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Patterns and drivers of vegetation resilience across China and the negative impact of warm droughts 认领 引用
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作者 Xueming Zhao Zhaoju Zheng +4 位作者 Bernhard Schmid Yujin Zhao Dan Zhao Bingfang Wu Yuan Zeng 《Geography and Sustainability》 CSCD 2026年第3期63-75,共13页
Ecological resilience—defined as the capacity of ecosystems to withstand perturbations—is an important dimension of ecosystem stability.However,the spatial patterns and underlying drivers of resilience in forests re... Ecological resilience—defined as the capacity of ecosystems to withstand perturbations—is an important dimension of ecosystem stability.However,the spatial patterns and underlying drivers of resilience in forests relative to grasslands remain poorly understood.Using a temporal autocorrelation index(AC1)derived from satellite-based vegetation indices,we assessed high-resolution(250 m)vegetation resilience from 2000 to 2020 across China's natural terrestrial vegetation.We found that vegetation in forest-dominated regions was more resilient than in grassland-dominated regions,with divergent associations to climate,vegetation,soil,topography,and human drivers.Specifically,forest resilience was primarily associated with climate variables,with path analysis indicating a potential indirect link through vegetation characteristics.By contrast,grassland resilience was strongly related to soil and topographic properties.Particularly,among the climate variables examined,long-term warm drought condition,represented by the negative of Standardized Precipitation Evapotranspiration Index(SPEI),generally showed spatial negative associations with resilience,especially in temperate steppe and subtropical forest regions.Notably,the resilience of cold forests and high-altitude grasslands showed weaker or even positive relationship with warm droughts.These findings highlight the threats posed by ongoing climate change to vegetation resilience,and emphasize the need for ecosystem-specific strategies to enhance resilience in the future. 展开更多
关键词 Ecological resilience Temporal autocorrelation Warm droughts Remote sensing Vegetation index
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A basin-scale water budget calibration method for sustainable water management:A case study in the Loess Plateau,China 认领 引用
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作者 Zonghan Ma Bingfang Wu +7 位作者 Nana Yan Weiwei Zhu Mengxiao Li Hongwei Zeng Yixuan Wang Peilin Song Qiquan Yang Qingcheng Pan 《Geography and Sustainability》 CSCD 2026年第1期166-176,共11页
Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities.Although error reduction methods for individual water balance com... Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities.Although error reduction methods for individual water balance components have advanced,persistent biases remain due to the independent development of datasets,impacting basin scale water budget balance.In this research,we analyzed the mathematical origin of the bias between water budget components and developed a new basin-scale water balance calibration method that redistributes errors across components while enforcing water balance constraints.Validation confirms systematic improvements,with reduced RMSE(Precipitation:-2.29 mm/month;ET:-1.34 mm/month)and increased R2 against in situ observations.Applied to the Jinghe River Basin(2000−2019),the calibrated data reveal declining precipitation(-1.70 mm/year)and evapotranspiration(-1.84 mm/year)alongside slightly increasing runoff(0.20 mm/year in basin depth),signaling a drying trend.Land cover changes—marked by cropland loss(-3,497 km2)and forest(+720 km2)and grassland(+2,776 km2)expansion—reflect improved water consumption requirements by ecosystem,raising concerns for water retention and ecosystem stability.The method is particularly effective for ungauged basins with sparse ground data and underscores the need for integrated land-water management to enhance long-term resilience. 展开更多
关键词 Basin water balance Revegetation Loess plateau Evapotranspiration
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基于GF-6影像与迁移学习的跨区域梯田提取研究 认领 引用
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作者 黎一帆 吴炳方 +4 位作者 赵旦 田富有 常晟伟 覃星力 张淼 《遥感技术与应用》 CSCD 北大核心 2026年第2期348-360,共13页
梯田是丘陵和山区常见的农业景观和有效土地利用方式,有助于提高农业生产的产量和质量。迁移学习技术能够实现已有样本、模型、知识的最大程度复用,但是其在梯田提取中的性能研究较少。本研究选取UNet、HRNet、SwinUNet、TransUNet、Seg... 梯田是丘陵和山区常见的农业景观和有效土地利用方式,有助于提高农业生产的产量和质量。迁移学习技术能够实现已有样本、模型、知识的最大程度复用,但是其在梯田提取中的性能研究较少。本研究选取UNet、HRNet、SwinUNet、TransUNet、Segmenter共5种典型高性能深度学习框架,基于由GF-6卫星获取的2 m高空间分辨率的遥感影像开展迁移学习跨区域梯田监测分析。结果表明:UNet在梯田空间特征提取任务中表现最为优异,在目标域总体精度为81.67%,平均精度为81.97%;2020年,散渡河流域梯田总面积为1 274 km2,占耕地比例61.8%。借助迁移学习,依托预训练模型获取梯田分布信息,既能有效降低人力、物力成本,为跨区域梯田监测分析提供理论依据,又能为大规模遥感监测、土地利用规划等相关应用奠定基础。 展开更多
