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remote sensing of quality traits in cereal and arable production systems:A review

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The Crop Journal 2024年第1期12卷 45-57页

作者： Zhenhai Li Chengzhi Fan Yu Zhao Xiuliang Jin Raffaele Casa Wenjiang Huang Xiaoyu Song Gerald Blasch Guijun Yang James Taylor Zhenhong 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 Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of Agriculture and Rural AffairsBeijing 100081China DAFNE Universitàdella TusciaVia San Camillo de Lellis01100 ViterboItaly key laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Maize and Wheat Improvement center(CIMMYT) PO Box 5689Addis AbabaEthiopia ITAP Univ.MontpellierINRAEInstitut AgroMontpellier 34000France College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054ShaanxiChina

Cereal is an essential source of calories and protein for the global *** predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy *** use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality *** studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing *** this paper,the key quality traits that are of interest to producers and consumers are *** literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality *** review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

关键词： remote sensing Quality traits Grain protein Cereal

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

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Journal of Integrative agriculture 2021年第9期20卷 2535-2551页

作者： ZHAO Yu WANG Jian-wen CHEN Li-ping FU Yuan-yuan ZHU Hong-chun FENG Hai-kuan XU Xin-gang LI Zhen-hai key laboratory of quantitative remote sensing in ministry of agriculture and rural affairs/beijing research center for information technology in agriculture Beijing 100097P.R.China National Engineering research center for information technology in agriculture Beijing 100097P.R.China College of Geomatics Shandong University of Science and TechnologyQingdao 266590P.R.China

The nitrogen nutrition index(NNI)is a reliable indicator for diagnosing crop nitrogen(N)***,there is currently no specific vegetation index for the NNI inversion across multiple growth *** overcome the limitations of the traditional direct NNI inversion method(NNI_(T1))of the vegetation index and traditional indirect NNI inversion method(NNI_(T2))by inverting intermediate variables including the aboveground dry biomass(AGB)and plant N concentration(PNC),this study proposed a new NNI remote sensing index(NNI_(RS)).A remote-sensing-based critical N dilution curve(Nc_(_RS))was set up directly from two vegetation indices and then used to calculate NNI_(RS).Field data including AGB,PNC,and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1),2013–2014(Exp.2),2014–2015(Exp.3),2015–2016(Exp.4))in beijing,*** experimental datasets were cross-validated to each of the NNI models(NNI_(T1),NNI_(T2)and NNI_(RS)).The results showed that:(1)the NNI_(RS)models were represented by the standardized leaf area index determining index(sLAIDI)and the red-edge chlorophyll index(CI_(red edge))in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b),where"a"equals 2.06,2.10,2.08 and 2.02 and"b"equals 0.66,0.73,0.67 and 0.62 when the modeling set data came from Exp.1/2/4,Exp.1/2/3,Exp.1/3/4,and Exp.2/3/4,respectively;(2)the NNI_(RS)models achieved better performance than the other two NNI revised methods,and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14,respectively;(3)when the remaining data were used for verification,the NNI_(RS)models also showed good stability,with RMSE values of 0.09,0.18,0.13 and 0.10,***,it is concluded that the NNI_(RS)method is promising for the remote assessment of crop N status.

关键词： nitrogen nutrition index(NNI) critical nitrogen dilution curve standardized leaf area index determining index(s LAIDI) the red-edge chlorophyll index(CI_(red edge))

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

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Journal of Integrative agriculture 2019年第7期18卷 1562-1570页

作者： DUAN Dan-dan ZHAO Chun-jiang LI Zhen-hai YANG Gui-jun ZHAO Yu QIAO Xiao-jun ZHANG Yun-he ZHANG Lai-xi YANG Wu-de Institute of Dry Farming Engineering Shanxi Agricultural UniversityTaigu 030801P.R.China National Engineering research center for information technology in agriculture Beijing 100097P.R.China key laboratory of quantitative remote sensing in agriculture of ministry of agriculture and rural affairs/beijing research center for information technology in agriculture Beijing 100097P.R.China beijing Engineering research center for agriculture Internet of Things Beijing 100097P.R. China

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental *** this study,we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND).The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the *** results show that:(1) the vertical distribution law of LNC was distinct,presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth *** entire canopy,the three upper layers,the three upper layers,and the top layer are the effective layers at the jointing stage,flag leaf stage,flowering stages,and filling stage,respectively.(3) The TLNC model considering the VND has high predicting accuracy and *** models based on the greenness index(GI),mND705(modified normalized difference 705),and normalized difference vegetation index(NDVI),the values for the determining coefficient(R2),and normalized root mean square error(nRMSE) are 0.61 and 8.84%,0.59 and 8.89%,and 0.53 and 9.37%,***,the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.

