Temperature exerts a first-order control on vegetation photosynthesis and transpiration. Yet most studies investigating temperature impacts on plants rely on near-surface air temperature, rather than canopy temperature-the temperature plants actually experience. Because canopy temperature directly regulates ecosystem function, it provides a more accurate measure of vegetation-climate interactions. Combining Earth System Model (ESM) simulations and satellite observations in a dual emergent constraint, here we show that canopy temperature is projected to increase substantially more than air temperature (~0.11-degrees more or a 16% increasein their difference) over the 21st century. The ESM ensemble median fails to capture these stronger increases in the majority of vegetated regions. We find that the largest projected increases in the difference between canopy and air temperature are predicted to occur in regions where elevating moisture stress-particularly rising vapor pressure deficit-increasingly constrains vegetation growth and transpiration. This implies that future warming will impose stronger constraints on plant function than currently estimated. Relying on air temperature alone will therefore lead to systematic underestimation of temperature effects on photosynthesis, vegetation growth, and the land carbon sink. Accurate representation of canopy temperature in ESMs is thus essential to improve projections of ecosystem responses and feedbacks to climate change.

Land-use simulation is essential for understanding future spatiotemporal changes on the Earth’s surface. Most existing methods simulate land use change by modelling dynamic processes among cell labels, often overlooking the landscape-scale patterns that emerge from these cells. Moreover, reliably constructing evolutionary rules and key parameters to simulate future land use change remains a fundamental challenge. We proposed MapsGT, a novel generative deep learning approach for the simulation of land use change over multiple classes. The framework consists of two core components: MapsVAE, which learns compact landscape encodings to capture spatial information from historical maps of land-use and geographic environment; and EMTrans, which models spatiotemporal dependencies within these encodings to generate future maps of land use. Experiments in the Pearl River Delta and the Changsha-Zhuzhou-Xiangtan urban agglomerations showed that MapsGT excels in short-term simulation, achieving a high Figure of Merit and User’s Accuracy. Additionally, a newly developed regional consistency index (RCI) reveals the model’s ability to capture and reproduce complex landscape patterns with high spatial authenticity. MapsGT provides a valuable framework for geospatial artificial intelligence that complements existing simulation methods of land use change.

Future land use change and carbon storage dynamics: a machine learning-based spatial scenario approach

This study applied the Markov chain model integrated with Geographic Information Systems (GIS) to analyze land use trends in Cu Chi district, Ho Chi Minh City, from 2010 to 2020 and to project land use changes for 2025 and 2030. Utilizing land use status maps in 2010, 2015, and 2020, the study developed a land use transition probability matrix to simulate future land use trends. The results indicated that from 2010 to 2030, agricultural land and perennial cropland will decline, while residential land and non-agricultural production land will expand significantly due to urbanization and industrialization. The model's accuracy was validated by comparing the 2020 forecast with actual data, yielding an overall model average of 9.08% for Mean Absolute Percentage Error (MAPE) and 428.73 ha for root mean square error (RMSE), demonstrating high reliability. By 2030, residential land is projected to increase (+973.81 ha, +28.52%), whereas perennial cropland will decline sharply (-1,946.22 ha, -12.45%), primarily being converted into urban and industrial zones, posing challenges in balancing urban expansion, economic growth, and land resource conservation. This research provides a scientific basis to support land use planning, thereby assisting policymakers and urban planners in developing sustainable land management strategies for Cu Chi district.

Abstract. The terrestrial biosphere absorbs about one third of anthropogenic CO2 emissions, thereby significantly slowing human-induced climate change. Its capacity to act as a carbon sink strongly depends on climate conditions, particularly soil moisture (SM), which can constrain plant growth and amplify land–atmosphere feedbacks. Therefore, accurately capturing these effects in Earth System Models (ESMs) is critical. Using dedicated experiments of the Land Feedback Intercomparison Project (LFMIP, an experiment within the Land Surface, Snow, and Soil Moisture Model Intercomparison Project, LS3MIP) from the latest generation of ESMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that projected SM changes substantially reduce the land carbon sink by the end of the century (2070–2099). This reduction is mainly driven by SM variability, highlighting the importance of SM extremes, which are projected to become more frequent and intense under climate change. Our results confirm those of the previous model generation based on the Global Land–Atmosphere Climate Experiment-Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5). The results show that the strong negative impact of SM changes on the land carbon sink shown for GLACE-CMIP5 is less severe in LFMIP. A more in-depth analysis reveals that this is due at least in part to the specific ESM sampling of the respective experiments, with participating ESMs from CMIP5 generally showing a stronger drying trend. Despite agreement on the negative impact of SM on the land carbon sink in most tropical and mid-latitude ecosystems in both sets of multi-model experiments, there are large intermodel differences in the projected magnitudes. As SM can influence land carbon uptake both directly and indirectly via land–atmosphere coupling, we conduct a contribution analysis on the impact of direct and indirect SM effects on carbon uptake, which reveals that SM–atmosphere interaction dominate SM-induced changes globally. However, models show disagreement on the magnitude of these effects. Intermodel differences arise mainly from varying sensitivities of GPP to SM-related direct and indirect effects, suggesting that differences likely stem from varying representations of water-stress related processes across ESMs. Our findings highlight SM–atmosphere coupling as a critical factor for future land carbon uptake. Improving the representation of water stress processes, plant hydraulics, and vegetation characteristics in ESMs is essential for reducing uncertainty in projections. Maintaining and possibly extending the experimental setup to a larger set of models in future CMIP generations will be key to advancing our understanding of SM-carbon interactions and consequently of the evolution of the land carbon sink under human-induced climate change.

