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Development of a novel critical nitrogen concentration-cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

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The Crop Journal 2024年第4期12卷 1242-1251页

作者： Tianyang Ye Yu Zhang Jingyan Xuan Xintian Wang Yang Li Junhao Xu Liujun Xiao Leilei Liu Liang Tang Weixing Cao Bing Liu Yan Zhu National Engineering and Technology center for information agriculture Engineering Research Center of Smart AgricultureMinistry of EducationKey Laboratory for Crop System Analysis and Decision MakingMinistry of AgricultureJiangsu Key Laboratory for Information AgricultureJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjing 210095JiangsuChina Institute of remote sensing and Digital agriculture Sichuan Academy of Agricultural SciencesChengdu 610066SichuanChina

Accurate nitrogen(N)nutrition diagnosis is essential for improving N use efficiency in crop *** widely used critical N(Nc)dilution curve traditionally depends solely on agronomic variables,neglecting crop water *** three-year field experiments with winter wheat,encompassing two irrigation levels(rainfed and irrigation at jointing and anthesis)and three N levels(0,180,and 270 kg ha1),this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration(T),providing a comprehensive analysis of the interaction between N and water *** Nc curves derived from both crop dry matter(DM)and T demonstrated N concentration dilution under different conditions with different *** equation Nc=6.43T0.24 established a consistent relationship across varying irrigation *** test results indicated that the nitrogen nutrition index(NNI),calculated from this curve,effectively identifies and quantifies the two sources of N deficiency:insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root ***,the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit(Nand)and a positive correlation with relative grain yield(RGY).The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY,as indicated by larger R2 values and smaller *** novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status,predicting grain yield,and optimizing N fertilizer management across varying irrigation *** findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

关键词： Crop dry matter Crop cumulative transpiration Bayesian statistical model Critical nitrogen dilution curve Nitrogen nutrition index

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Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

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International Journal of Agricultural and Biological Engineering 2024年第2期17卷 260-267页

作者： Xiaobin Xu Cong Teng Hongchun Zhu Haikuan Feng Yu Zhao Zhenhai Li College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina Key Laboratory of Quantitative remote sensing in Ministry of agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China

Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making,which are also important requirements for precise agricultural *** the development of hyperspectral imaging technology,a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral *** this study,the Unmanned Aerial Vehicle(UAV)hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were *** winter wheat yield prediction model was established by optimizing Vegetation Indices(VIs)feature scales and sample scales,incorporating Partial Least Squares Regression(PLSR),Random Forest algorithm(RF),and Back Propagation Neural Network algorithm(BPN).Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy(RMSE=948.88 kg/hm2).Contrary to the belief that more input features result in higher accuracy,PLSR,RF,and BPN models performed best when trained with the top 3,8,and 4 VIs with the highest correlation,*** an increase in training samples,model accuracy improves,reaching stability when the training samples reach *** PLSR and optimal feature scales,UAV yield prediction maps were generated,holding significant value for field management in precision agriculture.

关键词： hyperspectral imagery unmanned aerial vehicle winter wheat yield prediction model remote sensing

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Retrieval of high spatial resolution mountainous land surface temperature considering topographic and adjacency effects

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Science China Earth Sciences 2024年第11期67卷 3610-3625页

作者： Zhiwei HE Bohui TANG Zhaoliang LI Faculty of Land Resources Engineering Kunming University of Science and Technology Key Laboratory of Plateau remote sensing Department of Education of Yunnan Province State Key Lab of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences Key Laboratory of Agricultural remote sensing Ministry of Agriculture and Rural Affairs Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences

Land surface temperature(LST) is a key parameter reflecting the interaction between land and atmosphere. Currently,thermal infrared(TIR) quantitative remote sensing technology is the only means to obtain large-scale, high spatial resolution LST. Accurately retrieving high spatial resolution mountainous LST(MLST) plays an important role in the study of mountain climate change. The complex terrain and strong spatial heterogeneity in mountainous areas change the geometric relationship between the surface and satellite sensors, affecting the radiation received by the sensors, and rendering the assumption of planar parallelism invalid. In this study, considering the influence of complex terrain in mountainous areas on atmospheric downward radiation and the thermal radiation contribution of adjacent pixels, a mountainous TIR radiative transfer model based on the sky view factor was developed. Combining with the atmospheric radiative transfer model MODTRAN 5.2, a nonlinear generalized split-window algorithm suitable for high spatial resolution MLST retrieval was constructed and applied to Landsat-9 TIRS-2satellite TIR remote sensing data. The analysis results indicate that neglecting the topographic and adjacency effects would lead to significant discrepancies in LST retrieval, with simulated data showing LST differences of up to 2.5 K. Furthermore, due to the lack of measured MLST in the field, the MLST accuracy obtained by this retrieval method was indirectly validated using the currently recognized highest-accuracy forward 3D radiative transfer model DART. The MLST and emissivity were input into the DART model to simulate the brightness temperature at the top of the atmosphere(TOA) of Landsat-9 band 10, and compared with the brightness temperature at TOA of Landsat-9 band 10. The RMSE(Root Mean Square Error) for the two subregions was0.50 and 0.61 K, respectively, indicating that the method proposed can retrieve high-precision MLST.

关键词： Mountainous land surface temperature Topographic and adjacency effects Nonlinear generalized split-window algorithm Landsat-9 data DART model

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Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

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Journal of Integrative agriculture 2024年第8期23卷 2820-2841页

作者： Xianglin Zhang Jie Xue Songchao Chen Zhiqing Zhuo Zheng Wang Xueyao Chen Yi Xiao Zhou Shi Institute of Applied remote sensing and information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Department of Land Management Zhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation center Zhejiang UniversityHangzhou 311215China Institute of Digital agriculture Zhejiang Academy of Agricultural SciencesHangzhou 310021China Key Laboratory of Spectroscopy sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management *** a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different ***,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human *** overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in *** results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on *** the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to *** recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average *** the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final *** the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping

关键词： cropland soil organic matter digital soil mapping machine learning feature selection model averaging

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Late Triassic Intracontinental Deformation of South Tianshan,Central Asia:Evidence from Syn-tectonic Sedimentation and Detrital Zircon Provenances of the Kuqa Depression

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Acta Geologica Sinica(English Edition) 2024年第3期98卷 548-568页

作者： QIN Xiang CHEN Xuanhua SHAO Zhaogang ZHANG Yiping WANG Yongchao LI Bing SU He WANG Ye LIU Kui HAN Lele China Aero Geophysical Survey and remote sensing center for Natural Resources Beijing 100083China Key Laboratory of Airborne Geophysics and remote sensing Geology MNRBeijing 100083China Chinese Academy of Geological Sciences Beijing 100037China School of Earth Sciences and Resources China University of Geosciences(Beijing)Beijing 100083China

The Tianshan range,a Paleozoic orogenic belt in Central Asia,has undergone multiple phases of tectonic activities characterized by the N-S compression after the early Mesozoic,including the far-field effects of the Cenozoic Indian-Asian ***,there are limited reports on the tectonic deformation and initiation of Triassic intracontinental deformation in the Tianshan *** this structural context is crucial for interpreting the early intracontinental deformation history of the Eurasian continent during the early *** strata and syn-tectonic sediments provide a rich source of information on tectonic activities and have been extensively used in the studies of orogenic *** on detail fieldwork conducted in this study,the middle-late Triassic Kelamayi Formation of the northern Kuqa Depression in the southern Tianshan fold-thrust belt has been identified as the typical syn-tectonic growth *** youngest detrital zircon component in two lithic sandstone samples from the bottom and top of the Kelamayi growth strata yielded U-Pb ages of 223.4±3.1 and 215.5±2.9 Ma,respectively,indicating that the maximum depositional age of the bottom and top of the Kelamayi growth strata is 226-220 and 218-212 *** geochronological distribution of detrital samples from the Early-Middle Triassic and Late Triassic revealed abrupt changes,suggesting a new source supply resulting from tectonic activation in the Tianshan *** coupling relationship between the syn-tectonic sedimentation of the Kelamayi Formation and the South Tianshan fold-thrust system provides robust evidence that the Triassic intracontinental deformation of the South Tianshan range began at approximately 226-220 Ma(during the Late Triassic)and ended at approximately 218-212 *** findings provide crucial constraints for understanding the intraplate deformation in the Tianshan range during the Triassic.

