyses like MERRA2, model output like WRF, or climate models like CESM)? Especially\nfor urban modelling, it would be useful to use GEE-MEGAN with alternative weather inputs.\nResponse: We thank the reviewer for this important question. Yes, GEE-MEGAN is designed\nto be flexible and compatible with a wide range of meteorological inputs beyond ERA5-\nLand hourly.\nThe GEE platform provides direct access to several global meteorological reanalysis products,\nincluding MERRA-2, CFSv2, and GLDAS-2.1, as well as regional high-resolution datasets\nsuch as RTMA for the United States (see Table R5 below, now Table S7 in the Supplementary\nInformation). In addition, users can upload custom meteorological inputs, including high-\nresolution weather model output (e.g., from WRF) or projections from Earth system models\nsuch as CESM.\nThis flexibility allows GEE-MEGAN to be adapted for urban-scale studies, climate scenario\nanalysis, or region-specific modeling based on the user’s choice of meteorological driver.\nRevised Text (233-237):\n23\n“In addition to ERA5, GEE-MEGAN can incorporate other meteorological datasets such as\nModern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the\nClimate Forecast System Version 2 (CFSv2), or the Global Land Data Assimilation System\n(GLDAS-2.1), and also support user-defined inputs such as WRF model output or CESM\nprojections (Table S7).”\nResponse Table R5：\nTable R5: Meteorological data in GEE that can be used for GEE-MEGAN\nNominal Temporal Temporal Spatial\nDataset Source\nresolution granularity coverage coverage\nERA5-Land hourly 11132 m Hourly 1950-Now Global CDSa\nCFSV2 (NCEP Climate Forecast\n22264 m 6 Hours 1979-Now Global UCAb\nSystem Version 2)\nGLDAS-2.1 (Global Land Data\n27830 m 3Hours 2000-Now Global NASAc\nAssimilation System)\nRTMA (Real-Time Mesoscale\n2500 m Hourly 2011-Now American NCEPd\nAnalysis)\nMERRA-2 (M2T1NXSLV\nSingle-Level Diagnostics 69375 m Hourly 1980-Now Global GMAOe\nV5.12.4)\naCDS (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land);\nbUCAR (https://rda.ucar.edu/datasets/d094000/);\ncNASA (https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary);\ndNCEP (https://www.nco.ncep.noaa.gov/pmb/products/rtma/);\neGMAO (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/)\n• Lines 223 – 224: When you say “align with expectations”, do you mean that they agree with\nthe default MEGAN2.1 model?\nResponse: We appreciate the reviewer’s comment and are happy to provide clarification. Yes,\nthe phrase “align with expectations” specifically means that the emission activity factors and\n24\noverall outputs from GEE-MEGAN agree with those of the original MEGAN2.1 model\nwhen both are driven by the same input datasets.\nAlthough GEE-MEGAN includes architectural modifications and computational optimizations\nfor cloud-based scalability, these changes do not alter the core computational logic of\nMEGAN2.1. This equivalence has been verified through a series of comparative simulations,\nas detailed in Figure S8 and Supplementary Note S4, which demonstrate near-identical\noutputs across 16,576 grid cells.\nTo clarify this point in the manuscript, we have revised the relevant sentence.\nRevised Text (Line 246-248):\n“Despite improving GEE-MEGAN computational efficiency and enabling near-real-time\nsimulations, GEE-MEGAN produces emission activity factors and outputs consistent with\nMEGAN2.1 when using identical inputs (see Figure S8 and Note S4).”\n• Lines 279 – 280: It appropriate to use ERA-5 meteorology when driving a 30 – 100 m\nresolution simulation?\nResponse: We thank the reviewer for raising this important point regarding the potential\nmismatch between spatial resolution of ERA5-Land hourly meteorology (~11 km) and the 30–\n100 m resolution used for BVOC emission simulations in GEE-MEGAN.\nWe fully agree that ERA5-Land hourly cannot capture fine-scale urban meteorological\nfeatures such as localized temperature variations, shading, or street-level radiative effects.\nHowever, for the purpose of a globally scalable and operational near-real-time framework,\nERA5-Land hourly currently represents the most feasible and consistent choice due to its\nhourly temporal resolution, global coverage, and long historical baseline (Table R5, now Table\nS7 in the Supplementary Information).\nIn practice, when finer meteorological resolution is required, mesoscale models like WRF are\n25\noften used to dynamically downscale ERA5 inputs for specific regions. These simulations are\nstill based on the same underlying emission and meteorological conditions. While valuable,\nsuch approaches come with significant computational cost and are not yet operational at global\nscale.\nIn this study, we chose to focus on improving land surface heterogeneity, including high-\nresolution LUCC and LAI inputs, which are the primary spatial driver of BVOC emissions.\nOur results (e.g., in Paris) show that enhancements in vegetation representation produce\nsubstantial improvements in emission estimates, even when paired with coarse meteorology.