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Hyperspectral diagnosis of nitrogen status in arbuscular mycorrhizal inoculated soybean leaves under three drought conditions

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 126-131页

作者： Yinli Bi Weiping Kong wenjiang huang State Key Laboratory of Coal Resources and Safe Mining China University of Mining and Technology(Beijing)Beijing 100083China Key Laboratory of Quantitative Remote Sensing Information Technology Academy of Opto-ElectronicsChinese Academy of SciencesBeijing 100094China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China

Precision diagnosis of leaf nitrogen(N)content in arbuscular mycorrhizal inoculated crops under drought stress,using hyperspectral remote sensing technology,would be significant to evaluate the mycorrhizal effect on crop growth condition in the arid and semi-arid *** this study,soybean plants with inoculation and non-inoculation treatments were grown under severe drought,moderate drought and normal irrigation *** spectral reflectance and several biochemical parameters were measured at 30 d,45 d and 64 d after *** analyses were conducted between leaf N content and the original and first derivative spectral reflectance.A series of first-order differential area indices and differential area ratio indices were proposed and *** indicated that arbuscular mycorrhizal fungi improved leaf N content under drought stresses,the spectral reflectance in visible to red edge regions of inoculated plants was lower than that of non-inoculated *** first-order differential area index at bands of 638-648 nm achieved the best estimation and prediction accuracies in leaf N content inversion,with the determination coefficient of calibration of 0.72,root mean square error of prediction and relative error of prediction of 0.46 and 11.60%,*** study provides a new insight for the evaluation of mycorrhizal effect under drought stress and opens up a new field of application for hyperspectral remote sensing.

关键词： leaf nitrogen content hyperspectral remote sensing mycorrhizal effect soybean drought stress

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Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform

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Science China Earth Sciences 2011年第4期54卷 625-630页

作者： QIN YuChu LI Bin NIU Zheng huang wenjiang WANG ChangYao State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

As an advanced generation instrument of earth observation,small footprint full waveform light detection and ranging(LiDAR) technology has been widely used in the past few *** and radiative correction is an important step in waveform data processing,it influences the accuracy of both information extraction and further *** on a stepwise strategy,this study adopts Gaussian mixture model to approximate the LiDAR *** addition to waveform decomposition,a relative correction model is proposed in this paper,the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative *** of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye,*** results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms,center position and width of each peak *** relative radiometric correction also improves the similarity of waveforms which acquired at the same target.

关键词： LiDAR waveform Gaussian mixture model stepwise decomposition relative radiometric correction

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Machine learning-based crop recognition from aerial remote sensing imagery

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Frontiers of Earth Science 2021年第1期15卷 54-69页

作者： Yanqin TIAN Chenghai YANG wenjiang huang Jia TANG Xingrong LI Qing ZHANG Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China USDA-Agricultural Research Service Aerial Application Technology Research UnitCollege StationTX 77845USA

Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management *** study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping *** analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features(layer spectral,geometry,and texture).Static recognition using layer spectral features had the highest accuracy of 75.4%in object-based classification,and dynamic recognition had the highest accuracy of 88.0%in object-based classification based on layer spectral and geometry *** identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results,but also amplify the differences between different ***-based classification produced better results than pixel-based classification,and the three feature sets(layer spectral alone,layer spectral and geometry,and all three)resulted in only small differences in accuracy in object-based *** recognition combined with objectbased classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial *** methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.

关键词： machine learning crop recognition aerial imagery dynamic recognition static recognition

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Influence of China’s Overseas power stations on the electricity status of their host countries

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International Journal of Digital Earth 2022年第1期15卷 416-436页

作者： Xumiao Gao Mingquan Wu Chao Li Zheng Niu Fang Chen wenjiang huang State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Jinan Geotechnical Investigation and Surveying Institute JinanPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China

China has become a major investor and constructor of electrical power plants in developing ***,the impacts of China's overseas power stations(COPSs)on the developing countries hosting them are poorly ***,a novel method is proposed to evaluate the influence of COPSs in 80 host ***,their electricity consumption from 1971 to 2017 was estimated using data provided by the World Bank,International Energy Agency,and World Resources *** analysis was then used to predict consumption from 2018 to ***,three parameters were used to evaluate the influences of *** results show that:1)COPSs significantly increased the total installed capacity of 35 of the host countries by>20%.2)The power generated by COPSs is greater than the growing demands of 32 of the host countries.3)COPSs will increase the per capita electricity consumption of all 80 host countries.4)Among the 437 COPSs existing in 2000–2019,renewable power plants(including hydropower)were most numerous,accounting for 51.3%.This proportion increased significantly after 2013 and renewable plants will continue to dominate as China will no longer invest in new coal-fired power stations after 2021.