ABSTRACT Global warming increases the potential risks of hydrological extremes, such as extreme precipitation and flood. Limited attention has been given to the integrated effects of climate change, land‐use change, and socioeconomic advancement on flood risk under global warming of 1.5°C and 2.0°C threshold outlined in the Paris Agreement. Here, utilizing the latest coupled model Intercomparison Project 6 (CMIP6), the new shared socioeconomic pathway scenarios (SSPs), hydrological model and future land use simulation (FLUS) model, we perform a comprehensive assessment of the flood risk in the Huai River Basin (HRB) under the global warming of 1.5°C and 2.0°C scenarios. The results reveal that (1) more intense extreme precipitation events will occur in the HRB under two global warming scenarios. The increases in extreme precipitation are approximately twice as high under 2.0°C than under 1.5°C global warming scenario; (2) under global warming of 1.5°C and 2.0°C scenarios, future 100‐year floods will increase by 18.4% and 19.2%, respectively, in the HRB; and (3) high flood‐risk areas are expected to primarily locate in regions with unfavorable flood regimes, with increases of 4.3% and 17.8%, and very high flood‐risk areas are projected to expand by 2% and 4.3%, respectively. Considering the holistic effects of future environmental changes on the flood risk, it is imperative to incorporate flood control management and prevention measures into regional adaptation strategies.

Financial incentive programs, commonly administered by public institutions, have long supported sustainable land management practices in the United States, including soil conservation, water quality improvement, and biodiversity preservation. Recently, these initiatives have expanded under the concept of nature-based solutions (NBS), which emphasize land-based practices that deliver co-benefits for people and ecosystems. However, the effectiveness of such programs often depends on how well they align with landowners' diverse values, preferences, and motivations. This study examines factors influencing forest and farm landowners' likelihood of enrolling in traditional and NBS-oriented incentive programs. We surveyed 2,000 forest and farm landowners across four regions in North Carolina to assess how ownership motivations, land use intentions, and personal values influence program participation preferences and compensation expectations. The full information maximum likelihood regression results reveal significant differences between forest and farm landowners in their motivations, future land management plans, and financial expectations. Landowners who reside on their land or have never applied to any cost-share programs before are generally less inclined to participate in either program type. These findings highlight the importance of designing targeted outreach strategies and tailoring program structures to better reflect the values and needs of different landowner groups, thereby improving landowners' participation and enhancing the long-term effectiveness of land-based conservation and sustainability initiatives.

Combined drought index for drought monitoring and severity assessment under future climate and land use changes.

Biodiversity net gain (BNG) is a "net outcome" planning policy which aims for development projects to leave biodiversity in a better state than before they started. Understanding the origins and history of existing mandatory BNG is necessary to understand the drivers and barriers that have influenced the policy to date and could inform the development and implementation of future BNG policies. Biodiversity net gain legislation was first discussed in Parliament in England through the passage of the Environment Act (2021) and became a mandatory requirement for most terrestrial and intertidal developments in February 2024. The policy uses habitat attributes as a proxy for biodiversity and represented the widest reaching net outcome policy in the world at the point of its introduction. As such, it is expected to have a significant impact on future land use decisions in England. This paper uses a mixture of literature review and the knowledge of those involved in the early stages of this BNG policy development in England to present a timeline of the stages that have led to mandatory biodiversity net gain. In doing so, we highlight formative events and documents, as an important first step in understanding its history and understanding how this can be used to inform future biodiversity policy.

