Earths Future最新文献

{"title":"Key Environmental and Ecological Variables of Wetland CH4 and CO2 Fluxes Change With Warming","authors":"Mengze Li,&nbsp;Fa Li,&nbsp;Avni Malhotra,&nbsp;Sara H. Knox,&nbsp;Rafael Stern,&nbsp;Robert B. Jackson","doi":"10.1029/2024EF005751","DOIUrl":"https://doi.org/10.1029/2024EF005751","url":null,"abstract":"<p>Wetlands are important ecosystems for the global carbon cycle, impacting regional and global methane (CH₄) and carbon dioxide (CO₂) budgets. This study examines how environmental and ecological variables impact wetland CH₄ flux and net ecosystem exchange of CO₂ (NEE) across 17 sites globally. We also quantified the importance of variables for each wetland type and site at monthly scale under normal and warm temperatures using dominance analysis. We identified soil and air temperature (TS, TA, respectively) as key variables influencing wetland CH₄, and latent heat (LE) and shortwave radiation (SW) for NEE under normal and warm conditions. However, the importance of some variables shifted with warming. For predicting the variability of wetland CH₄ flux under warming, gross primary productivity (GPP) and LE, replacing wind direction (WD), were dominant variables for tropical swamps, while NEE was important for high-latitude fens and bogs under warm temperatures. For wetland NEE, the role of TA and TS decreased across all wetland types with warming, while vapor pressure deficit (VPD) became more important for mid and high-latitude wetlands. Our results reveal the complex responses of wetland carbon flux to environmental and ecological variables with warming and provide new insights into improving wetland models by incorporating additional variables and accounting for the changing roles of variables in carbon flux under warming.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indian Ocean Dipole Impacts on Eastern African Short Rains Across Observations, Historical Simulations and Future Projections","authors":"Yiling Zheng,&nbsp;Chi-Yung Tam,&nbsp;Matthew Collins","doi":"10.1029/2024EF005219","DOIUrl":"https://doi.org/10.1029/2024EF005219","url":null,"abstract":"<p>Eastern African “short rains” (October–December) are significantly influenced by the Indian Ocean Dipole (IOD), with increased rainfall during positive IOD events and dryness during negative IOD events. Most Coupled Model Intercomparison Project Phase 6 models overestimate the short-rain response to IOD events in Eastern Africa, especially during negative events. This is due to enhanced zonal moisture transport anomalies, mainly related to amplified IOD intensity and the westward extension of the IOD eastern core. Mean-state precipitation biases further contribute to overestimated short rains, traced back to the mean-state positive IOD-like biases. Under global warming, the contrasting short-rain responses to IOD in the Horn of Africa and Southeastern Africa are primarily driven by the westward-shifting IOD circulation. Enhanced impacts in the Horn of Africa are driven by stronger IOD-related zonal winds and the more humid mean-state atmosphere which is attributed to the IOD-like warming pattern. In contrast, weakened impacts in Southeastern Africa are associated with anomalous meridional wind changes. Additionally, due to more extreme negative IOD events, the frequency as well as severity of droughts in the Horn of Africa are projected to increase. These findings provide valuable insights into Eastern Africa's climate dynamics and inform climate adaptation strategies.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing Cumulative Impacts of Droughts Under Climate Change Does Not Alter the Ecosystem Resilience in India","authors":"Vijaykumar Bejagam,&nbsp;Ashutosh Sharma","doi":"10.1029/2024EF005888","DOIUrl":"https://doi.org/10.1029/2024EF005888","url":null,"abstract":"<p>Drought significantly impacts plant carbon uptake, a critical process for regulating atmospheric CO₂. With the intensification of droughts under climate change, understanding ecosystem responses to these events is essential. In this study, we analyzed the impacts of droughts on Net Primary Productivity (NPP) in India over the 21st century. We tested two hypotheses: (a) increasing drought intensity and frequency will exacerbate NPP reductions, and (b) climate warming will diminish ecosystem resilience, leading to greater NPP reductions per drought event. To evaluate ecosystem responses, we employed a Multi-Dimensional Resilience Index (MDRI), which integrates resistance and recovery time to quantify resilience. Our analysis revealed a substantial increase in extreme and moderate droughts, while mild droughts remained stable. Extreme droughts were projected to cause NPP reductions three times greater under the SSP2-4.5 scenario and six times greater under the SSP5-8.5 scenario compared to the baseline period (1850–2014). Ecosystems in the Western Ghats and lower Himalayan regions demonstrated moderate resilience due to prolonged recovery times and moderate resistance. Conversely, ecosystems in Northeastern India exhibited high resilience, characterized by strong resistance and shorter recovery periods. Interestingly, while resistance exhibited an increasing trend, likely influenced by carbon fertilization, recovery times showed a declining trend, potentially linked to rising temperatures. These findings do not support the second hypothesis, as no significant changes in overall ecosystem resilience were observed due to compensatory effects between resistance and recovery. This study can inform conservation strategies aimed at mitigating the adverse impacts of drought on carbon cycling.