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SSRN 2025年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Department of Geography Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Geography South China Normal University Shipai Campus Guangzhou510631 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Study region: The Tibetan Plateau in China, the Third Pole of the *** focus: Accurate descriptions of spatial variation in evapotranspiration (ET) and its components on the Tibetan Plateau (TP) are crucial for understanding the dynamics of the regional water cycle and energy balance. However, how various environmental factors affect ET and its components remains unclear. This study addresses this gap using observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and underlying *** hydrological insights for the region: The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory model performance with an average R² of 0.75. The ratio of transpiration to ET was generally higher in alpine meadows (0.55) than alpine steppes (0.39). Across the TP, precipitation and the leaf area index together shaped spatial variation in ET and transpiration. Precipitation emerged as the primary driver of interannual variation in ET, while the leaf area index strongly regulated the proportion of ET accounted for by transpiration. Furthermore, above aridity index values of 0.73, grassland ecosystems transitioned from water to energy. This finding is significant as it challenges the common application of an aridity index value of 0.65 to distinguish arid from humid regions. It suggests that TP grasslands, with an AI of 0.65-0.73, remain primarily water-limited. © 2025, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

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The Innovation 2021年第4期2卷 27-34页

作者： Xueqin Yang Jianping Wu Xiuzhi Chen Philippe Ciais Fabienne Maignan Wenping Yuan Shilong Piao Song Yang Fanxi Gong Yongxian Su Yuhang Dai Liyang Liu Haicheng Zhang Damien Bonal Hui Liu Guixing Chen Haibo Lu Shengbiao Wu Lei Fan Pierre Gentine SJoseph Wright Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies School of Atmospheric SciencesSun Yat-sen University&Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Laboratoire des Sciences du Climat et de l’Environnement IPSLCEA-CNRS-UVSQUniversite Paris-Saclay91191 Gif sur YvetteFrance Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China College of Earth Sciences Chengdu University of TechnologyChengdu 610000China College of Forestry and Landscape Architecture South China Agricultural UniversityGuangzhou 510000China Department of Geoscience Environment&SocietyUniversite Libre de BruxellesBruxellesBelgium INRA UMR“Ecologie et Ecophysiologie Forestieres”Universite de Lorraine-INRA54280 ChampenouxFrance South China Botanical Garden Chinese Academy of SciencesGuangzhou 510650China School of Biological Sciences The University of Hong KongPokfulamHong Kong Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China Department of Earth&Environmental Engineering Columbia UniversityNew YorkNY 10027USA Smithsonian Tropical Research Institute Apartado 0843-03092BalboaPanama

Relationships among productivity,leaf phenology,and seasonal variation in moisture and light availab.lity are poorly understood for evergreen broadleaved tropical/subtropical forests,which contribute 25% of terrestrial *** the one hand,as moisture availab.lity declines,trees shed leaves to reduce transpiration and the risk of hydraulic *** the other hand,increases in light availab.lity promote the replacement of senescent leaves to increase ***,we provide a comprehensive framework that relates the seasonality of climate,leaf abscission,and leaf productivity across the evergreen broadleaved tropical/subtropical forest *** seasonal correlation between rainfall and light availab.lity varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 *** rainfall and light covary positively,litterfall and productivity also covary positively and are always greater in the wetter sunnier *** rainfall and light covary negatively,litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate;otherwise productivity is smaller in the drier sunnier *** framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

关键词： tropical forest leaf abscission and productivity plant adaptive strategy climate and phenology regime climatic driver

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Aerosol Optical Depth Estimate Using Ground-Measured Spectral Skylight Ratio Method