\nWe have clarified this trade-off in the revised manuscript.\nRevised Text (Line 304-307):\n“To balance near real-time applicability across spatial scales, GEE-MEGAN uses consistent\nmeteorological inputs from ERA5-Land hourly while tailoring land-use and vegetation inputs\nacross 100 m, 500 m, and 5 km scales, with varying data sources for LUCC and LAI to reflect\nspatial heterogeneity in emissions.”\n• Lines 316 – 318: I was unable to access the GitHub repository containing the GEE-MEGAN\ncode. Please double-check this link.\nResponse: We thank the reviewer for pointing this out and apologize for the inconvenience.\nThe GitHub repository link was temporarily inactive due to privacy settings at the time of\nreview. We have since resolved the issue and ensured that the repository is now publicly\naccessible.\nThe GEE-MEGAN framework, along with the full source code, figure generation scripts, and\ninstructions for data access and model replication, is now available at:\nhttps://doi.org/10.5281/zenodo.15714886.\n• Figure 1: Spelling mistake in caption (\"MGEAN2.1\")\nResponse: Thank you very much for your careful check. We confirm that the mistake in the\nFigure 1 caption was a misspelling. It has been corrected to “MEGAN2.1” in the revised\nmanuscript in the revised manuscript (Line 549):\n“…Derivation of leaf area index ( ) and environmental activity factor (A) in MEGAN2.1…”\nLAI\nWe have also carefully reviewed the manuscript and figure captions to ensure that no similar\ntypographical errors remain.\n• Figure 3: If possible, can you add another Google Earth imagery column for the zoomed out\npanels (a, d, g, and j)? It is hard to interpret the emission maps in those panels without a\nreference image, except for Los Angeles where the coastline and mountains are somewhat\nvisible in panel (a).\nResponse: We appreciate this helpful and thoughtful suggestion. We fully agree that the\nabsence of corresponding geographic reference imagery in the zoom-out emission panels (a, d,\ng, and j) may limit interpretability, particularly for inland cities without obvious natural\nlandmarks.\nIn response, we have revised Figure 3 to include an additional column of Google Earth imagery\nthat matches the full geographic extent of panels (a, d, g, and j). This change improves the\nvisual alignment between modeled emissions and underlying land cover, and provides clearer\nspatial context for interpreting BVOC emission patterns in each city.\nWe thank the reviewer for helping us improve the clarity and accessibility of the figure.\nRevised caption for Figure 3 (Line 572-580):\n“Figure 3 | Spatial distribution of isoprene emissions in Paris, Los Angeles, London, and Beijing\nsimulations for July 2019. (a, d, g, j) Monthly mean isoprene emissions modelled by GEE-MEGAN,\n27\nresampled to 100 m resolution; each panel includes an upper-right inset showing a magnified view\nof a representative urban sub-region. (b, e, h, k) Corresponding 500 m resolution isoprene fluxes\nestimated by the default MEGAN model, plotted over the same spatial extents and with identical inset\nareas for comparison. (c, f, i, l) Google Earth satellite imagery for the same regions (Maps data ©2019\nGoogle). All panels in each city share a common geographic frame. The Paris Rive Gauche (PRG) site,\nmarked in panel (j), is located on the seventh floor of the Lamarck B building at Université Paris Cité\n(30 m above ground level) and is equipped with Proton-Transfer-Reaction Mass Spectrometry (PTR-MS)\nfor ambient isoprene monitoring.”\n• Figure 4: I'm not sure I understand the figure caption. \" The bar depict the emissions of\nisoprene, monoterpenes, and sesquiterpenes for each city, focus on regions with nighttime light\npercentages above 80% (nighttime light data from the National Polar-orbiting Partnership,\nVisible Infrared Imaging Radiometer Suite).\" Does this mean the urban boundaries for these\ncities are defined based on a nighttime light threshold of 80%, or does it mean something else?\nResponse: Thank you for pointing out the ambiguity in the caption for Figure 4. We confirm\nthat the phrase “nighttime light percentages above 80%” refers to the threshold used to define\nurban boundaries in this analysis. Specifically, we used VIIRS (Visible Infrared Imaging\nRadiometer Suite) nighttime light data and applied a threshold of 80% of the maximum\nradiance value to delineate areas with intense nighttime activity, serving as a proxy for urban\ncores.\nThe method provides a more consistent and behavior-based representation of urban extent\ncompared to conventional administrative boundaries or impervious surface area definitions,\nwhich can vary widely across global cities and may misrepresent functional urban space1-4.\nTo clarify this, we have revised the figure caption. Additionally, to ensure a fair comparison,\nemission totals (isoprene, monoterpenes, and sesquiterpenes) from both MEGAN default and\nGEE-MEGAN were computed over identical urban extents defined by the nighttime light\nthreshold.