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the Effects of Policy Instruments on Cost and Deployment Trajectories of Direct Air Capture in the U.S. Energy System","authors":"Franklyn Kanyako,&nbsp;Michael Craig","doi":"10.1029/2025EF005924","DOIUrl":"https://doi.org/10.1029/2025EF005924","url":null,"abstract":"<p>Capturing and sequestering carbon dioxide (CO₂) from the atmosphere via large-scale direct air capture (DAC) deployment is critical for achieving net-zero emissions. Large-scale DAC deployment, though, will require significant cost reductions in part through policy and investment support. This study evaluates the impact of policy interventions on DAC cost reduction by integrating energy system optimization and learning curve models. We examine how three policy instruments—incremental deployment, accelerated deployment, and R&amp;D-driven innovation—impact DAC learning investment, which is the total investment required until the technology achieves cost parity with conventional alternatives or target cost. Our findings show that while incremental deployment demands significant learning investment, R&amp;D-driven innovation is considerably cheaper at cost reduction. Under a baseline 8% learning rate, incremental deployment may require up to $998 billion to reduce costs from $1,154 to $400/tCO₂, while accelerated deployment support could save approximately $7 billion on that investment. In contrast, R&amp;D support achieves equivalent cost reductions at less than half the investment of incremental deployment. However, the effectiveness of R&amp;D intervention varies with learning rates and R&amp;D breakthroughs. R&amp;D yields net benefits in all cases except at extremely low breakthroughs (5%) and very high learning rates (20%), where they are slightly more expensive. For learning rates below 20%, R&amp;D provides net benefits even at minimal breakthroughs. These findings underscore the need for comprehensive public policy strategies that balance near-term deployment incentives with long-term innovation investments if we are to ensure DACS becomes a viable technology for mitigating climate change.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EF005924","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wetland Gain and Loss in the Mississippi River Bird-Foot Delta","authors":"Jiangjie Yang,&nbsp;Zhijun Dai,&nbsp;Xuefei Mei,&nbsp;Yaying Lou,&nbsp;Sergio Fagherazzi","doi":"10.1029/2024EF005003","DOIUrl":"https://doi.org/10.1029/2024EF005003","url":null,"abstract":"<p>The Mississippi River Bird-foot Delta (MRBD) has long been at risk of deterioration due to Relative Sea Level Rise (RSLR), yet information on historical spatial distribution in wetland gain and loss remains limited. Using a Random Forest algorithm in Google Earth Engine, we extract wetland area from multiple Landsat images spanning 1990–2022. Data are integrated with sediment load, wave dynamics, sea level, and surface elevation to analyze drivers of wetland gain and loss. Results indicate a minor net change of only 1.21 km², with a total gain of 160.83 km² and a total loss of 159.62 km². Overall stability of wetland area masks significant regional variability, with notable wetland expansion in the interior and substantial losses along eastern and southeastern margins. Sediment diversion toward the interior of the delta lead to distributaries narrowing (Main Pass and Pass a Loutre) that further hindered sediment-laden water transport into deltaic margins. Wetland dynamics along the edges were closely linked to wave action, with large-scale retreat in northern (4.0 ± 9.9 m/yr), eastern (58.0 ± 48.2 m/yr), and southeastern (38.6 ± 15.8 m/yr) regions, while progradation in the southern (13.6 ± 10.1 m/yr) and western areas (7.4 ± 19.4 m/yr). Fluvial sediments significantly impact wetland growth with 1-year lag. Vertical accretion of wetlands exceeds RSLR, indicating equilibrium along vertical dimension but are affected by lateral dynamics driven by wave and fluvial sediment inputs. In conclusion, the MRBD is abandoning the distal parts to wave erosion, while focusing on building wetlands in the interior to create a more compact delta.