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Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network

Chinese Geographical Science 2019年第2期29卷 270-282页

作者： HUANG Yajie LI Zhen ye huichun ZHANG Shiwen ZHUO Zhiqing XING An HUANG Yuanfang Key Laboratory of Arable Land Conservation (North China) Ministry of Agriculture/Key Laboratory of Agricultural Land Quality Monitoring Ministry of Land and Resources College of Resources and Environmental Sciences China Agricultural University Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences School of Earth and Environment Anhui University of Science and Technology

Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.

关键词： ordinary kriging neural network soil electrical conductivity variability mapping Ningxia,China

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热效应作用下SiO_(2)光学微腔中的多孤子光场分析

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光学学报 2024年第10期44卷 385-392页

作者：徐昕叶回春焦晨金雪莹潘成亮陈东夏豪杰合肥工业大学仪器科学与光电工程学院测量理论与精密仪器安徽省重点实验室安徽合肥230009 中国科学技术大学精密机械与精密仪器系安徽合肥230027

光学微腔中热效应对腔内光场的演化有重要的影响。在Lugiato-Lefever方程的基础上,分析了热效应对SiO_(2)光学微腔中光场演化的影响。研究发现,在零失谐的初始条件下,热效应可以在腔内激发出多孤子光场,但由于热效应的加剧,该光场只能... 详细信息

光学微腔中热效应对腔内光场的演化有重要的影响。在Lugiato-Lefever方程的基础上,分析了热效应对SiO_(2)光学微腔中光场演化的影响。研究发现,在零失谐的初始条件下,热效应可以在腔内激发出多孤子光场,但由于热效应的加剧,该光场只能短暂存在。因此,当多孤子光场产生后,利用调谐泵浦波长和功率的方式来保持微腔内的多孤子。通过选择合适的泵浦功率和波长扫描速度,可有效补偿热效应引起的失谐,维持多孤子光场。此外,泵浦的参数也会对调谐后的光场产生不同的影响。改变泵浦功率可以改变调谐后多孤子光场中脉冲的数量,泵浦功率越高,则热效应越明显,调谐后的多孤子光场包含的脉冲数量也越多。而泵浦波长的调谐速度过小,会导致多孤子光场逐渐消失;泵浦波长扫描过快,会引起微腔工作于正失谐状态,从而导致原有的多孤子演化为混沌光场。

关键词：激光器光学微腔热效应多孤子光场调谐

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2018–2020年广西甘蔗10 m分辨率种植分布数据集

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中国科学数据（中英文网络版） 2024年第2期9卷 50-57页

作者：陈森政叶回春聂超甲雒培磊桂林电子科技大学计算机与信息安全学院广西桂林541004 中国科学院空天信息创新研究院数字地球重点实验室北京100094 中国-东盟地球大数据区域创新中心南宁530022

作物种植分布的提取是作物监测的基础,及时准确地提取出甘蔗种植分布对甘蔗监测及种植结构调整具有重要意义。本文基于Sentinel-1和Sentinel-2数据,采用主被动遥感协同方法和决策树分类方法,开展了广西甘蔗种植分布提取方法研究,并结合... 详细信息

作物种植分布的提取是作物监测的基础,及时准确地提取出甘蔗种植分布对甘蔗监测及种植结构调整具有重要意义。本文基于Sentinel-1和Sentinel-2数据,采用主被动遥感协同方法和决策树分类方法,开展了广西甘蔗种植分布提取方法研究,并结合多年调查的地面样本数据进行验证,在此基础上提取了2018–2020年广西甘蔗种植分布。该方法利用遥感数据提取得到的2018年广西甘蔗种植分布总体精度达92%,Kappa系数为0.85;2019年广西甘蔗种植分布总体精度达94%,Kappa系数为0.88;2020年广西甘蔗种植分布总体精度达94%,Kappa系数为0.88。本数据集可作为广西甘蔗时空变化分析的基础数据,也可为广西甘蔗生产管理与种植结构优化调整等提供基础数据支撑。

关键词：甘蔗 Sentinel-2数据主被动遥感协同决策树分类广西

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香蕉枯萎病无人机多光谱影像数据集

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中国科学数据（中英文网络版） 2024年第2期9卷 58-65页

作者：叶回春陈森政郭安廷聂超甲王晶晶中国科学院空天信息创新研究院中国科学院数字地球重点实验室北京100094 中国-东盟地球大数据区域创新中心南宁530022 可持续发展大数据国际研究中心北京100094 桂林电子科技大学计算机与信息安全学院广西桂林541004 海南大学林学院海口570228