\n“Figure 4 | BVOC compositions in Paris, Los Angeles, London, and Beijing as modelled by\nMEGAN default and GEE-MEGAN. The base map is derived from Google Earth remote\nsatellite imagery (Maps data ©2025 Google). The bars depict the emissions of isoprene,\nmonoterpenes, and sesquiterpenes for each city, calculated over areas with nighttime light\nintensity ≥80% of the maximum value (based on VIIRS data from the National Polar-orbiting\nPartnership). This threshold was used to define urban boundaries consistently across cities. For\neach city, the same spatial extent was used for both models to ensure direct comparison.”\nReferences:\n1. Chakraborty, T. et al. Large disagreements in estimates of urban land across scales and\ntheir implications. Nat. Commun. 15, 9165 (2024).\n2. Chen, B. et al. Contrasting inequality in human exposure to greenspace between cities of\nGlobal North and Global South. Nat. Commun. 13, 4636 (2022).\n3. Zhao, M. et al. A global dataset of annual urban extents (1992–2020) from harmonized\nnighttime lights. Earth Syst. Sci. Data 14, 517–534 (2022).\n4. Zheng, Q., Seto, K. C., Zhou, Y., You, S. & Weng, Q. Nighttime light remote sensing for\nurban applications: Progress, challenges, and prospects. ISPRS J. Photogramm. Remote Sens.\n202, 125–141 (2023).\nSupplementary Information:\n• Note S2: Spelling error (\"framwork\")\nResponse: Thank you for identifying the spelling error in Note S2. We have corrected\n“framwork” to “framework” in the sentence:\nRevised Text Note S2 in SI (Supplementary Information): “The Data Source Module is a\ncritical component of the GEE-MEGAN framework, responsible for systematically extracting\n29\nand organizing the necessary datasets from the GEE data repository.”\n• Note S4: Can you explain the steps described in Figure S7 in more detail here? For readers\nwho are not familiar with MEGAN or canopy models, this section may not provide enough\ninformation for them to understand that figure.\nResponse: Thank you for this helpful suggestion. We agree that the original Note S4 may not\nhave provided sufficient detail for readers who are less familiar with MEGAN or canopy energy\nbalance models. To improve clarity and accessibility, we have expanded Note S4 in the revised\nSupplementary Information to include a step-by-step explanation of the computational\nworkflow illustrated in Figure S7.\nThe updated Note S4 in SI now reads:\n“Step 1: The model begins by extracting solar radiation from input meteorological data. This\nprovides the primary energy input for subsequent canopy-level calculations.\nStep 2: The canopy is vertically divided into five layers, and a Gaussian integration scheme is\nused to assign weights to each layer based on the typical vertical distribution of radiation within\nthe canopy.\nStep 3: Using these layer weights and the total incident radiation, layer-specific radiation\nfluxes are initialized. Other meteorological parameters, such as wind speed, humidity, and\npressure, are similarly allocated across layer.\nStep 4: The canopy energy balance is solved through an iterative procedure. At each layer,\nenergy fluxes are computed using both meteorological inputs (e.g., radiation, air temperature,\nwind speed, humidity, and pressure) and leaf-level physiological parameters (e.g., leaf area\nindex, leaf length, emissivity, and absorptivity). Radiative transfer is estimated using the\nStefan–Boltzmann law and principles of black-body radiation. This yields net radiative fluxes,\nfrom which both sensible and latent heat fluxes are derived. These heat fluxes are used to update\nthe air and leaf temperature fields iteratively. The process continues until thermal equilibrium\n30\nis achieved or the maximum iteration count (10 steps, as in MEGAN2.1) is reached. For\ncomputational efficiency, the entire process is implemented as 3D matrix operations in GEE,\nenabling array-based optimization and pixel-level parallelization.\nStep 5: After convergence of iterative computation, temperature and light response functions\nare applied at each canopy layer to compute the activity factors γ (temperature) and γ (light),\nt p\nwhich are used in the final BVOC emission rate calculations.”\nWe believe this expanded explanation will help readers, including those less familiar with\ncanopy modeling, better understand the logic and structure of the energy balance process in\nFigure S7 and GEE-MEGAN overall.\n• Note S6: Several spelling errors (\"basedon\", \"distinquished\", \"regios\"); Repeated word (\"Retain the the MODIS MCD12Q1...\")\nResponse: Thank you for carefully identifying these typographical and repetition errors in Note\nS6. We have made the following corrections in the revised SI:\n1. Corrected “basedon” to “based on”\n2. Corrected “distinquished” to “distinguished”\n3. Corrected “regios” to “regions”\n4. Removed the repeated word “the the” from “Retain the the MODIS…”\n5. Removed a similar duplication of “the the” in Note S1.\nRevised sentences in Note S4 in SI:\n1. “The Köppen classification for arid regions is primarily based on the relationship between\nprecipitation and annual average temperature.”