ABSTRACT Predicting urban waterlogging risk influenced by land use dynamics has become increasingly important with growing urbanization. However, prior studies have scarcely considered the influence of land use patterns. The present study proposes a novel methodology for predicting waterlogging risk, while exploring the enhancement effect of incorporating landscape indices on prediction. The present study proposes an enhanced framework for predicting waterlogging risk, while exploring how incorporating landscape indices affects prediction. This framework integrates the MaxEnt and PLUS methods, which are specifically designed to incorporate landscape indices. It can enhance the predictive performance and interpretability of waterlogging risk. The integrated model was constructed using data from 2015, and its accuracy was validated with data from 2020. First, landscape indices were computed and the PLUS model was constructed based on the land use data from 2015. Subsequently, we trained two sets of MaxEnt models (with and without landscape indices) using the waterlogging data from 2015, and simulated the landscape indices and waterlogging risk in 2020 with the MaxEnt‐PLUS method being utilized for this purpose. The findings indicate that the incorporation of landscape indices resulted in an enhancement of the AUC value of the MaxEnt model from 0.919 to 0.928. Furthermore, the results of the waterlogging risk simulation are more consistent with actual waterlogging hotspots. Therefore, the calibrated PLUS model was deemed appropriate for the purpose of predicting future land use change. Then, the MaxEnt‐PLUS method was employed to project the waterlogging risk in 2030, and the changing patterns of the various risk areas during 2015–2030 were examined. In summary, the proposed method could enhance the reliability of waterlogging risk prediction, thereby providing decision support for sponge city planning and stormwater management.

Abstract Characterizing terrain surface properties is an essential step in assessing the feasibility of landing successfully at a location on a planetary surface. Slopes and terrain ruggedness index (TRI) values derived from high-resolution (2 m pixel −1 ) digital terrain models provided important constraints in selecting the landing site for the upcoming Payloads and Research Investigations on the Surface of the Moon program as part of the Commercial Lunar Payload Services task order CP-21 mission. The selected landing site needed to balance safety requirements with the ability to achieve the science and exploration goals of the Lunar Vulkan Imaging and Spectroscopy Explorer payload. In this study, we compare several morphometric parameters in the context of the CP-21 landing site on Mons Gruithuisen Gamma, or the Gamma dome, and quantify the information they convey about lunar surface properties to assess their utility for future landing site evaluation. TRI was found to be a useful metric for assessing landing site safety. Metrics that better decouple slope and surface roughness, the vector ruggedness measure and the standard deviation of slope, provided additional information about surface characteristics and textures such as the degree to which roughness is isotropic.

Prediction of future Land Use/Land Cover (LULC) changes using random forest model in google earth engine (2035–2045)

This study focuses on the Maritime Continent within the Coordinated Regional Climate Downscaling Experiment–Southeast Asia (CORDEX–SEA) domain. This study provides the first systematic evaluation of present-day diurnal rainfall simulations over the CORDEX–SEA domain, and projects future changes under the Representative Concentration Pathway 8.5 scenario, emphasizing the Maritime Continent islands and adjacent coastal–offshore environments. The analysis covers both boreal summer and winter seasons and includes a diagnosis of the underlying dynamic and thermodynamic mechanisms driving the projected rainfall changes. High-resolution regional climate models better capture present-day diurnal rainfall amplitude, peak timing, and coastal-to-offshore propagation than their driving global climate models, with particularly marked improvements in propagation over the Maritime Continent. Future projections in the late 21st century indicate minimal change in diurnal timing over land but a widespread weakening of amplitude, especially in summer, with reduced coastal propagation. Over oceans, changes display a latitudinal pattern, with amplitude increases north of 10°N and decreases to the south. Physical mechanism analysis further reveals that reduced surface specific humidity and weakened surface wind convergence jointly suppress the diurnal rainfall peak, with thermodynamic effects dominating over Sumatra and dynamic effects over Borneo. These results provide new physical insights into the coupled roles of circulation and moisture in shaping the spatial heterogeneity of future diurnal rainfall changes over the Maritime Continent. • CORDEX improves simulation of diurnal rainfall propagation in the Maritime Continent. • Warming climate suppresses land diurnal rainfall, weakening future land–sea contrast. • Dynamic and thermodynamic shifts drive regionally varying diurnal rainfall weakening.

Forest plantations cover large areas globally, but their climate benefits remain unclear, hampering projections of the future land carbon (C) sink. Here, we combined an unprecedented 14-years series of eddy-covariance CO2 fluxes (net ecosystem productivity, NEP) with biometric measurements to explore C balance in a commercial Eucalyptus plantation in Brazil across three rotations. The plantation exhibited a 14-year average and maximum annual NEP of 9.8 and 20.2 MgC ha-1 y-1, respectively, which ranks among the highest documented forest productivity. The amount of time before the forest recaptures as much C as was emitted after harvest was substantially shorter than previously published values: 20 and 27 months, for first and second harvest, respectively. Time series of leaf area index, but not trunk biomass growth, were strongly related to NEP dynamics (> 75% of explained variance at annual and monthly scales) suggesting possible amenability to remote-sensing assessment in Eucalyptus plantations. Estimates of C accumulation in litter and below-ground stocks varied from positive to negative depending on the methodology used, and showed substantial differences across rotations. This indicates that long-term C accumulation in commercial Eucalyptus plantations is not guaranteed, despite their high productivity.