关键词： nitrogen concentration hyperspectral vertical nitrogen distribution winter wheat

Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

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The Crop Journal 2022年第5期10卷 1346-1352页

作者： Yu Zhao Yang Meng Shaoyu Han Haikuan Feng Guijun Yang Zhenhai Li key laboratory of quantitative remote sensing in agriculture of ministry of agriculture and rural affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Collaborative Innovation center for Modern Crop Production Co-sponsored by Province and ministry Nanjing Agricultural UniversityNanjing 210095JiangsuChina College of Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

关键词： Agronomic traits Phenological effect Vegetation index Hierarchical linear model Winter wheat

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

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The Crop Journal 2022年第5期10卷 1251-1263页

作者： Jia Sun Lunche Wang Shuo Shi Zhenhai Li Jian Yang Wei Gong Shaoqiang Wang Torbern Tagesson key laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeoscienceWuhan 430079HubeiChina Department of Physical Geography and Ecosystem Science Lund UniversityLund 117 SE-22100Sweden State key laboratory of information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079HubeiChina key laboratory of quantitative remote sensing in ministry of agriculture and rural affairs Beijing Research Center for Information Technology in AgricultureBeijing 100097China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagen 1172Denmark

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological *** of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model *** structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment *** canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval *** using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy *** study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

关键词： Leaf pigment PROSAIL model Canopy structure Chlorophyll content Leaf area index Leaf angle distribution

A Ceph-based storage strategy for big gridded remote sensing data

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Big Earth Data 2022年第3期6卷 323-339页

作者： Xinyu Tang Xiaochuang Yao Diyou Liu Long Zhao Li Li Dehai Zhu Guoqing Li College of Land Science and technology China Agricultural UniversityBeijingChina key laboratory of remote sensing for Agri-Hazards Ministry of Agriculture and Rural AffairsBeijingChina Aerospace information research Institute Chinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina

When using distributed storage systems to store gridded remote sensing data in large,distributed clusters,most solutions utilize big table index storage ***,in practice,the performance of big table index storage strategies degrades as scenarios become more complex,and the reasons for this phenomenon are analyzed in this *** improve the read and write performance of distributed gridded data storage,this paper proposes a storage strategy based on Ceph *** strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data,finding the cluster location of gridded data through hash-like *** method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded *** write and spatial query experiments proved the feasibility,effectiveness,and stability of this *** experiments prove that the method has higher stability than,and that the average query time is 38%lower than that for,the large table index storage strategy,which greatly improves the storage and query efficiency of gridded images.

关键词： Gridded images distributed storage system metadata management Ceph remote sensing

Farmers’precision pesticide technology adoption and its influencing factors:Evidence from apple production areas in China

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Journal of Integrative agriculture 2023年第1期22卷 292-305页

作者： YUE Meng LI Wen-jing JIN Shan CHEN Jing CHANG Qian Glyn JONES CAO Yi-ying YANG Gui-jun LI Zhen-hong Lynn JFREWER College of Economics and Management Huazhong Agricultural UniversityWuhan 430070P.R.China School of Natural and Environmental Sciences Newcastle UniversityNewcastle upon TyneNE17RUUK Institute for Agri-Food research and Innovation Fera Science Ltd.York YO411LZUK Institute of Agricultural Economics and Development Chinese Academy of Agricultural SciencesBeijing 100081P.R.China information technology research center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097P.R.China RSK ADAS Ltd. Helsby WA60ARUK key laboratory of quantitative remote sensing in agriculture of ministry of agriculture and rural affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097P.R.China College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054P.R.China

The research aimed to understand farmers’willingness to adopt(WTA)and willingness to pay(WTP)for precision pesticide technologies and analyzed the determinants of farmers’*** used a two-stage approach to consider farmers’WTA and WTP for precision pesticide technologies.A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in *** data were analyzed using the double-hurdle *** results indicated that 78.72%of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises,and 69.72%were willing to buy the equipment for using precision pesticide *** results of the determinant analysis indicated that farmers’perceived perceptions,farm scale,cooperative membership,access to digital information,and availability of financial services had significant and positive impacts on farmers’WTA precision pesticide *** membership,technical training,and adherence to environmental regulations increased farmers’WTP for precision pesticide ***,nonlinear relationships between age,agricultural experience,and farmers’WTA and WTP for precision pesticide technology services were found.

关键词： precision technologies apple production precision pesticides willingness to adopt willingness to pay

quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

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Journal of Arid Land 2023年第1期15卷 91-108页

作者： LIU Yifeng GUO Bing LU Miao ZANG Wenqian YU Tao CHEN Donghua School of Civil Architectural Engineering Shandong University of TechnologyZibo 255000China key laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000China key laboratory of National Geographic Census and Monitoring Ministry of Natural ResourcesWuhan 430072China Aerospace information research Institute Chinese Academy of SciencesBeijing 100101China State key laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China key laboratory of Agricultural remote sensing Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081China Zhongke Langfang Institute of Spatial information Applications Langfang 065000China College of Computer and information Engineering Chuzhou UniversityChuzhou 239000China

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound *** can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical ***,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change *** on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector *** results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to *** 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing *** gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to *** 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the *** the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human *** results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

关键词： vegetation evolution driving mechanisms climate change human activities relative actions Geodetector Yellow River Basin

Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

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Journal of Integrative agriculture 2021年第10期20卷 2613-2626页

作者： YANG Fei-fei LIU Tao WANG Qi-yuan DU Ming-zhu YANG Tian-le LIU Da-zhong LI Shi-juan LIU Sheng-ping key laboratory of Agri-information Service technology Ministry of Agriculture and Rural Affairs/Agricultural Information InstituteChinese Academy of Agricultural SciencesBeijing 100081P.R.China Jiangsu key laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation center for Modern Production technology of Grain Crops/Agricultural College Yangzhou UniversityYangzhou 225009P.R.China College of Geoscience and Surveying Engineering China University of Mining&TechnologyBeijing 100083P.R.China

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall *** water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine ***,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing *** hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was *** with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of *** decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe *** sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)*** BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.

关键词： winter wheat hyperspectral remote sensing leaf water content new vegetation index BP neural network