Aerosol Optical Depth Estimate Using Ground-Measured Spectra...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Jing Nie Huazhong Ren Hui Zeng Jiaji Dong Jinxin Guo Yitong Zheng Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China Geographic Information System Technology Innovation Center of the Ministry of Natural Resources Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Aerosol Optical Depth (AOD) is an important physical quantity of atmospheric turbidity and a key factor for the atmospheric correction of optical remote sensing image. Several approaches have been developed to retrieve AOD from remote sensing data, and at ground level the sunphotometer is usually used to measure AOD. However, the sunphotometer is expensive and not portable for field campaign, which make it difficult to synchronously obtain AOD data along with other relevant measurement. In order to overcome this problem, this study proposed a new way to estimate AOD using Ground-measured Spectral Skylight Ratio method (GSSR) on the basis that skylight ratio is highly determined by such parameter. In the GSSR method, a look-up table (LUT) containing spectral skylight ratio in 400-1000nm was first established from the MODTRAN code under various AOD levels, solar zenith angles (SZA), and atmospheric types. Based on the LUT and actual skylight ratio measured from a ground spectrometer, the AOD was then determined using a shortest distance between the measurement and the spectral values in the LUT. Finally, the AOD derived from the GSSR method was validated using the AOD data from the CE318 sunphotometer data, and it was found that AOD error was about 0.0185, which demonstrated that the GSSR method can be used to estimate AOD accurately, and can be regarded as an alternative method to estimate AOD in the field work if no sunphotometer is availab.e.

关键词： Atmospheric measurements Table lookup Aerosols Standards remote sensing Optical variables measurement Optical sensors

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LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation

Computing Open

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Computing Open 2024年第2期computing-open卷

作者： Guanghong Deng Bing Yu Wenlong Jing Yong Li Xiaodan Zhao GuangDong Engineering Technology Research Center of UAV Remote Sensing Network Guangzhou iMapCloud Intelligent Technology Co. Ltd. Guangzhou 510095 P. R. China Guangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 P. R. China Foshan iMapCloud Intelligent Technology Co. Ltd. Foshan 528251 P. R. China

Medical image segmentation has always been a challenging task. This paper proposes a new LUneXt medical image segmentation model based on the characteristics analysis of medical image data sets and testing of different nonlinear activation units. Both normalized activations for the original negative input image activation have good optimization capabilities for the tokenization module parameters proposed in the original UneXt model. Using different activation coefficients for different foreground and background areas has achieved better results. The experimental results of this paper on the Breast Ultrasound Images (BUSI) data set reached an intersection over union (IoU) value of 62.64%, a Dice value of 76.12%, and a single inference speed of 807.57 ms. The experimental IoU value of the International Skin Imaging Collab.ration (ISIC 2018) data set reached 82.95%, and the Dice value reached 90.50%. The single inference speed reached 842.58 ms. The LUneXt model is more robust than other models. While improving model performance, it does not introduce higher computational complexity and does not have a major impact on the processing speed of a single image.

关键词： Image segmentation medical images neural networks activation function

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Design of Command and Dispatch system for Automatic Reading of Meter Images in Substations by UAV Inspection Photos

Computing Open

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Computing Open 2024年第2期computing-open卷

作者： Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei GuangDong Engineering Technology Research Center of UAV Remote Sensing Network Guangzhou iMapCloud Intelligent Technology Co. Ltd. Guangzhou 510095 P. R. China Guangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou 510070 P. R. China Foshan iMapCloud Intelligent Technology Co. Ltd. Foshan 528251 P. R. China

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplab.3 + image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplab.3 + algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1 ms and 13.2 ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7 ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7 ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly im

关键词： Object detection image segmentation YOLOv6n Deeplab.3 + meter reading

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration

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National Science Review 2021年第2期8卷 60-73页