关键词 梯田制图 卫星影像 深度学习 迁移学习 微调技术
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Cloud services with big data provide a solution for monitoring and tracking sustainable development goals 认领 引用 被引量：16
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作者 Bingfang Wu Fuyou Tian +2 位作者 Miao Zhang Hongwei Zeng Yuan Zeng 《Geography and Sustainability》 2020年第1期25-32,共8页
To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of polici... To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence. 展开更多
关键词 Big data Cloud services SDGs Monitoring
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Detecting the linkage between arable land use and poverty using machine learning methods at global perspective 认领 引用 被引量：6
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作者 Fuyou Tian Bingfang Wu +3 位作者 Hongwei Zeng Gary R Watmough Miao Zhang Yurui Li 《Geography and Sustainability》 CSCD 2022年第1期7-20,共14页
Eradicating extreme poverty is one of the UN’s primary sustainable development goals(SDG).Arable land is related to eradicating poverty(SDG1)and hunger(SDG2).However,the linkage between arable land use and poverty re... Eradicating extreme poverty is one of the UN’s primary sustainable development goals(SDG).Arable land is related to eradicating poverty(SDG1)and hunger(SDG2).However,the linkage between arable land use and poverty reduction is ambiguous and has seldom been investigated globally.Six indicators of agricultural inputs,crop intensification and extensification were used to explore the relationship between arable land use and poverty.Non-parametric machine learning methods were used to analyze the linkage between agriculture and poverty at the global scale,including the classification and regression tree(CART)and random forest models.We found that the yield gap,fertilizer consumption and potential cropland ratio in protected areas correlated with poverty.Developing countries usually had a ratio of actual to potential yield less than 0.33 and fertilizer consumption less than 7.31 kg/ha.Overall,crop extensification,intensification and agricultural inputs were related to poverty at the global level. 展开更多
关键词 Arable land use Poverty Machine learning Yield gap Random forest
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Coordinating red line policies with water consumption caps in China’s drylands 认领 引用 被引量：3
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作者 Bingfang Wu Zonghan Ma +5 位作者 Xiaoming Feng Hongwei Zeng Si Gou Haitao Li Jianping Huang Bojie Fu 《Science Bulletin》 SCIE EI CAS CSCD 2025年第13期2039-2042,共4页
China’s rapid economic growth over the past four decades has come at the cost of the environment,with increasing challenges related to natural resource depletion,in addition to genuine water scarcity in nearly one-th... China’s rapid economic growth over the past four decades has come at the cost of the environment,with increasing challenges related to natural resource depletion,in addition to genuine water scarcity in nearly one-third of the country[1].As a result of extensive irrigation development,more than 50%of cropland is irrigated[2],and upland agricultural production is facilitated by rain harvesting,resulting in less water being available downstream. 展开更多
关键词 water scarcity natural resource depletionin rain harvestingresulting red line policies upland agricultural production economic growth irrigation developmentmore drylands
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Increasing Cropland Area and Its Associated Human-Induced Water Consumption Put Ebinur Lake at Risk of Drying Up 认领 引用 被引量：1
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作者 Hongwei Zeng Yan Xie +4 位作者 Abdelrazek Elnashar Shuai Wang Hang Zhao Junbin Li Bingfang Wu 《Journal of Remote Sensing》 EI CSCD 2025年第1期756-773,共18页
Quantifying the impact of agriculture on tail-end lakes is key to the sustainable development of mountain-oasis-desert ecosystems.This study integrated remote sensing and machine learning to explore the linkages betwe... Quantifying the impact of agriculture on tail-end lakes is key to the sustainable development of mountain-oasis-desert ecosystems.This study integrated remote sensing and machine learning to explore the linkages between the cropland in the oasis and water dynamics of the tail-end lake in the Ebinur Lake Basin.A novel data-driven model is proposed to decouple cropland evapotranspiration(ET)into natural(ETn)and human-induced(ETh)components,which helps to quantify the contributions