关键词： growth strata basin-mountain coupling Triassic Indosinian event Kuqa Depression South Tianshan

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The non-stationary and spatially varying associations between hand,foot and mouth disease and multiple environmental factors:A Bayesian spatiotemporal mapping model study

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Infectious Disease Modelling 2024年第2期9卷 373-386页

作者： Li Shen Minghao Sun Mengna Wei Qingwu Hu Yao Bai Zhongjun Shao Kun Liu School of remote sensing and information Engineering Wuhan UniversityWuhanHubei ProvinceChina Department of Infection Disease Control and Prevention Xi'an Center for Disease Control and PreventionXi'anShaanxiChina Department of Epidemiology Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational EnvironmentSchool of Public HealthAir Force Medical UniversityXi'anShaanxi ProvinceChina

The transmission and prevalence of Hand,Foot and Mouth Disease(HFMD)are affected by a variety of natural and socio-economic environmental *** study aims to quantitatively investigate the non-stationary and spatially varying associations between various environmental factors and HFMD *** collected HFMD surveillance cases and a series of relevant environmental data from 2013 to 2021 in Xi'an,Northwest *** controlling the spatial and temporal mixture effects of HFMD,we constructed a Bayesian spatiotemporal mapping model and characterized the impacts of different driving factors into global linear,non-stationary and spatially varying *** results showed that the impact of meteorological conditions on HFMD risk varies in both type and magnitude above certain thresholds(temperature:30°C,precipitation:70 mm,solar radiation:13000 kJ/m^(2),pressure:945 hPa,humidity:69%).Air pollutants(PM_(2.5),PM_(10),NO_(2))showed an inverted U-shaped relationship with the risk of HFMD,while other air pollutants(O_(3),SO_(2))showed nonlinear ***,the driving effect of increasing temperature on HFMD was significant in the 3-year period,while the inhibitory effect of increasing precipitation appeared evident in the 5-year *** addition,the proportion of urban/suburban/rural area had a strong influence on HFMD,indicating that the incidence of HFMD firstly increased and then decreased during the rapid urbanization *** influence of population density on HFMD was not only limited by spatial location,but also varied between high and low *** road density inhibited the risk of HFMD,but higher night light index promoted the occurrence of *** findings further demonstrated that both ecological and socioeconomic environmental factors can pose multiple driving effects on increasing the spatiotemporal risk of HFMD,which is of great significance for effectively responding to the changes in HFMD epidemic outbreaks.

关键词： Hand,foot,and mouth disease(HFMD) Environmental factors Bayesian spatiotemporal mapping model Non-stationary effects Spatially varying effects

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Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient

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Journal of Integrative agriculture 2024年第4期23卷 1381-1392页

作者： Xuan Li Shaowen Wang Yifan Chen Danwen Zhang Shanshan Yang Jingwen Wang Jiahua Zhang Yun Bai Sha Zhang Research center for remote sensing and Digital Earth College of Computer Science and TechnologyQingdao UniversityQingdao266071China center for Geospatial information Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Geographical Sciences Hebei Normal UniversityShijiazhuang 050024China

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in ***,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat ***,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional ***-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter *** this study,Fr equations were fitted for each organ of winter wheat using site-scale *** equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),*** PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.