\n2. “Regions are distinguished as either Bh (hot) or Bk (cold), depending on whether the annual\ntemperature exceeds or falls below 18°C.”\n31\n3. “Cold Zones (Cold): for cold regions, we used the Köppen criteria for non-arid climates\nwhere Thot>10°C & Tcold≤0°C.”\n4. “Integrity: Retain the MODIS MCD12Q1 classification, as it is reliable for capturing land\nuse data. 2)”\n5.“BOX Modeling as an extensions of the Kinetic PreProcessor (KPP)”\n• Note S7: Missing word (\"...are employed by GEE-MEGAN...\")\nResponse: Thank you for pointing out the missing word “by” in Note S7. We agree that the\noriginal sentence was incomplete and lacked grammatical clarity.\nRevised Text in Note S7 in SI:\n“Dynamic EFMAPs (DE) are employed by GEE-MEGAN to adjust emission factors in real-\ntime, accounting for temporal and spatial changes in environmental conditions.”\n• Figure S1: Some of the text bubbles are unclear, especially in panel D. For example,\n“computational chemical” is not meaningful. I understand from the caption you intend to export\nthese emissions for use in atmospheric models, so this should be more obvious in the figure.\nResponse: We thank the reviewer for this valuable feedback. We agree that the original labeling\nin panel D of Figure S1, particularly the term “computational chemical”, was unclear and could\nmislead readers unfamiliar with the output workflow.\nIn response, we have revised panel D to replace the ambiguous term “computational chemical”\nwith clearer, scientifically accurate labels. Specifically, the updated figure now explicitly\nillustrates the two main output pathways: (1) real-time online visualization within the GEE\ninterface; and (2) export of emission results in NetCDF format, which supports downstream\nuse in chemical transport models.\nWe also reviewed and revised all terminology in the figure caption and related text in the\n32\nmanuscript. These changes enhance the figure’s clarity and ensure that its workflow aligns with\nthe model’s intended use.\nThe updated Figure S1 has been included in the revised Supplementary Information.\nRevised Figure S1:\nFigure S1 GEE-MEGAN calculation framework. (A) Multi-source data retrieval and\ncomputation module, which aggregates and processes input data from various sources. (B)\nPreprocessing module, responsible for ensuring data consistency through spatial re-projecting,\ntemporal filtering, and resampling. (C) BVOC calculating module, where emission factors and\nenvironmental variables are integrated to estimate BVOC emissions. (D) Output and post-\nprocessing module, which visualizes and exports the results for further analysis and integration\nwith atmospheric models.\n• Figure S2: Panels (c) – (h) need labels. Also, because the colour bar scale is different on each\npanel, it is hard to directly compare the different emission estimates other than the spatial\ndistribution. Consider remaking these panels using the same colour bar so the differences are\nmore obvious.\nspecific labels and the inconsistent color bar scales in panels (c)–(h) made it difficult to compare\nthe emission estimates across different datasets, especially in terms of absolute magnitude.\nIn response, we have revised Figure S2 as follows:\n1. Panel labels have been added to clearly indicate the corresponding dataset in each subplot;\n2. A uniform color bar scale has been applied across all spatial panels (c-h), allowing for direct\nvisual comparison of emission magnitudes and spatial patterns across datasets.\nThese changes significantly improve the clarity and interpretability of the figure and support a\nmore robust comparison of emission differences among models and retrievals.\nRevised Figure S2:\nduring August 2010 simulated by GEE-MEGAN, MEGAN default and other MEGAN-\nbased retrievals. The unit of emission is g m⁻² month⁻¹. (a) Comparison of emission curves\nfor GEE-MEGAN and OMI-based retrievals in the simulation area. The red line represents the\n24-hour average total emissions of GEE-MEGAN, while blue line shows the 24-hour average\ntotal emissions constrained by OMI retrievals. (b) The simulation region, with the red area\nindicating the study region. (c-h) Monthly total emissions from (c) GEE-MEGAN, (d) MEGAN\ndefault, (e) MEGAN2.1-MOHYCAN-OMI, (f) MEGAN2.1-IMAGESv2-GOME2, (g)\nMEGAN2.1-MOHYCAN-GFWMOD, and (h) MEGAN2.1-MOHYCAN-CLM. All panels use\nthe same color bar scale for comparability.\n35\n• Figure S4: What is the large spike in simulated and observed isoprene on July 18th? It’s hard\nto see what’s happening in the rest of the time series because the spike at the beginning stretches\nthe y-axis scale. Consider remaking the figure without those data or explain what the large\nisoprene spike is in the caption.\nResponse: Thank you for this thoughtful observation. The pronounced spike in both simulated\nand observed isoprene concentrations on June 18th in Figure S4 corresponds to an extreme\nheatwave event in the Paris region. Elevated temperatures are known to strongly enhance\nisoprene emissions due to the compound’s well-documented temperature dependence, and this\nsignal was captured by both the GEE-MEGAN model and PTR-MS observations.