Having the ability to accurately and effectively obtain soil organic carbon (SOC) spatial information is critical for assessing soil carbon sequestration capacity and mitigating climate change. However, there remains a significant research gap in the collaborative application of multi-source data and their impact on model estimation accuracy. This gap limits the ability to assess soil carbon pools accurately. Therefore, we propose a data-model fusion framework that uses three types of multi-source data-environmental variables, optical remote sensing, and synthetic aperture radar (SAR)- along with three machine learning algorithms to predict SOC. We conducted data fusion of SOC field observation data and model simulations using high-accuracy surface modeling (HASM). The results showed that: (1) The data VII combination, which incorporates all three data types, paired with support vector machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost) models, obtained higher prediction accuracy (R2 increased by 4 % - 53 %, and RMSE decreased by 18 % - 25 %) compared to other data combinations. (2) After data fusion using HASM, simulation accuracy improved significantly (R2 increased by 18 % - 22 %, and RMSE decreased by 2 % - 12 %). Additionally, the spatial distribution pattern was more reasonable, with corrections made to previously underestimated and overestimated SOC content. This study demonstrates that multi-source data fusion combined with machine learning techniques can achieve optimal results for SOC prediction. This approach provides an accurate and novel method for estimating SOC at national and global scales and offers scientific guidance for the spatial planning of terrestrial carbon sink strategies.

Drought stress is increasingly constraining forest recovery under climate change. However, national-scale differences in drought resilience between natural and planted forests remain unclear. Here, we combined event-based drought characterization with lag-1 temporal autocorrelation (TAC) analysis of solar-induced chlorophyll fluorescence (SIF) and net ecosystem exchange (NEE). We assessed forest recovery dynamics across China from 2000 to 2022. Results show a widespread slowdown in forest recovery. Positive TAC trend slopes occurred in 60.94% of SIF pixels and 61.05% of NEE pixels. Drought intensity and duration were more consistently associated with increasing TAC than drought frequency. This indicates that forest recovery was constrained mainly by the severity and persistence of water deficit. Clear forest-type differences were observed. Planted forests had higher proportions of positive TAC slopes than natural forests for both SIF (69.35% vs. 56.17%) and NEE (65.33% vs. 58.19%). They also responded more strongly to drought-event gradients. Planted forests responded more strongly to drought-event gradients than natural forests. For SIF-derived TAC trend slopes, the responses to drought intensity and duration were 1.87- and 3.23-fold stronger in planted forests, respectively. For NEE-derived TAC trend slopes, the response to drought intensity was 1.24-fold stronger in planted forests, whereas the difference in response to drought duration was relatively small.These findings indicate that planted forests are more sensitive to drought-related recovery slowdown, especially in photosynthetic activity. Our study highlights the need to integrate drought-event characteristics and forest type into resilience assessments and to improve planted forest management under future climate extremes.

Cropland abandonment is a widespread phenomenon globally, including in China. However, the role of carbon sequestration on abandoned cropland in climate change mitigation remains uncertain, largely due to the dynamic interplay between abandonment and recultivation as well as inconsistencies in definitions across studies. The duration of continuous abandonment is a key determinant of carbon sequestration magnitude. In this study, the maximum continuous abandonment duration for each pixel across China was mapped using annual land-use maps from 1990 to 2025. The maximum carbon sequestration potential—expressed as cumulative Net Ecosystem Productivity (NEP)—was then estimated by integrating these duration data with corresponding annual NEP. Results show that the average maximum abandoned duration is 14.62 years, with the 6–10 years category accounting for the largest proportion of abandoned pixels (24.50%). In contrast, longer duration categories (26–30 years and >30 years) represent only 10.44% and 3.72%, respectively, indicating that most abandoned cropland is recultivated before substantial net carbon accumulation (reflected in positive NEP) can occur. Sensitivity analysis suggests that the choice of threshold duration has only a marginal influence on estimates of abandoned area and duration. The total maximum net carbon sequestration from cropland abandonment over the study period amounts to 79 Tg C, which is lower than the emission reduction potential achievable through appropriate agricultural management measures. A comparative analysis of emissions and sequestration indicates that relying solely on vegetation recovery for CO2 removal is insufficient to support carbon neutrality objectives within the cropland system. These findings underscore the need to prioritize emission-reduction strategies in agricultural management over passive reliance on abandonment-driven carbon sinks.