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response of China's Terrestrial Carbon Uptake to Shift in Nitrogen Deposition","authors":"L. Zhang,&nbsp;P. Li,&nbsp;G. Yu,&nbsp;H. He,&nbsp;Y. Jia,&nbsp;J. Zhu,&nbsp;W. Ju,&nbsp;C. Zhang,&nbsp;X. Ren,&nbsp;T. Wang,&nbsp;Y. Zheng,&nbsp;H. WU","doi":"10.1029/2024EF004946","DOIUrl":"https://doi.org/10.1029/2024EF004946","url":null,"abstract":"<p>China has experienced a shift in nitrogen (N) deposition from an upward trend since 1980s to stabilized since 2001–2005 and decline in recent years due to N management. Global atmospheric chemical transport models tend to underestimate the magnitude of N deposition in China and fail to reproduce such a shift. How do underestimation and trend shift in N deposition influence China's terrestrial carbon (C) uptake remains unclear. Here we used a new N deposition data set and three independent methods to investigate the effect of N deposition on terrestrial C uptake in China. We found that the magnitude and trend of China's terrestrial C sink induced by N deposition (∆C_Ndep) would be underestimated during 1990–2015 when using commonly used global atmospheric N deposition data sets. Despite the decrease in N deposition trend, the increasing rate of ∆C_Ndep changed from 4.42 Tg C yr⁻² in 1990–2005 to 5.64 Tg C yr⁻² in 2006–2015, which was dominated by subtropical and tropical monsoon region. The interactive effect of N deposition with other environmental factors has a greater impact on the trend of ∆C_Ndep than direct effect. Our results highlight the rising terrestrial C uptake as N deposition stabilizes and the crucial role of interaction mechanisms among global change factors in assessing the impact of declining reactive N inputs on China's future land C sinks under C neutrality targets.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Declining Contribution of Plant Physiological Effects to Global Drought Characteristics With Rising CO2 Using State-of-the-Art Earth System Models","authors":"Ziwei Li,&nbsp;Fubao Sun,&nbsp;Hong Wang,&nbsp;Tingting Wang","doi":"10.1029/2024EF005548","DOIUrl":"https://doi.org/10.1029/2024EF005548","url":null,"abstract":"<p>Vegetation physiology responses to rising atmospheric CO₂ can alter the global hydrological cycle, thereby influencing drought occurrence. It has long been controversial and poorly understood how vegetation physiological effects influence meteorological drought characteristics with increasing CO₂. To investigate that, we employ multiple CO₂ sensitivity experiments of the state-of-the-art Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We quantify drought characteristics in response to rising CO₂ using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), with SPEI calculated using both the Penman-Monteith method (SPEI_PM) and energy-only method (SPEI_Rn). Our findings reveal that plant physiological effects can robustly induce more intense, frequent, and prolonged droughts under elevated CO₂ levels. Spatially, drought intensity as measured by SPI, SPEI_PM, and SPEI_Rn, resulting from CO₂ physiological forcing, is projected to increase over 61%, 69%, and 78% of global terrestrial areas, respectively. Notably, we found that the contribution of plant physiological effects (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mtext>PHY</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${beta }_{text{PHY}}$</annotation>\u0000 </semantics></math>) to drought characteristics, including intensity, frequency, and duration, exhibits a significant and spatially extensive declining trend with rising CO₂ across most land areas. This declining trend is robustly depicted in both the multi-model mean and individual models. Vegetation coverage plays an important role in the spatial pattern of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mtext>PHY</mtext>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${beta }_{text{PHY}}$</annotation>\u0000 </semantics></math>. CO₂ physiological forcing therefore exerts greater impacts in the tropics, particularly over tropical forests. Our results demonstrate that drought characteristics are expected to become less dependent on plant physiological effects with increasing CO₂, a consideration essential for accurate drought projections.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Guided Urban Climate Risk Mitigation Through Optimal Spatial Allocation of Green and Cool Roofs","authors":"JiHyun Kim,&nbsp;Suyeon Choi,&nbsp;Mahdi Panahi,&nbsp;Hocheol Seo,&nbsp;Yeonjoo Kim","doi":"10.1029/2024EF005749","DOIUrl":"https://doi.org/10.1029/2024EF005749","url":null,"abstract":"<p>With cities facing increasing challenges due to climate change, we developed a deep learning-based surrogate modeling framework to optimize urban roofing strategies for climate risk mitigation. Applied to Seoul, South Korea, the framework utilized the Weather Research and Forecasting model coupled with an Urban Canopy Model (WRF-UCM) to generate objective indices for heat stress, flash floods, and wind circulation projected to the end of this century under four roof schemes: business-as-usual, 25% and 100% cool roofs (CR25 and CR100), and 100% green roofs (GR100). These indices were used to test four deep learning algorithms: UNet, UNet++, UNet3+, and Multi-ResUNet. Multi-ResUNet demonstrated superior performance, thus it was employed to develop the surrogate model, which was applied to 262,144 multi-type roofing scenarios. Two optimal roofing scenarios were identified using the Pareto method, balancing