\nTo preserve the integrity of the full observational period, we have retained the complete time\nseries in the main panel. However, in response to the reviewer’s concern about visualization,\nwe have added a new subpanel showing a zoomed-in view of the time series excluding the June\n18th, beginning from June 21st. This addition improves readability and better highlights the\ntemporal evolution of isoprene concentrations outside of the extreme event\nRevised Figure S4:\nFigure S4 Observed and BOXMOX-simulated ambient isoprene concentrations (pptv) at\nthe PRG (Paris Rive Gauche) site. PTR-MS observations were compared to BOXMOX\n36\nmodel results driven by GEE-MEGAN (using ERA5-Land hourly data and WRF meteorology\nderived from ERA5) and MEGAN default emission estimates (also using ERA5-Land hourly\ndata). All simulations were conducted within a 20 km radius of the PRG site and assumed no\natmospheric transport. Panel A shows the full time series, including a pronounced spike on June\n18th, attributed to an extreme heatwave event. To improve the visibility of variability across the\nremainder of the study period, Panel B provides a zoomed-in view starting from June 21st.\n• Figure S8: Which gamma factor are we looking at here? In Figure S7 and Note S4 we are\nlooking at gamma_T and gamma_P (temperature and sunlight activity factor). Is that what this\nfigure shows, or are we also including the effects of the canopy environment (e.g., gamma-LAI?)\nResponse: Thank you for this excellent question. To clarify, Figure S8 presents the composite\nemission activity factor γ used in the MEGAN framework. which reflects the cumulative effect\nof several modifying environmental and canopy-related factors. This γ is a dimensionless\nmultiplier that adjusts the standard emission rate (ε) under non-standard environmental\nconditions, as defined in Equation (2) of our Supplementary Information.\nSpecifically, the γ factor shown in Figure S8 includes the combined effects of γₚ (light-\ndependent activity factor), γₜ (temperature-dependent activity factor), γₐ (leaf age factor), γ\nSM\n(soil moisture factor), C (canopy environment coefficient) and LAI (leaf area index). Thus,\nCE\nthe spatial patterns shown in Figure S8 reflect the full γₖ,ᵢ,ⱼ value as applied in the MEGAN\nemission equation:\nE = ε ×γ (1)\ni,j i,j k,i,j\nwhere:\nγ = C ×LAI×γ ×γ ×γ ×γ (2)\nk,i,j CE P,k T,k A,k SM,k\nIn contrast, Figure S7 and Note S4 focus specifically on the canopy energy balance calculations,\nwhich provide the basis for calculating the light (γ ) and temperature (γ) activity factors only.\np t\nTo avoid confusion, we have added a clarifying sentence in the caption of Figure S8 and\n37\nreferenced Equation (2) explicitly:\n“Figure S8 | Difference in activity factor coefficients (γ) between MEGAN2.1 and GEE-\nMEGAN. The γ factor shown here represents the composite emission activity factor, which\ncombines the effects of light (γₚ), temperature (γₜ), leaf age (γₐ), soil moisture (γ ), canopy\nSM\nenvironment coefficient (C ), and LAI. This is distinct from Figure S7, which shows only the\nCE\nγₚ and γₜ factors derived from canopy energy balance calculations.”\n• Table S3: For the 500m resolution MCD12Q1 dataset, all the cities have zero vegetation\ncoverage. Does this mean that if you run GEE-MEGAN at 500m resolution, there will be no\nurban BVOC emissions? In the main text in the “Multiscale Simulation Framework” section,\nyou say that you can run GEE-MEGAN with coarser LUCC and LAI inputs. But this table\nseems to imply that if you use the MCD12Q1 inputs, you will get zero urban BVOC emissions.\nIf this is true, you should clearly explain this somewhere in the text, since accurate urban BVOC\nemissions seem to be one of the main advantages of GEE-MEGAN.\nResponse: Thank you for this important point regarding Table S3 (Now Table S6). You are\ncorrect: when GEE-MEGAN is run using the 500 m MODIS MCD12Q1 dataset, urban areas\nare often classified as impervious surfaces or \"built-up land\", resulting in near-zero vegetation\ncoverage in these grids. Consequently, urban BVOC emissions are negligible when using this\ncoarser-resolution LUCC input alone.\nThis limitation does not contradict the framework described in the “Multiscale Simulation\nFramework” section. Rather, it highlights a key point: the choice of LUCC and LAI input\nresolution significantly affects the ability to resolve vegetation within urban environments.\nFor global-scale modeling or studies focused on natural ecosystems, coarser datasets like\nMCD12Q1 (500 m) or MCD12C1 (5 km) are adequate and computationally efficient.\nHowever, accurate estimation of urban BVOC emissions requires higher-resolution inputs,\nsuch as those derived from Landsat (30 m) or Sentinel-2 (10–20 m), which can resolve intra-\nurban green spaces (e.g., street trees, parks, and shrubs) that are otherwise missed in coarse\nclassifications.