作者： Philippe Ciais Yitong Yao Thomas Gasser Alessandro Baccini Yilong Wang Ronny Lauerwald Shushi Peng Ana Bastos Wei Li Peter A.Raymond Josep G.Canadell Glen P.Peters Rob J.Andres Jinfeng Chang Chao Yue A.Johannes Dolman Vanessa Haverd Jens Hartmann Goulven Laruelle Alexandra G.Konings Anthony W.King Yi Liu Sebastiaan Luyssaert Fabienne Maignan Prabir K.Patra Anna Peregon Pierre Regnier Julia Pongratz Benjamin Poulter Anatoly Shvidenko Riccardo Valentini Rong Wang Grégoire Broquet Yi Yin Jakob Zscheischler Bertrand Guenet Daniel S.Goll Ashley-P.Ballantyne Hui Yang Chunjing Qiu Dan Zhu State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F University CSIRO Oceans and Atmosphere 15. Institute for Geology CEN—Center for Earth System Research and SustainabilityUniversity of Hamburg 16. Department of Earth System ScienceStanford University 17. Department of Earth System ScienceStanford UniversityStanford 18. School of Geographical SciencesNanjing University of Information Science and Technology 19. Department of Ecological SciencesVrije Universiteit Amsterdam 20. Research Institute for Global ChangeJAMSTEC Laboratoire des Sciences du Climat et de l'Environnement CEA-CNRS-UVSQ-UPSACLAY Center for Environmental Remote Sensing Chiba University Institute of Soil Science and Agrochemistry Siberian Branch of the Russian Academy of Sciences (SB RAS) Tuva State University Republic of Tuva Max Planck Institute for Meteorology NASA Goddard Space Flight Center Biospheric Sciences Lab. 26. Department for Innovation in BiologicalAgro-food and Forest systems (DIBAF)University of Tuscia RUDN University Department of Environmental Science and Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and PreventionInstitute of Atmospheric SciencesFudan University 29. Division of Geological and Planetary SciencesCalifornia Institute of Technology 30. Climate and Environmental Physics and Oeschger Centre for Climate Change ResearchUniversity of Bern 31. Department of Ecosystem and Conservation Sci

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange(NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2uptake of –2.2 ± 0.6 Pg C yr-1consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2of 39 Pg C yr-1with an interquartile of 33–46 Pg C yr-1—a much smaller portion of net primary productivity than previously reported.

关键词： carbon budget human appropriation of ecosystems soil carbon

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Complex network simulation of forest network spatial pattern in Pearl River Delta

Complex network simulation of forest network spatial pattern...

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ISPRS Geospatial Week 2017

作者： Zeng, Yuhuai Guangzhou Institute of Geography Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangzhou510070 China

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. remote sensing and GIS spatial data are availab.e through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network's power-law distribution parameter value size;The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network's degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network's main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum. © Authors 2017. CC BY 4.0 License.

关键词： Ecology

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Near surface camera informed agricultural land monitoring for climate smart agriculture

Climate Smart Agriculture

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Climate Smart Agriculture 2024年第1期1卷

作者： Le Yu Zhenrong Du Xiyu Li Qiang Zhao Hui Wu Duoji weise Xinqun Yuan Yuanzheng Yang Wenhua Cai Weimin Song Pei Wang Zhicong Zhao Ying Long Yongguang Zhang Jinbang Peng Xiaoping Xin Fei Xu Miaogen Shen Hui Wang Yuanmei Jiao Yong Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing China Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing China School of Information and Communication Engineering Dalian University of Technology Dalian China Geothermal Geological Brigade Tibet Autonomous Region Geological and Mineral Exploration and Development Bureau Lhasa China International Department Beijing No. 35 High School Beijing China Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education Nanning Normal University Nanning China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning China Key Laboratory of Coastal Environmental Processes and Ecological Restoration Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China Yellow River Delta Field Observation and Research Station of Coastal Wetland Ecosystem Chinese Academy of Sciences Dongying China Department of Landscape Architecture School of Architecture Tsinghua University Beijing China Institute for National Parks Tsinghua University Beijing China School of Architecture and Hang Lung Center for Real Estate Key Laboratory of Eco Planning & Green Building Ministry of Education Tsinghua University Beijing China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Nanjing China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing Univer

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

关键词： Near surface camera (NSCam) Climate-smart agriculture (CSA) Agricultural monitoring Data fusion Sentinel-2

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