Biogenic VOC emissions and ozone formation potential in the Yangtze River Delta under future climate and land cover changes

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures.

Abstract The terrestrial carbon sink is a critical buffer in the climate system, yet its persistence under rising atmospheric CO 2 remains a major uncertainty in Earth system projections. Using an ensemble of 20 dynamic global vegetation models from the TRENDY v12 project, we decompose the CO 2 sensitivity of the land carbon sink, represented by net biome production (NBP), to diagnose long-term changes in intrinsic efficiency. We identify a robust, emergent turning point in the simulated intrinsic CO 2 sensitivity of NBP, shifting from a significant multi-decadal increase to a sustained decline around 1980. This reversal is reproduced in both carbon-only and coupled carbon–nitrogen model classes and is consistent with a progressive decoupling between CO 2 sensitivities of carbon inputs and losses (net primary production, NPP versus heterotrophic respiration, Rh). In contrast, the turning point is not readily detectable in the total apparent sensitivity, reflecting both the dominance of high-frequency climate and land-use variability and interacting environmental effects that may partially offset intrinsic CO 2 responses. Together, these results point to a weakening intrinsic efficiency of the terrestrial carbon sink, implying diminishing marginal land uptake per unit CO 2 increase and a potentially more constrained resilience of future land carbon sequestration than suggested by raw sink magnitudes alone.

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations.

The capacity of nutrient-limited forests to enhance carbon (C) sequestration under elevated CO2 (eCO2) remains a critical uncertainty in C cycle modeling. While existing evidence suggests that low phosphorus (P) bioavailability may constrain CO2 fertilization effects on plant growth, the extent to which this limitation modulates ecosystem responses to eCO2 in forests adapted to P-deficient soils remains poorly understood. Here, using eight P-enabled models, we simulated the magnitudes and mechanisms through which P bioavailability interacts with eCO2, emulating an ecosystem-scale P enrichment experiment at a P-limited Eucalyptus forest undergoing long-term Free-Air CO2 Enrichment. While models predicted pronounced P effects on tree growth, P enrichment unexpectedly did not increase the CO2 effects on tree growth and ecosystem C sequestration. Models prioritized either CO2-driven or P-driven growth, but rarely both. This tradeoff emerged due to model-specific assumptions on 1) partitioning of the extra P in soil labile versus nonlabile pools; 2) plant photosynthetic acclimation to P deficiency; 3) C and nutrient use strategies regulating plant size and allocation; and 4) microbial-driven soil decomposition processes. By generating divergent yet biologically plausible outcomes, these predictions establish critical testable hypotheses for empirical research and highlight multiple P-related pathways that may influence the future land C sink.

Abstract. Panja K, Krishnaiah YV, Das D, Hati M, Mondal V, Chakma A. 2026. Land capability assessment for land use planning in West Tripura District, Northeast India with an integrated AHP-MCDA approach. Asian J Agric 10 (1): g100114. https://doi.org/10.13057/asianjagric/g100114. Land capability assessment is essential for evaluating land resources, revealing the strengths and limitations of land utilization, and addressing problems of land encroachment, land degradation, deforestation, and food security. In West Tripura, Northeast India, rapid urbanization and changing agricultural practices have inadvertently altered the landscape, impacting the livelihoods of local people. Proper land use planning based on land capability is needed to address land-related issues. The primary objective of this study is to analyze the land capability of the district and identify specialized land capability zones to plan for future land use through conscious utilization of the district's natural resources. In this study, USDA land capability classification was derived from soil texture, lithology, soil depth, soil fragments, slope, elevation, drainage density, rainfall, soil moisture, soil pH, land use and land cover, groundwater potential, and temperature. These factors were considered as basic parameters for evaluating land capability in this hilly region. Analytic Hierarchy Process (AHP) techniques were used as part of multi-criteria decision analysis, and weighted overlay analysis was performed through Geographic Information System (GIS) to identify potential Land Capability Classes (LCC) in the district. The present study identified seven land capability classes. Land areas in the riverine plains and lunga (intermontane valley) areas were categorized as very good (Class I), good (Class II), moderately good (Class III), and fair (Class IV). The remaining classes are unsuitable for agricultural practices but suitable for pasture, plantation, forestry, wildlife habitat, and natural vegetation. This study reveals that land capability classes II and III occupy nearly half (49.94%) of the potential land area. Field observations and land use analysis indicate that good and moderately good LCC areas associated with fertile floodplains and intensive agricultural practices are being encroached upon by unplanned land utilization, especially rapidly expanding settlements and rubber plantations.