\nThis distinction is one of the core strengths of the GEE-MEGAN framework—it allows flexible\n38\nintegration of multi-scale inputs, and for urban applications, it enables users to override the\ncoarse classifications with high-resolution satellite-derived vegetation data.\nTo address this point, we have revised the manuscript text to explicitly mention this limitation\nand clarify the scale-dependent capabilities of GEE-MEGAN.\nRevised Text (Line 312–315):\n“At coarser spatial resolutions (e.g., 500 m), urban green space may be underrepresented or\nmisclassified as impervious surfaces, resulting in negligible urban BVOC emissions. Accurate\nestimation of emissions in urban areas therefore requires high-resolution LUCC and LAI inputs,\nwhich GEE-MEGAN supports.”\nReviewer #2 (Remarks to the Author):\nZhang et al. developed an innovative modeling framework, GEE-MEGAN, by integrating\nmultiple remote sensing products with machine learning–based tuning. Compared to the default\nMEGAN scheme, GEE-MEGAN offers significantly higher spatial resolution, enabling it to\ncapture finer-scale features of BVOC emissions, particularly in disturbed forests and urban\nareas. This framework represents a valuable advancement, and such improvements in BVOC\nemissions are critical for better modeling of air pollution, especially in regions where air quality\nis highly sensitive to BVOC emissions.\nHowever, I have several concerns that I hope the authors can address to strengthen the\nmanuscript:\nMajor Comments\n1. Evaluation of GEE-MEGAN is relatively weak\nThe comparison between GEE-MEGAN and the default MEGAN scheme appears more as a\nconsistency or sanity check rather than a rigorous evaluation. Since GEE-MEGAN builds upon\nthe default scheme but uses different input sources and a finer spatial resolution, agreement\nbetween the two is expected. For example, the statement in lines 65–66, that GEE-\nMEGAN “accurately captures the daily and monthly trends” compared to the default MEGAN, is not\n39\nparticularly meaningful. If the two products did not agree at regional scales, that would indicate\na problem with GEE-MEGAN.\nA more meaningful evaluation involves comparison with observed isoprene fluxes (as\npresented in Table 1), which is far more informative than comparison against MEGAN 2.1.\nThat said, the performance of GEE-MEGAN in Table 1 is ambiguous. In some cases, such as\nthe BR-Sa1 and Qianyanzhou sites, MEGAN 2.1 shows lower RMSE and MAE than GEE-\nMEGAN. This contradicts the claim in lines 84–85 that GEE-MEGAN outperforms MEGAN\n2.1.\nResponse: We thank the reviewer for this important comment and fully agree that the\ncomparison with MEGAN2.1 alone, particularly at regional scale, functions more as a\nstructural consistency check than a rigorous evaluation. As the reviewer noted, GEE-\nMEGAN shares its core formulation with MEGAN2.1, so broad agreement between the two is\nexpected and only confirms correct implementation under varied input resolutions.\nTo address the need for more meaningful evaluation, we have taken the following actions:\n1) Clarified the Purpose of GEE-MEGAN vs. MEGAN2.1 Comparisons\nWe now emphasize in both the main text and Supplementary Information that agreement with\nMEGAN2.1 at large scales (e.g., Figure S3) is used solely to verify internal consistency and\nnumerical stability under different input configurations. The revised manuscript avoids over-\ninterpreting this as a validation of performance.\n2) Improved Flux Evaluation with Site-Specific Emission Factors\nAt the BR-Sa1 site, we re-evaluated GEE-MEGAN using a site-specific emission factor (~2.0\nmg m⁻² h⁻¹) based on published EC flux studies1 rather than the MEGAN default (7.0 mg m⁻²\nh⁻¹). This significantly improved model performance: RMSE decreased from 1.93 to 0.44 mg\nm⁻² h⁻¹; MAE decreased from 1.03 to 0.22 mg m⁻² h⁻¹; correlation remained high (R = 0.90),\nexceeding that of MEGAN2.1 (R = 0.83). These results are now included in Table R6 (see\nbelow) and summarized in the main text.\n3) New Evaluation at an Urban Flux Site (Beijing)\nTo better demonstrate GEE-MEGAN's advantage in complex urban environments, we added a\n40\nnew validation at an urban EC flux site in Beijing2. GEE-MEGAN clearly outperformed\nMEGAN2.1 in this setting: R increased from 0.74 to 0.76; RMSE decreased from 1.47 to 1.01;\nMAE decreased from 0.75 to 0.53. This new result is included in Table R7.\n4) Clarification of Uncertainty at the Qianyanzhou Site\nWe appreciate the reviewer’s careful evaluation and acknowledge the concern regarding the\nQianyanzhou site, where MEGAN2.1 appears to show lower RMSE/MAE than GEE-MEGAN.\nHowever, it is important to note that flux measurements at this site were conducted using the\nRelaxed Eddy Accumulation (REA) method, which is known to be more uncertain than eddy\ncovariance (EC), especially under high light conditions when vertical wind and scalar\nfluctuations are rapid3,4.