of climate change and human activities to cropland ET.The model performed well with R2 values ranging from 0.88 to 0.96 from 2003 to 2019.Ebinur Lake experienced a“shrinkage-expansion”change from 2003 to 2019.Between 2003 and 2015,the surface water area(SWA)decreased from 785.91 to 478.83 km2 with an accumulated water storage deficit of 0.32 km3.After 2015,SWA rebounded to over 700 km2 and the accumulated water storage deficit was almost eliminated due to increased rainfall and external water transfer.Concurrently,the oasis cropland expanded from 2,871.2 km2 in 2003 to 4,325.7 km2 in 2019.The expansion of the cropland and the increased cropland ETh after 2013 raised the total water consumption of the cropland from 1.37 km3 in 2003 to 2.21 km3 in 2019.The contribution of ETh increased from 67%in 2013 to 77%in 2019,but the share of ETn decreased from 33%to 23%.To restore the SWA of Ebinur Lake to its ideal 800 km2,an additional water of 0.29 km3 would be required.Alarmingly,Ebinur Lake has resumed rapid shrinkage since 2020,and urgent action is needed to prevent further degradation. 展开更多
关键词 sustainable development remote sensing cropland machine learning cropland evapotranspiration et water dynamics evapotranspiration quantify contributions climate change human ac
Improved Soybean Mapping with Spectral Gaussian Mixture Modeling 认领 引用
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作者 Guilong Xiao Kaiqi Du +6 位作者 Shuangxi Miao Xuecao Li Anne Gobin Tiecheng Bai Miao Zhang Bingfang Wu Jianxi Huang 《Journal of Remote Sensing》 EI CSCD 2025年第1期698-716,共19页
Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driv... Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driven soybean mapping algorithms often rely on extensive,representative datasets,which can limit their applicability across different regions and periods.In contrast,existing sample-free soybean mapping methods have yet to fully exploit key physiological traits,such as chlorophyll content,canopy greenness,and water content,that are essential for distinguishing soybeans from other crops,particularly during peak growth stages when many crops share similar spectral characteristics.To address these limitations,this study introduces an innovative approach:the spectral Gaussian mixture model(SGMM)for globalscale soybean mapping.Specifically,the SGMM develops a novel Bhattacharyya coefficient weighting method to optimize spectral probabilistic separability between soybeans and other crops.Moreover,it identifies an accurate soybean mapping timeframe,named the optimal time window,to refine spectral feature extraction across varying environmental conditions and crop calendars.Unlike previous methods that rely on fixed thresholds or a limited set of spectral indices,our SGMM offers a probabilistic mapping framework that dynamically adapts to regional variations in soybean growth.The SGMM was validated across multiple soybean-producing regions,showing high accuracy with average overall accuracies of 0.875 in China,0.907 in the United States,0.895 in Argentina,and 0.884 in Brazil.Furthermore,the provincial-level estimates of soybean areas correlated strongly with official statistics,highlighting the model’s reliability and scalability for global soybean mapping.By leveraging key physiological insights and optimizing spectral feature extraction,the SGMM provides an efficient,scalable solution for global agricultural monitoring and can serve as a reference for mapping other crops. 展开更多
关键词 Spectral Gaussian Mixture Model Chlorophyll Content Soybean Mapping Bhattacharyya Coefficient Physiological Traits Water Content Canopy Greenness optimizing production
Remote sensing-based global crop monitoring: experiences with China’s CropWatch system 认领 引用 被引量：44
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作者 Bingfang Wu Jihua Meng +3 位作者 Qiangzi Li Nana Yan Xin Du Miao Zhang 《International Journal of Digital Earth》 SCIE EI 2014年第2期113-137,共25页
Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)us... Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems. 展开更多
关键词 CropWatch crop monitoring remote sensing crop production
Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation 认领 引用 被引量：20
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作者 Jihua Meng Xin Du Bingfang Wu 《International Journal of Digital Earth》 SCIE EI 2013年第3期203-218,共16页
While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contr... While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R2 of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail. 展开更多
关键词 high spatial and temporal resolution NDVI crop biomass Digital Earth data fusion