the three climate objectives and economic costs: the first with CR100 covering 95.9% of urban areas, reducing heat stress by over 50% in 34.3% of regions and wind circulation by 10% in 27.7% of regions, and the second with CR100 covering 60.2% of urban areas, achieving a similar heat stress reduction in 21.6% of regions but a stronger reduction in wind circulation. Both scenarios had minimal impact on flash flood mitigation. This study highlights the importance of spatial configuration in maximizing the benefits of urban roofing strategies due to the heterogeneous effects across urban areas. Furthermore, the considerably lower computational time increases the practical utility of the proposed surrogate modeling framework for use in a diverse range of urban contexts, advancing global efforts to mitigate urban climate risks.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-Temporal Dynamics of Invasive Spartina Alterniflora and Its Functional Traits' Responding to Hydro-Meteorology","authors":"Bolin Fu,&nbsp;Bingyan Yuan,&nbsp;Hang Yao,&nbsp;Weiwei Sun,&nbsp;Mingming Jia,&nbsp;Zuofang Yao,&nbsp;Yeqiao Wang","doi":"10.1029/2024EF005421","DOIUrl":"https://doi.org/10.1029/2024EF005421","url":null,"abstract":"<p>Coastal wetlands represent one of the most critical ecosystem types worldwide, and offer a diverse array of vital ecosystem services. Large-scale and rapid invasion of <i>Spartina alterniflora</i> (<i>S. alterniflora</i>) has imposed significant impacts on coastal wetland ecosystems and biodiversity globally. Tracking dynamic trajectories of <i>S. alterniflora</i> and the rhythmic changes of its vegetation functional traits is important to understand the invasion mechanism. This study proposed a novel time series adaptive threshold segmentation method (NSATS) to extract <i>S</i>. <i>alterniflora</i>. Specifically, for the San Francisco Bay (SFB) of the U.S., the extraction was carried out from 1995 to 2004, while for three representative bays along the coastal zone of China, the extraction was conducted from 2011 to 2020, respectively, and to explore their spatio-temporal distribution patterns. We developed an innovative CCD-HCM method to track the historical growth trajectories of <i>S. alterniflora</i>, and evaluated their growth dynamics. Finally, we explored the phenological rhythm of <i>S. alterniflora</i> functional traits, and clarified the response mechanisms of its vegetation functional traits to hydro-meteorological factors. NSATS showed high accuracy (0.82–0.96) in identifying <i>S. alterniflora</i>. Its invasion was faster in China's bays, with rapid expansion in 2011–2020, especially in YRD. SFB remained stable, with minor changes. Functional traits showed earlier SOS and longer LOS with latitude decrease. Air and water temperatures influence <i>S. alterniflora</i> traits differently across bays. These findings aid in monitoring and controlling its invasion.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping China Aquaculture Ponds: Integrating a New Aquaculture Index With Machine Learning","authors":"JianChun Chen,&nbsp;Chen Lin,&nbsp;Kun Xue,&nbsp;Ke Song,&nbsp;ZhiGang Cao,&nbsp;RongHua Ma,&nbsp;DanHua Ma,&nbsp;YiJun Tong","doi":"10.1029/2024EF005637","DOIUrl":"https://doi.org/10.1029/2024EF005637","url":null,"abstract":"<p>Aquaculture Pond (AP) plays a vital role in ensuring food security, driving economic development, conserving resources, and maintaining ecological balance. Thus, accurately delineating the extent of AP is critical for effective policy-making in aquaculture. However, existing methods for large-scale extraction of AP face challenges, such as difficulty in transferring segmentation thresholds and confusion with similar land features, which limits the accurate determination of their spatial distribution. This study focuses on AP in China, developing a tailored spectral index for AP extraction and creating an optimized classification method for large-scale, automated AP extraction by integrating the WV_ndapi index with machine learning techniques. Using high-resolution Sentinel-2 data from 2023 and leveraging the Google Earth Engine, a nationwide AP distribution map was generated. The results indicate that: (a) The optimized WV_ndapi index extraction results indicate that the overall accuracy (OA) of AP identification across the nation reached 91%, with Cohen's Kappa of 0.88. (b) At the national scale, the spatial distribution of AP shows a pattern of higher density in the north and lower density in the south, with more AP in the east than in the west. Notably, inland AP account for 15% of the national total. (c) The contours and shapes of AP extracted used WV_ndapi index closely match the high-precision results obtained through manual digitization (0.43 m), effectively distinguishing AP from confounding features such as gully, lake, river, and shadow. In summary, the establishment of the WV_ndapi index overcomes the limitations of confusion and misclassification among similar land covers, achieving the goal of adaptive threshold at a large scale.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}