\nWhile GEE-MEGAN yielded a slightly higher RMSE at this site, it notably corrected the\ndirectionality of the correlation, improving a negative R (MEGAN2.1) to a positive R = 0.56,\nsuggesting that GEE-MEGAN better captured the overall seasonal trend, despite the increased\nvariability.\nUpon further inspection of the original REA data at the Qianyanzhou site, we also observed a\nnumber of physically implausible isoprene flux values, including negative isoprene emissions\naround noon, which are not consistent with expected biogenic behavior. These likely result from\nthe limitations of the REA technique used in the original study.\nGiven the measurement uncertainties and the presence of potentially unreliable data, after\nextensive discussion, we have chosen to remove the Qianyanzhou comparison from Table 1\nand will clarify this in the revised manuscript text. To improve the clarity and flow of the main\nmanuscript, we have decided to relocate the full multi‐site comparison form Table 1 to the\nTable S2 in Supplementary Information.\nIn summary, these expanded evaluations demonstrate that GEE-MEGAN retains consistency\nwith MEGAN2.1 at large scales (sanity check); that it improves predictive accuracy, especially\nwhen site-specific EFs are used and in urban or edge environments; and that EC-based sites\n(e.g., BR-Sa1, Hyytiälä, Beijing) offer stronger support for the model's robustness than REA-\nbased sites like Qianyanzhou. We have updated Table 1 (Now Table S2 in the Supplementary\nResponse Table R6 and R7:\nTable R6 Comparison of GEE-MEGAN and MEGAN 2.1 in BR-Sa1 under different Emission factors with\nfluxes of isoprene\nEmission factors Resolution\nSites Model N R P R2 RMSE MAE\n(mg m-2 h-1) (m)\nGEE-MEGAN 7.0 1500 305 0.90 <0.005 0.81 1.68 0.88\nBR-Sa1,\n(default) 500 305 0.90 <0.005 0.81 1.93 1.03\nBrazil\nMEGAN2.1 2.0 N/A 305 0.83 <0.005 0.70 0.53 0.28\nGEE-MEGAN 2.0 1500 305 0.90 <0.005 0.81 0.48 0.23\nBR-Sa1,\n500 305 0.90 <0.005 0.81 0.44 0.22\nBrazil\nMEGAN2.1 2.0 N/A 305 0.83 <0.005 0.70 0.53 0.28\nBR-Sa1, Brazil (54.959 °W, 2.857 °S)\nTable R7 Comparison of GEE-MEGAN and MEGAN 2.1 at the Beijing site with flux of isoprene\nResolution\nSites Location Model N R P R2 RMSE MAE\n(m)\nGEE-MEGAN 1500 474 0.76 <0.005 0.57 1.10 0.55\n116.370 °E,\nBeijing, China 500 474 0.76 <0.005 0.57 1.01 0.53\n39.9742 °N\nMEGAN2.1 N/A 474 0.74 <0.005 0.55 1.47 0.75\nRevised Table 1 (Now Table S2 in the Supplementary Information):\nTable 1 (Now Table S2 in the Supplementary Information) Comparison of observed and modeled isoprene\nfluxes across global sites. Metrics include the number of observations (N), correlation coefficient (R), p-value (P),\ncoefficient of determination (R²), root mean square error (RMSE, mg m-2 h-1), and mean absolute error (MAE, mg\nm-2 h-1). Results are shown for GEE-MEGAN simulations at 1500 m and 500 m resolutions, alongside MEGAN2.1\nresults where available.\nSites Model EFa N R P R2 RMSE MAE\n(m)\n1500 370 0.89 <0.005 0.79 0.58 0.27\nHyytiälä, GEE-MEGAN 1.6\n500 370 0.90 <0.005 0.81 0.38 0.18\nFinland25,b\nMEGAN2.1 1.6 N/A 370 0.89 <0.005 0.80 0.74 0.32\n1500 305 0.90 <0.005 0.81 1.68 0.88\nGEE-MEGAN 7.0\n500 305 0.90 <0.005 0.81 1.93 1.03\nBR-Sa1,\n1500 305 0.90 <0.005 0.81 0.48 0.23\nBrazil26,c GEE-MEGAN 2.0\n500 305 0.90 <0.005 0.81 0.44 0.22\nMEGAN2.1 2.0 N/A 305 0.83 <0.005 0.70 0.53 0.28\n1500 474 0.76 <0.005 0.57 1.10 0.55\nBeijing, GEE-MEGAN 10.0\n500 474 0.76 <0.005 0.57 1.01 0.53\nChina27,d\nMEGAN2.1 10.0 N/A 474 0.74 <0.005 0.55 1.47 0.75\nLin’an, 1500 5 1.00 <0.005 0.99 0.19 0.14\nGEE-MEGAN 6.6\nZhejiang, 500 5 1.00 <0.005 0.99 0.13 0.11\nChina28,e MEGAN2.1 N/A N/A N/A N/A N/A N/A N/A N/A\nMOFlux, 1500 16 0.85 <0.005 0.72 10.80 9.34\nGEE-MEGAN 10.0\nMissouri, 500 16 0.85 <0.005 0.72 11.97 10.31\nUSA29,f MEGAN2.1 N/A N/A N/A N/A N/A N/A N/A N/A\naEF(mg m-2 h-1), Emission factors. The EFs used in GEE-MEGAN were the default parameters in MEGAN2.1, while the EFs used\nin MEGAN 2.1 were locally reported EFs 25-29. “N/A” denotes that no valid values are available.\nbHyytiälä, Finland (24.192°E, 61.833°N); cBR-Sa1, Brazil (54.959°W, 2.857°S); dBeijing, China (116.370°E, 39.974°N); eLin’an,\nZhejiang, China (119.566°E, 30.300°N); fMOFlux Missouri, USA (92.200°W, 38.744°N).\nReferences:\n1. Sarkar, C. et al. PTR-TOF-MS eddy covariance measurements of isoprene and\nmonoterpene fluxes from an eastern Amazonian rainforest. Atmos. Chem. Phys. 20, 7179–\n7191 (2020).\n2. Acton, W. J. F. et al. Surface–atmosphere fluxes of volatile organic compounds in Beijing.\nAtmos. Chem. Phys. 20, 15101–15125 (2020).\n3. Bai, J., Guenther, A., Turnipseed, A., Duhl, T. & Greenberg, J. Seasonal and interannual\nvariations in whole-ecosystem BVOC emissions from a subtropical plantation in China.\nAtmos. Environ. 161, 176–190 (2017).\n4. Vogl, T., Hrdina, A. & Thomas, C. K. Choosing an optimal β factor for relaxed eddy\n43\naccumulation applications across vegetated and non-vegetated surfaces. Biogeosciences 18,\n5097–5115 (2021).