Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields
暂无分享,去创建一个
[1] Shuisen Chen,et al. Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra , 2016 .
[2] Sanford Weisberg,et al. An R Companion to Applied Regression , 2010 .
[3] Yang Song. Evaluation of the UAV-Based Multispectral Imagery and Its Application for Crop Intra-Field Nitrogen Monitoring and Yield Prediction in Ontario , 2016 .
[4] Anatoly A. Gitelson,et al. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[5] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[6] J. Watmough,et al. Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion , 2010 .
[8] Arko Lucieer,et al. Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing , 2012, Remote. Sens..
[9] Yan Zhu,et al. Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles , 2019, Sensors.
[10] Mohammad El-Hajj,et al. Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping , 2019, Remote. Sens..
[11] S. Kaparthi,et al. A Bibliometric Analysis , 2005, J. Decis. Syst..
[12] Jiali Shang,et al. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. , 2019, The Science of the total environment.
[13] Sofia Bajocco,et al. A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies , 2019, International Journal of Remote Sensing.
[14] Jan G. P. W. Clevers,et al. Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[15] Farid Kendoul,et al. Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[16] J. A. Schell,et al. Monitoring vegetation systems in the great plains with ERTS , 1973 .
[17] K. Soudani,et al. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .
[18] Wenjiang Huang,et al. Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[19] F. Maupas,et al. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping , 2017 .
[20] Hadley Wickham,et al. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .
[21] Stephanie S. Day,et al. Relationship of Drone‐Based Vegetation Indices with Corn and Sugarbeet Yields , 2019, Agronomy Journal.
[22] Narcisa Pricope,et al. Multi-Sensor Assessment of the Effects of Varying Processing Parameters on UAS Product Accuracy and Quality , 2019 .
[23] R. J. Larsen. Winter-Hardy Spring Wheat Breeding: Analysis of Winter x Spring Wheat Germplasm and the Development of Selection Tools , 2012 .
[24] A. Qin,et al. Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize , 2018 .
[25] W. E. Larson,et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. , 2000 .
[26] J. Schjoerring,et al. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .
[27] J. Dungan,et al. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.
[28] S. Shafian,et al. Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat , 2018 .
[29] Jan G. P. W. Clevers,et al. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[30] C. Jordan. Derivation of leaf-area index from quality of light on the forest floor , 1969 .
[31] A. Gitelson,et al. Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .
[32] Jaume Lloveras,et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service , 2016, Remote. Sens..
[33] L. Deng,et al. UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[34] Chunhua Zhang,et al. The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.
[35] Yong Liu,et al. Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[36] Syed Tahir Ata-UI-Karim,et al. Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status , 2018, Front. Plant Sci..
[37] W. Maes,et al. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. , 2019, Trends in plant science.
[38] Arko Lucieer,et al. Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..
[39] John R. Miller,et al. Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.
[40] Jun Ni,et al. Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat , 2013, Sensors.
[41] Jan G. P. W. Clevers,et al. Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
[42] H. Jones,et al. Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .
[43] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[44] P. Stephen Baenziger,et al. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat , 2018 .
[45] M. Westoby,et al. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .
[46] Wei Li,et al. A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle , 2018, Remote. Sens..
[47] A. Gitelson,et al. Remote estimation of crop fractional vegetation cover: the use of noise equivalent as an indicator of performance of vegetation indices , 2013 .
[48] Luis Miguel Contreras-Medina,et al. A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.
[49] Susan L. Ustin,et al. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices , 2014 .
[50] Mahmoud Omid,et al. Multispectral remote sensing for site-specific nitrogen fertilizer management , 2013 .