香蕉枯萎病被称为香蕉“癌症”,目前已严重威胁着全球香蕉生产。及时精准地识别香蕉枯萎病植株对疫株除治及农业种植结构优化调整具有重要意义。为探究香蕉枯萎病的无人机遥感识别方法,本研究在广西隆安县的一个香蕉种植农场,选择有香... 详细信息

香蕉枯萎病被称为香蕉“癌症”,目前已严重威胁着全球香蕉生产。及时精准地识别香蕉枯萎病植株对疫株除治及农业种植结构优化调整具有重要意义。为探究香蕉枯萎病的无人机遥感识别方法,本研究在广西隆安县的一个香蕉种植农场,选择有香蕉枯萎病发生的区域,获取了香蕉枯萎病遥感综合实验数据,经过整理和预处理制作了本数据集。本数据集包含了无人机多光谱反射率数据和香蕉枯萎病发生状况地面样点调查数据,无人机多光谱影像数据采集时天气晴朗、无云且光照充足,飞行高度120 m,空间分辨率8 cm/pixel;香蕉枯萎病发生状况样点调查采用过高精度手持GPS单元进行样点经纬度信息采集,并在室内将地面调查样点位置与无人机影像上对应香蕉植株的中心位置进行匹配。本数据集的无人机多光谱影像数据空间分辨率高,地面样点调查数据准确,可应用于香蕉枯萎病无人机遥感识别与监测相关研究,对促进农作物病害遥感监测研究及应用具有重要意义。

关键词：枯萎病病害香蕉无人机多光谱遥感

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Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

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Big Earth Data 2022年第4期6卷 580-602页

作者： Naichen Xing Wenjiang Huang huichun ye Yingying Dong Weiping Kong Yu Ren Qiaoyun Xie Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Earth Observation Aerospace Information Research InstituteChinese Academy of SciencesSanyaChina Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Technology Sydney SydneyAustralia

Leaf area index(LAI)and canopy chlorophyll density(CCD)are key indicators of crop growth status.In this study,we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages.The indices were calculated with Sentinel-2 MSI data and hyperspectral data.Their performances were validated against ground measurements using R2,RMSE,and bias.The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling stage.Furthermore,rededge modified indices outperformed the traditional indices for both data genres.Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early joint-ing and milk development stage,while indices with long red-edge wavelength estimate the sought variables better at the middle three stages.The results were consistent with the red-edge inflec-tion point shift at different growth stages.The best indices for Sentinel-2 LAI retrieval,Sentinel-2 CCD retrieval,hyperspectral LAI retrieval,and hyperspectral CCD retrieval at five growth stages were determined in the research.These results are beneficial to crop trait monitoring by providing references for crop biophysical and bio-chemical parameters retrieval.

关键词： Growth stages hyperspectral red-edge band Sentinel-2 vegetation index

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Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 145-152页

作者： Wenjiang Huang Junjing Lu huichun ye Weiping Kong A.Hugh Mortimer Yue Shi State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Institute of Geographical Science Hebei Academy of SciencesShijiazhuang 050021China Hainan Provincial Key Laboratory of Earth Observation Sanya 572029HainanChina Rutherford Appleton Laboratory Harwell Science and Innovations CampusOxfordshire OX110QXUK

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2

关键词： winter wheat crop disease powdery mildew stripe rust nitrogen-water stress continuous wavelet analysis quantitative identification

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A new spectral index for the quantitative identification of yellow rust using fungal spore information

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Big Earth Data 2021年第2期5卷 201-216页

作者： Yu Ren huichun ye Wenjiang Huang Huiqin Ma Anting Guo Chao Ruan Linyi Liu Binxiang Qian Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Hainan Key Laboratory of Earth Observation Hainan Institute of Aerospace InformationChinese Academy of SciencesSanyaChina

yellow rust(Puccinia striiformis f.sp.Tritici)is a frequently occurring fungal disease of winter wheat(Triticum aestivum L.).During yellow rust infestation,fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins.We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index(YRSI).The estimation accuracy and stability were evaluated using two years of leaf spectral data,and the results were compared with eight indices commonly used for yellow rust detection.The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data(R^(2)=0.710,RMSE=0.097)and outperformed the published indices(R^(2)=0.587,RMSE=0.120)for the validation using the 2002 spectral data.The random forest(RF),k-nearest neighbor(KNN),and support vector machine(SVM)algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested,healthy,and aphid–infested wheat spectral data.The YRSI provided the best performance.