\n2. Lack of quantitative discussion on air quality and climate implications\nThe section “Implications for air quality and climate” lacks quantitative analysis. As the authors\nnote, BVOC emissions influence ozone, aerosols, cloud formation, and atmospheric oxidative\ncapacity. Given this context, I would expect the authors to quantify these impacts. For instance,\nhow much ozone or SOA production changes when switching from the default MEGAN to\nGEE-MEGAN. Without such estimates, the implications remain speculative.\nResponse: We thank the reviewer for this important and constructive suggestion. We fully agree\nthat given the well-established influence of BVOC emissions on atmospheric chemistry,\nincluding ozone production, SOA formation, oxidative capacity, and radiative forcing, it is\nessential to provide a more quantitative context to support broader implications discussed.\nWhile our current study focuses primarily on the emission side, especially the improvement in\ncapturing land-use and land-cover (LUCC) heterogeneity in urban-forest transition zones, we\nrecognize the value of connecting those emission differences to downstream air quality and\nclimate effects.\nAlthough we did not perform full chemical transport simulations, we have already included a\npreliminary quantification in Los Angeles (Lines 147–162), where we estimated community-\nlevel increases in OH reactivity (OHR) up to 23.4 times higher with GEE-MEGAN vs.\nMEGAN default, and in SOA formation potential (SOAFP) up to 25.1 times higher under the\nsame conditions.\nTo strengthen the discussion of broader implications, we have expanded the revised manuscript\nby incorporating findings from published observations and modelling studies that quantify the\natmospheric effects of emissions. These external results may help place our work in a broader\ncontext and provide more persuasive evidence beyond our own analysis. For example, recently\nWei et al. (2024) showed that enhanced urban vegetation in New York City can increase peak\n3 x\n(2014) demonstrated that reductions in BVOC emissions due to land-use change can induce\nradiative forcing effects comparable to surface albedo-driven warming associated with land\ncarbon loss2.\nThese references have been incorporated into the Discussion section to better contextualize the\npotential downstream consequences of GEE-MEGAN’s refined emission estimates on air\nquality and climate.\nAlthough the current study is focused on emissions rather than full atmospheric chemistry, we\nfully agree that understanding chemical and radiative impacts is critical next step. We are\nactively developing workflows that couple GEE-MEGAN with regional and global chemical\ntransport models (e.g., GEOS-Chem, CHIMERE) to evaluate the implications for tropospheric\nO , SOA, and climate forcing.\nenable the broader atmospheric science and modelling community to easily incorporate high-\nresolution, near real-time BVOC emissions into their simulations and advance further\napplications in air quality and climate research.\nRevised Text (Lines 198-200):\n“As illustrated by the examples from Los Angeles discussed above, GEE-MEGAN provides a\nmore precise understanding of the contributions of biogenic emissions to total urban terpenoid\nemissions, which is essential for evaluating and their roles in air pollution, including ozone and\nSOA formation19,20,42.\nWe are happy to make further adjustments if the editor or reviewer prefers relocation for\nstructural balance.\n50"}]

Decision letter
Response to Reviewers’ Comments
Thank you again for submitting your manuscript "Improved modelling of biogenic emissions
in human-disturbed forest edges and urban areas" to Nature Communications. We have now
received reports from 2 reviewers and, based on their comments, we have decided to invite a
revision of your work. Your revision should address all the points raised by our reviewers (see
their reports below).
Both reviewers, while highly positive, make numerous constructive comments which will
strengthen your manuscript. In particular we will need to see a more rigorous comparison
between GEE-MEGAN and MEGAN including improved quantification of the implications of
what GEE-MEGAN reveals, and improved discussion on the effects that the resolution of inputs
will have on the reliability of your approach. Please note that Nature Communications has a
more generous word limit than the journal you initially submitted to (5000 main text, 10
figures/tables, unlimited methods and supplementary information) which should allow you to
expand on all points necessary.
When resubmitting, you must provide a point-by-point response to the reviewers’ comments.
Please show all changes in the manuscript text file with track changes or colour highlighting.
If you are unable to address specific reviewer requests or find any points invalid, please explain
why in the point-by-point response.
Important: In addition to the above, you must comply with the following editorial requests;
we will not be able to proceed with your revised manuscript otherwise. Please also see
the Nature Communications formatting instructions, which you may find useful while
preparing your revised manuscript.