Spartina alterniflora exhibits vigorous growth and remarkable adaptability, enabling its rapid expansion throughout the intertidal zones of Shandong Province. As an invasive species, it not only disrupts native coastal ecosystems but also incurs significant economic burdens. Although substantial resources have been allocated by local authorities for its control, a comprehensive evaluation of these management efforts remains lacking. In particular, the influence of tidal dynamics on the spatial distribution of S. alterniflora has been largely overlooked, underscoring the need for advanced remote sensing approaches to accurately monitor. In this study, we propose a method to monitor S. alterniflora by combining low-tide imagery with phenological features. By utilizing low-tide images, we effectively overcome the impact of tidal fluctuations on monitoring accuracy. Using the Maximum Spectral Index Synthesis and Otsu algorithms, we achieved efficient, automated classification and change detection of S. alterniflora in Shandong Province, with an overall accuracy of 90.55%. In 2019, the total area of S. alterniflora was 11,386.05 ha, which decreased to 1787.13 ha by 2023. The distribution of S. alterniflora in Shandong followed a trend of initial increase followed by a decrease from 2019 to 2023. By 2023, the eradication rate had reached 91.10%, demonstrating the outstanding success of the province's management efforts. Although the management efforts have been somewhat effective, they have also led to significant carbon storage loss. These results validate the effectiveness of combining low-tide imagery with phenological features, offering a reference for similar studies in other coastal regions. Future S. alterniflora management should incorporate more scientific approaches, including the consideration of carbon emissions, to promote the sustainable development of coastal ecosystems.

This study examines historical trends and future projections of extreme temperature indices in Bangladesh–India–Myanmar (BIM), a region of critical importance for regional food security and socioeconomic stability. Using ERA5 reanalysis data (1960 to 2019) and an ensemble of 5 corrected Coupled Model Intercomparison Project Phase 6 models under 4 climate scenarios, we project spatial and temporal changes in 8 extreme temperature indices for 2020 to 2100, with a focus on near-term and far-term periods. Our findings indicate a complex trajectory of temperature extremes: while under low-emission scenarios, the frequency of extreme warm events is projected to stabilize or even decline in the far future, overall warming trends persist, and extreme cold events may become more frequent. In contrast, under high-emission scenarios, both extreme warm and cold events are expected to intensify, with India exhibiting the highest interannual variability, Bangladesh showing more stable trends, and Myanmar experiencing an increase in warm extreme frequency. The projected amplification of temperature extremes underscores the urgent need for regionally coordinated adaptation strategies, including climate-resilient agricultural systems, transboundary disaster preparedness, and sustainable water management frameworks. This study highlights the importance of enhanced regional collaboration in climate governance to mitigate shared vulnerabilities and support sustainable development pathways across the BIM region.