Overlooked uneven progress across sustainable development goals at the global scale: Challenges and opportunities 认领 引用 被引量：8
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作者 Yali Liu Jianqing Du +12 位作者 Yanfen Wang Xiaoyong Cui Jichang Dong Pan Gu Yanbin Hao Kai Xue Hongbo Duan Anquan Xia Yi Hu Zhi Dong Bingfang Wu Jüurgen P.Kropp Bojie Fu 《The Innovation》 EI 2024年第2期42-50,共9页
Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences w... Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences would influence global sustainable development has been long neglected.Here,we present the first quantitative assessment of SDGs’progress differences globally by adopting the SDGs progress evenness index.Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because(1)it is strongly associated with many public health risks(e.g.,air pollution),social inequalities(e.g.,gender inequality,modern slavery,wealth gap),and a reduction in life expectancy;(2)it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems,increasing the challenges related to biodiversity conservation;(3)most countries with low average SDGs performance show lower progress evenness,which further hinders their fulfillment of SDGs;and(4)many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness,which is partly ascribed to the antagonism between climate actions and other goals.These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors,caution must be exercised to avoid new problems from intensifying uneven progress across goals.Moreover,our study reveals that the urgent needs regarding SDGs of different regions seem complementary,emphasizing that regional collaborations(e.g.,demand-oriented carbon trading between SDGs poorly performed and well-performed countries)may promote sustainable development achievements at the global scale. 展开更多
关键词 globally priori inequality
Enhancing China’s Three Red Lines strategy with water consumption limitations 认领 引用 被引量：5
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作者 Bingfang Wu Hongwei Zeng +2 位作者 Weiwei Zhu Nana Yan Zonghan Ma 《Science Bulletin》 SCIE EI CAS CSCD 2021年第20期2057-2060,共4页
Many countries face problems of scarcity and unsustainability in the water sector[1].In China,overexploitation of water has resulted in rapidly declining aquifers in important grainproducing areas,greatly reduced rive... Many countries face problems of scarcity and unsustainability in the water sector[1].In China,overexploitation of water has resulted in rapidly declining aquifers in important grainproducing areas,greatly reduced river flows,and often severe river pollution[2,3]. 展开更多
关键词 水资源消耗 pollution river
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Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing,crowdsourced,and Auxiliary Datasets 认领 引用 被引量：4
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作者 Mohsen Nabil Miao Zhang +2 位作者 Bingfang Wu Jose Bofana Abdelrazek Elnashar 《Big Earth Data》 EI 2022年第1期54-76,共23页
Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote... Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R2=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section. 展开更多
关键词 Cropland mapping synergy mapping land cover accuracy assessment multisource data fusion
Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia 认领 引用 被引量：3
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作者 Awetahegn Niguse Beyene Hongwei Zeng +4 位作者 Bingfang Wu Liang Zhu Tesfay Gebretsadkan Gebremicael Miao Zhang Temesgen Bezabh 《Big Earth Data》 EI 2022年第1期18-35,共18页
Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.Remote sensing observations,particularly the leaf area index(LAI),have a stron... Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.Remote sensing observations,particularly the leaf area index(LAI),have a strong relationship with crop yield.This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies(WOFOST)model.To improve the estimation of crop yield in the region,the Ensemble Kalman Filter(EnKF)was used to incorporate the LAI into the WOFOST model.The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season.Our findings indicated that wheat yield was more precisely estimated by WOFOST(at water-limited mode)with EnKF algorithm(R2=0.80 and RMSE=413 kg ha−1)compared to that of without assimilating remotely sensed LAI(R2=0.58,RMSE=592 kg ha−1).These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation.The findings from this study can provide information to policy,decision-makers,and other similar sectors to implement an appropriate and timely yield estimation measure. 展开更多
关键词 Wheat yield WOFOST model Ensemble Kalman Filter remote sensing
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