关键词： dry matter partition remote sensing model winter wheat yield North China Plain

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Long-term(2013-2022)mapping of winter wheat in the North China Plain using Landsat data:classification with optimal zoning strategy

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Big Earth Data 2024年第3期8卷 494-521页

作者： Yifei Liu Xuehong Chen Jin Chen Yunze Zang Jingyi Wang Miao Lu Liang Sun Qi Dong Bingwen Qiu Xiufang Zhu State Key Laboratory of remote sensing Science Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina Beijing Engineering Research center for Global Land remote sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina State Key Laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)BeijingChina State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina Key Laboratory of Spatial Data Mining&information Sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhouChina

security and formulating agricultural *** data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this *** various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse *** learning is an effective method for crop classification using Landsat ***,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this *** address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop ***,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification *** each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI *** sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different *** derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial *** results underscore the reliability of the produced annual winter wheat *** experiments demonstrate that

关键词： Winter wheat mapping Landsat machine learning North China Plain optimal zoning

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Toward Establishing a Low-cost UAV Coordinated Carbon Observation Network(LUCCN):First Integrated Campaign in China

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Advances in Atmospheric Sciences 2024年第1期41卷 1-7页

作者： Dongxu YANG Tonghui ZHAO Lu YAO Dong GUO Meng FAN Xiaoyu REN Mingge LI Kai WU Jing WANG Zhaonan CAI Sisi WANG Jiaxu GUO Liangfu CHEN Yi LIU Carbon Neutrality Research center(CNRC) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Middle Atmospheric Physics and Global Environment Observation(LAGEO) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China National remote sensing center of China Beijing 100036China The Administrative center for China’s Agenda 21 Beijing 100862China

In this study,we introduce our newly developed measurement-fed-perception self-adaption Low-cost UAV Coordinated Carbon Observation Network(LUCCN)*** LUCCN primarily consists of two categories of instruments,including ground-based and UAV-based in-situ *** use the GMP343,a low-cost non-dispersive infrared sensor,in both ground-based and UAV-based *** first integrated measurement campaign took place in Shenzhen,China,4 May *** the campaign,we found that LUCCN’s UAV component presented significant data-collecting advantages over its ground-based counterpart owing to the relatively high altitudes of the point emission sources,which was especially obvious at a gas power plant in *** emission flux was calculated by a crosssectional flux(CSF)method,the results of which differed from the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC).The CSF result was slightly larger than others because of the low sampling rate of the whole emission cross *** LUCCN system will be applied in future carbon monitoring campaigns to increase the spatiotemporal coverage of carbon emission information,especially in scenarios involving the detection of smaller-scale,rapidly varying sources and sinks.

关键词： CO_(2) measurement networks power station carbon emission cross-sectional flux method

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Optimal combination of the correction model and parameters for the precision geometric correction of UAV hyperspectral images

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 173-184页

作者： Wenzhong Tian Za Kan Qingzhan Zhao Ping Jiang Xuewen Wang Hanqing Liu College of Mechanical and Electrical Engineering Shihezi UniversityShihezi 832000XinjiangChina Research center for Space information Engineering Technology Shihezi 832000XinjiangChina XPCC Division of National remote sensing center of China Shihezi 832000XinjiangChina College of information Science and Technology Shihezi UniversityShihezi 832000XinjiangChina

Nowadays,with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology,people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral *** optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral *** this study,the geometric correction accuracy under various geometric correction models(including affine transformation model,local triangulation model,polynomial model,direct linear transformation model,and rational function model)and resampling methods(including nearest neighbor resampling method,bilinear interpolation resampling method,and cubic convolution resampling method)were ***,the distribution,number,and accuracy of control points were analyzed based on the control variable method,and precise ground control points(GCPs)were *** results showed that the average geometric positioning error of UAV hyperspectral images(at 80 m altitude AGL)without geometric correction was as high as 3.4041 m(about 65 pixels).The optimal geometric correction model and parameter combination of the UAV hyperspectral image(at 80 m altitude AGL)used a local triangulation model,adopted a bilinear interpolation resampling method,and selected 12 edgemiddle distributed *** correction accuracy could reach 0.0493 m(less than one pixel).This study provides a reference for the geometric correction of UAV hyperspectral images.

关键词： geometric correction hyperspectral images unmanned aerial vehicle(UAV) ground control point(GCP)

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