关键词： Influence of China’s overseas power stations Belt and Road Initiative electricity status renewable sources of energy SDG7

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Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm

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International Journal of Agricultural and Biological Engineering 2020年第2期13卷 182-188页

作者： Linsheng huang Hansu Zhang Chao Ruan wenjiang huang Tingguang Hu Jinling Zhao National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefeiChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

A new method was proposed to extract sensitive features and to construct a monitoring model for wheat scab based on in situ hyperspectral data of wheat ears to achieve effective prevention and control and provide theoretical support for its large-scale *** sensitive features were selected through correlation analysis and wavelet *** features were as follows:three original bands of 350-400 nm,500-600 nm,and 720-1000 nm;three vegetation indices of modified simple ratio(MSR),normalized difference vegetation index,and structural independent pigment index;and two wavelet features of WF01 and *** combining the selected sensitive features with support vector machine(SVM)and SVM optimized by genetic algorithm(GASVM),a total of 16 monitoring models were built,and the monitoring accuracies of the two types of models were *** ability of the monitoring models built by GASVM to identify scab was better than that of SVM algorithm under the same characteristic *** the 16 models,MSR combined with GASVM had an overall accuracy of 75%and a Kappa coefficient of *** can be used to monitor wheat scab and its application can improve the accuracy of disease monitoring.

关键词： wheat scab hyperspectral data correlation analysis genetic algorithm wavelet transform support vector machine

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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 *** and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting *** 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 *** 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 *** 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 *** overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based *** RF algorithm required significantly less training time than the SVM and ANN *** 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 *** 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 *** 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 *** 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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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 *** winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in *** mildew(Blumeria graminis)and stripe rust(Puccinia striiformis ***)are two of the most prevalent winter wheat diseases in *** study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral *** spectral normalization process was applied prior to the *** t-tests were used to determine the sensitivity of the spectral bands and vegetation *** 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 *** 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 *** 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 *** analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for *** 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 *** detection of the severity of stripe rust using

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

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Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

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International Journal of Agricultural and Biological Engineering 2019年第6期12卷 149-155页

作者： Weiping Kong wenjiang huang Xianfeng Zhou Hugh Mortimer Lingling Ma Lingli Tang Chuanrong Li Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhou 310018China Rutherford Appleton Laboratory Harwell Science and Innovations CampusOxfordshireOX110QXUK

Leaf water content(LWC)of crops is a suitable parameter for evaluation of plant water status and arbuscular mycorrhizal effect on the host plant under drought *** sensing technology provides an effective avenue to estimate LWC in ***,few LWC retrieval models have been developed specifically for the arbuscular mycorrhizal inoculated *** this study,soybean with inoculation and non-inoculation treatments were planted under the severe drought,moderate drought and normal irrigation *** LWC changes under different treatments at the 30 th,45 th and 64 th day after the inoculation were investigated,and the spectral response characteristics of inoculated and non-inoculated soybean leaves under the three drought stresses were *** types of spectral variables/indices including:raw spectral reflectance(R),continuum-removed spectral reflectance(R C),difference vegetation index(DVI),normalized difference vegetation index(NDVI)and ratio vegetation index(RVI)were applied to determine the best estimator of *** results indicate that LWC decreased as the aggravating of drought stress ***,LWC in inoculated leaves was higher than that in the counterparts under the same drought stress level,and the values of raw reflectance measured at inoculated leaves were lower than the non-inoculated leaves,especially around 1900 nm and 1410 *** water spectral features were more evident in the corresponding continuum-removed spectral *** newly proposed DVI C(2280,1900)index,derived from the continuum-removed spectral reflectance at 2280 nm and the raw spectral reflectance at 1900 nm in DVI type of index,was the most robust for soybean LWC assessment,with R 2 value of 0.72(p<0.01)and root mean square error(RMSE)and mean absolute error(MAE)of 2.12%and 1.75%,*** study provides a means to monitor the mycorrhizal effect on drought-induced crops indirectly and non-destructively.

关键词： leaf water content remote sensing arbuscular mycorrhizal fungi drought crops

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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 *** 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 *** indices were calculated with Sentinel-2 MSI data and hyperspectral *** performances were validated against ground measurements using R2,RMSE,and *** results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling ***,rededge modified indices outperformed the traditional indices for both data *** 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 *** results were consistent with the red-edge inflec-tion point shift at different growth *** 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 *** 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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Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 138-144页

作者： Jingcheng Zhang Bin Wang Xuexue Zhang Peng Liu Yingying Dong Kaihua Wu wenjiang huang College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhou 310018China Key laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094 China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China

Detection of yellow rust using hyperspectral data is of practical importance for disease control and *** an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and *** the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected *** impacts of the mother wavelet type and spectral interval on disease detection were *** results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow *** Mexh wavelet slightly outperformed the others in estimating disease *** the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 ***,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow *** relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

关键词： continuous wavelet analysis spectral interval hyperspectral data wheat yellow rust

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