关键词： yellow rust spectral index fungal spores quantitative identification hyperspectral remote sensing winter wheat

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Simple assessment of farmland soil phosphorus loss risk at county scale with high landscape heterogeneity

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International Journal of Agricultural and Biological Engineering 2021年第2期14卷 126-134页

作者： Huirong Ning Qimeng Liu Shiwen Zhang huichun ye Qiang Shen Wentao Zhang Zhen Li People’s Government of Xianshengdian Township Lu’an 237005AnhuiChina School of Earth and Environment Anhui University of Science and TechnologyHuainan 232001AnhuiChina Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Earth Sciences and Engineering Hohai UniversityNanjing 210098China College of Land Science and Technology China Agricultural UniversityBeijing 100193China

In order to improve the existing phosphorus index assessment methods,using the interactive evaluation index(IEI)as an auxiliary variable,the geographically weighted regression(GWR)was adopted as prediction means.A method of regional soil phosphorus risk assessment was constructed by modifying phosphorus index model(MPIM).The GWR-IEI method more accurately predicted available phosphorus(AP)and soil organic matter(SOM),and the prediction precision and goodness of fit were high.Compared with the ordinary least square(OLS)method,the relative improvement of the root mean squared errors(RMSE)with the GWR-IEI method reached 28.95%for available phosphorus predicted,while that of SOM was 21.24%.The phosphorus loss risk of most of the study area(95.29%)was moderate to low.The areas featuring an extremely high phosphorus loss accounted for merely 0.33%of the total research area.Phosphorus loss depends on the effects of many factors.Areas which have strong source or transfer factors are not necessarily high-risk areas for phosphorus loss.Only the co-occurrence of transfer and source factors leads to high risk and greater potential for phosphorus loss.The GWR-IEI-MPIM method accurately reflected the degree of risk for phosphorus at the regional scale,which provides a valuable reference for risk assessment of phosphorus.

关键词： high landscape heterogeneity GWR county scale simplified assessment

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Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 136-142,I0001页

作者： huichun ye Wenjiang Huang Shanyu Huang Bei Cui Yingying Dong Anting Guo Yu Ren Yu Jin Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Key Laboratory of Earth Observation Hainan ProvinceSanya 572029China Chinese Academy of Agricultural Engineering Planning&Design Beijing 100125China University of Chinese Academy of Sciences Beijing 100049China School of Electronics and Information Engineering Anhui UniversityHefei 230601China

The disease of banana Fusarium wilt currently threatens banana production areas all over the world.Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments.The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine(SVM),random forest(RF),and artificial neural network(ANN)algorithms to identify locations that were infested or not infested with Fusarium wilt.An unmanned aerial vehicle(UAV)equipped with a five-band multi-spectral sensor(blue,green,red,red-edge and near-infrared bands)was used to capture the multi-spectral imagery.A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt.The results showed that the SVM,RF,and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery.The overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based approach.The RF algorithm required significantly less training time than the SVM and ANN algorithms.The maps generated by the SVM,RF,and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2,accounting for 36.3%-40.1%of the total planting area of bananas in the study area.The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%.A simulation of the resolutions of satellite-based imagery(i.e.,0.5 m,1 m,2 m,and 5 m resolutions)showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt.The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery.The results provide guidance for disease treatment and crop planting adjustments.

关键词： banana fusarium wilt UAV-based multi-spectral remote sensing support vector machine artificial neural network random forest

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工程实践类课程中课程思政教学实践探索

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实验室研究与探索 2023年第4期42卷 246-249页

作者：叶回春张世武倪向贵中国科学技术大学精密机械与精密仪器系合肥230027 中国科学技术大学工程科学实验教学中心合肥230027

工程实践类课程是培养学生工程素养和创新能力的主要平台,同时也是培育学生具备劳动意识、科学精神、工匠精神、爱国主义情操的重要载体。以中国科学技术大学为例,通过在实践课程中巧妙融入思政内容,强调关键知识点与课程思政元素的紧... 详细信息

工程实践类课程是培养学生工程素养和创新能力的主要平台,同时也是培育学生具备劳动意识、科学精神、工匠精神、爱国主义情操的重要载体。以中国科学技术大学为例,通过在实践课程中巧妙融入思政内容,强调关键知识点与课程思政元素的紧密结合,让学生主动领会、内化于心,真正实现“立德树人”润物无声。同时,在教学形式上,打破以知识传授为主要特征的传统课堂教学模式,采取小组讨论、相互协作的教学模式,配合智慧教学工具,线上线下相融合,强调理论知识与实践动手相结合,突出对学生实践能力、创新意识的综合训练,注重实践过程中学生德行品质的塑造与培养,最终实现工程实践类课程思政育人、弘扬劳动精神。

关键词：工程实践课程思政线上线下混合教学

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