As crop yield stagnation, climate change, and the rising demand for agricultural products pose increasing challenges, mapping crop systems is becoming more and more important. Winter wheat is one of the major cereal crops cultivated in China, ranking as the third largest crop in terms of production and harvested area. Accurately mapping winter wheat is necessary for implementing effective farm management practices. While many studies have successfully produced high spatiotemporal resolution land cover maps, relatively few map products of crop types are available in China. The growing archive of satellite image time series provides enormous opportunities to map crops more closely. This research presents a two-step method to map winter wheat based on Sentinel-1 and Sentinel-2 time-series data from Shandong Province using the deep learning approaches. The winter crops were firstly mapped using time-series optical vegetation indices employing the deep learning methods. Then winter wheat was extracted from the winter crops mask by coupling optical and synthetic aperture radar time-series images. The results indicated that the precision of mapping winter wheat using Temporal Convolution Neural Networks (TempCNN) achieved the highest precision in mapping winter wheat, with an overall accuracy of 93.7 %, a kappa coefficient of 0.907, and an F1-score of 0.989. This was followed sequentially by the Residual 1D convolutional neural networks (ResNet), the Multi-Layer Perceptron (MLP), and the Lightweight Temporal Self-Attention Encoder (L-TAE). The Temporal Attention Encoder (TAE) model demonstrated the lowest precision among the compared models. The results agree well with independent county- level official census winter wheat area data (R2 = 0.936). The proposed framework can also be applied in other regions to generate maps of different crops, so future work can extend the proposed model to other agricultural regions, where an increased number of crop types and natural vegetation types can be included and tested. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Industrialization and urbanization have intensified land-use pressures on agroecosystems. Monitoring cultivated land use intensity (CLUI) is crucial for implementing sustainable agriculture. However, current agroecosystem management in Shandong Province lacks high-resolution CLUI information. To address this gap, this study measured and analyzed CLUI at a 1-km scale in Shandong Province from 2018 to 2022, using self-produced crop maps and the human appropriation of net primary production (HANPP) framework. The spatial autocorrelation model was used to analyze the spatiotemporal pattern and aggregation characteristics of cultivated land use intensity. The influencing factors of CLUI were analyzed using the propensity score matching method, which helps reduce the interference of confounding factors. The results are as follows: (1) The wheat-maize planting pattern in Shandong Province has remained relatively stable, with a notable trend toward intensified cultivation in the western region. (2) CLUI exhibited notable spatial and temporal heterogeneity, with low and medium values predominantly located in the western region. CLUI increased from 1.13 to 1.24, exceeding the global average of 0.84. (3) CLUI showed significant spatial aggregation characteristics. In 2018, 2020, and 2022, the western region was mainly characterized by high-high and high-low types. In 2019 and 2021, it was mainly characterized by the low-low type, with less prevalence of low-high type. The center of gravity of high-high and low-high types shifted southwest, whereas that of high-low and low-low types shifted northeast. (4) Chemical fertilizers, pesticides, and plastic mulch exhibited significant positive correlations with CLUI, whereas temperature and precipitation showed significant negative correlations. Favorable natural conditions can mitigate human interference, leading to lower CLUI.

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest‐grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar‐optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region.

Winter wheat, as one of the world's key staple crops, plays a crucial role in ensuring food security and shaping international food trade policies. However, there has been a relative scarcity of high-resolution, long time-series winter wheat maps over the past few decades. This study utilized Landsat and Sentinel-2 data to produce maps depicting winter wheat distribution in Google Earth Engine (GEE). We further analyzed the comprehensive spatial-temporal dynamics of winter wheat cultivation in Shandong Province, China. The gap filling and Savitzky-Golay filter method (GF-SG) was applied to address temporal discontinuities in the Landsat NDVI (Normalized Difference Vegetation Index) time series. Six features based on phenological characteristics were used to distinguish winter wheat from other land cover types. The resulting maps spanned from 2000 to 2022, featuring a 30-m resolution from 2000 to 2017 and an improved 10-m resolution from 2018 to 2022. The overall accuracy of these maps ranged from 80.5 to 93.3%, with Kappa coefficients ranging from 71.3 to 909% and F1 scores from 84.2 to 96.9%. Over the analyzed period, the area dedicated to winter wheat cultivation experienced a decline from 2000 to 2011. However, a notable shift occurred with an increase in winter wheat acreage observed from 2014 to 2017 and a subsequent rise from 2018 to 2022. This research highlights the viability of using satellite observation data for the long-term mapping and monitoring of winter wheat. The proposed methodology has long-term implications for extending this mapping and monitoring approach to other similar areas.

Jiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research.

在社会经济快速发展背景下,生产、生活和生态用地之间的转化加速,对生态环境和自然资源协调利用产生较大影响.基于2000-2018年的土地利用遥感解译数据,按照"生产-生活-生态"土地利用主导功能分类,通过地学信息图谱、生态环境综合评价指数、土地利用转移矩阵等方法,定量分析了京津冀地区2000-2018年三生用地时空格局变化及其对生态环境的影响.结果显示:(1)在2000-2018年期间,研究区生活用地和生态用地呈增加趋势,生产用地呈减少趋势.三生用地空间变化以生产用地转变为生活用地为主,同时存在生产用地转变为生态用地;(2)从空间分布上看,东部及北部生态环境质量状况较好,西南部状况较差,从数量上看总体呈现转好态势;(3)在生态环境质量变差区,生活用地的增加比率相对较高、生态用地减少;相反,在生态环境质量变优区,生活用地的增加比率相对较低、生态用地面积增加.结果可为京津冀地区土地资源可持续利用、降低生态环境风险提供有益参考.

Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.

高寒植被生长对气候变化的适宜性是生态学基础问题.基于2000-2018年三江源区气象数据和植被吸收光合有效辐射比数据,拟合了高寒草地植被生长的气候适宜性参数,以探讨气候适宜性指数的时空格局及气候影响.结果表明:气候适宜性参数中,高寒草地植被生长最低温度和最适温度分别为-16.69℃和12.55℃,水汽压亏缺值限制生长的最高和最低值分别为0.50 kPa和0.13 kPa,四个参数均与海拔高度呈负相关关系;三江源地区高寒草地植被温度适宜性指数尽管以每10年1.4％的速率增加,但水分适宜性指数和辐射适宜性指数呈下降趋势,导致综合气候适宜性指数呈不断下降的趋势,气候变化总体朝不利于高寒草地植被生长方向发展.本研究不仅为研究植被生长模拟提供本地化模型参数,也为认识气候变化影响,基于气候适宜性开展生态保护和利用提供科学参考.

South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences.

BACKGROUND:The agricultural production system is facing increasing demand pressure and environmental pressure. Green and efficient production methods are urgently needed in order to further enhance the yield of winter wheat and reduce the negative impact on the environment. Here, we analyzed the potential yield and yield gap of winter wheat in Shandong Province of China from 1981 to 2009. Meanwhile, we specified the effects of sowing time, irrigation and fertilization scheme, and variety characteristics on winter wheat. RESULTS:In the past 29 years, the yield gap in most areas of Shandong has become smaller, because the actual yield has increased and the potential yield has changed little under the background of climate change. In addition, it is found that delaying sowing date is beneficial to increase yield by helping winter wheat avoid adverse climate conditions. Also, an irrigation amount of 240 mm and nitrogen application amount of 180-210 kg ha-1 are best to maintain high yield, high resource utilization rate and low environmental pollution in this area. These suggested levels are lower than those currently used by many local farmers. Wheat varieties with longer grain-filling period and photoperiod response, higher grain-filling rate and grain weight were more adaptable to climate change. CONCLUSION:Improving agronomic management measures can significantly increase the yield of winter wheat and narrow the yield gap. This study can provide valuable information for improving the production potential of winter wheat, and for reducing the damage of agricultural activities to the environment. © 2022 Society of Chemical Industry.

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model.

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The size of cultivated land decreased during both intervals. The artificial surfaces gains were active for all three reaches and had strong evidence. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–2010 and 2010–2020. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase.

Habitat quality is an important indicator for measuring regional biodiversity and ecosystem service value. A change in habitat quality is the direct result of the interaction between human activities and the natural environment. In this study, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was used to evaluate the habitat quality of the Yellow River Basin (YRB) from 1980 to 2018. We further analyzed the quantity and spatial transfer status of habitat quality quantitatively using the Geo-informatic Tupu method. The results show that the habitat degradation degree under human disturbance showed a trend of increasing first and then decreasing, with values of 0.0196 in 1980, 0.0200 in 2000, and 0.0199 in 2018. In addition, it presents two ring structures: light-severe-high-moderate and light-moderate-high-severe in space. The overall level of habitat quality in the basin is relatively good, but there is a trend of decline, which are 0.6091, 0.6069, and 0.6049 in the three stages respectively. The spatial distribution of habitat quality showed a pattern of high in the middle and low on both sides. The habitat quality has been restored in some areas. The transition between good and medium and good and excellent in the Tupu change units of the habitat quality grade is the most intense. Both stages are mainly the transformation from high-grade to low-grade habitat quality, but there is a trend of gradual improvement. The findings could have theoretical support and policy implications for the maintenance of biodiversity and the protection of the natural environment in the Yellow River Basin.

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

We propose a fundamental theorem for eco-environmental surface modelling (FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling (FTESM). The Beijing-Tianjin-Hebei (BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling (HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-mean-square error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.