Leaf Nitrogen, Crop Nitrogen

Chapter Contents (Back)

Ramoelo, A.[Abel], Skidmore, A.K.[Andrew K.], Schlerf, M.[Martin], Mathieu, R.[Renaud], Heitkonig, I.M.A.[Ignas M.A.],
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations,
PandRS(66), No. 4, July 2011, pp. 408-417.
Elsevier DOI 1107
Nitrogen concentration; Phosphorus concentration; Water removal; Continuum removal; Bootstrapping BibRef

Foster, A., Kakani, V., Ge, J., Mosali, J.,
Discrimination of Switchgrass Cultivars and Nitrogen Treatments Using Pigment Profiles and Hyperspectral Leaf Reflectance Data,
RS(4), No. 9, September 2012, pp. 2576-2594.
DOI Link 1210

Miphokasap, P., Honda, K., Vaiphasa, C., Souris, M., Nagai, M.,
Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy,
RS(4), No. 6, June 2012, pp. 1651-1670.
DOI Link 1208

Ramoelo, A., Skidmore, A.K., Cho, M.A., Mathieu, R., Heitkönig, I.M.A., Dudeni-Tlhone, N., Schlerf, M., Prins, H.H.T.,
Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data,
PandRS(82), No. 1, August 2013, pp. 27-40.
Elsevier DOI 1306
In situ hyperspectral remote sensing, Ecosystem, Partial least square regression, Radial basis neural network, Nitrogen concentrations, Phosphorus concentrations BibRef

Yu, K.[Kang], Li, F.[Fei], Gnyp, M.L.[Martin L.], Miao, Y.X.[Yu-Xin], Bareth, G.[Georg], Chen, X.P.[Xin-Ping],
Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,
PandRS(78), No. 1, April 2013, pp. 102-115.
Elsevier DOI 1304
Hyperspectral index; Nitrogen status; Rice; Heading stage; N dilution effect; Stepwise multiple linear regression; Lambda by lambda band-optimized algorithm BibRef

Kim, J., Grunwald, S., Rivero, R.G.,
Soil Phosphorus and Nitrogen Predictions Across Spatial Escalating Scales in an Aquatic Ecosystem Using Remote Sensing Images,
GeoRS(52), No. 10, October 2014, pp. 6724-6737.
Biological system modeling BibRef

Cilia, C.[Chiara], Panigada, C.[Cinzia], Rossini, M.[Micol], Meroni, M.[Michele], Busetto, L.[Lorenzo], Amaducci, S.[Stefano], Boschetti, M.[Mirco], Picchi, V.[Valentina], Colombo, R.[Roberto],
Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery,
RS(6), No. 7, 2014, pp. 6549-6565.
DOI Link 1408

Li, S.[Shuo], Ji, W.J.[Wen-Jun], Chen, S.C.[Song-Chao], Peng, J.[Jie], Zhou, Y.[Yin], Shi, Z.[Zhou],
Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China,
RS(7), No. 6, 2015, pp. 7029.
DOI Link 1507

Huang, S.[Shanyu], Miao, Y.X.[Yu-Xin], Zhao, G.M.[Guang-Ming], Yuan, F.[Fei], Ma, X.B.[Xiao-Bo], Tan, C.X.[Chuan-Xiang], Yu, W.F.[Wei-Feng], Gnyp, M.L.[Martin L.], Lenz-Wiedemann, V.I.S.[Victoria I.S.], Rascher, U.[Uwe], Bareth, G.[Georg],
Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China,
RS(7), No. 8, 2015, pp. 10646.
DOI Link 1509

Chen, P.F.[Peng-Fei],
A Comparison of Two Approaches for Estimating the Wheat Nitrogen Nutrition Index Using Remote Sensing,
RS(7), No. 4, 2015, pp. 4527-4548.
DOI Link 1505

Yao, X.[Xia], Huang, Y.[Yu], Shang, G.Y.[Gui-Yan], Zhou, C.[Chen], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Cao, W.[Weixing], Zhu, Y.[Yan],
Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration,
RS(7), No. 11, 2015, pp. 14939.
DOI Link 1512

Du, L.[Lin], Shi, S.[Shuo], Yang, J.[Jian], Sun, J.[Jia], Gong, W.[Wei],
Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,
RS(8), No. 6, 2016, pp. 526.
DOI Link 1608

Xia, T.T.[Ting-Ting], Miao, Y.X.[Yu-Xin], Wu, D.[Dali], Shao, H.[Hui], Khosla, R.[Rajiv], Mi, G.H.[Guo-Hua],
Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index,
RS(8), No. 7, 2016, pp. 605.
DOI Link 1608

Dong, R.[Rui], Miao, Y.X.[Yu-Xin], Wang, X.B.[Xin-Bing], Yuan, F.[Fei], Kusnierek, K.[Krzysztof],
Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112

Song, X.Y.[Xiao-Yu], Yang, G.[Guijun], Yang, C.[Chenghai], Wang, J.[Jihua], Cui, B.[Bei],
Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Sun, J.[Jia], Yang, J.[Jian], Shi, S.[Shuo], Chen, B.[Biwu], Du, L.[Lin], Gong, W.[Wei], Song, S.[Shalei],
Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Du, L.[Lin], Shi, S.[Shuo], Gong, W.[Wei], Yang, J.[Jian], Sun, J.[Jia], Mao, F.Y.[Fei-Yue],
Wavelength Selection Of Hyperspectral Lidar Based On Feature Weighting For Estimation Of Leaf Nitrogen Content In Rice,
ISPRS16(B1: 9-13).
DOI Link 1610

Moharana, S.[Shreedevi], Dutta, S.[Subashisa],
Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery,
PandRS(122), No. 1, 2016, pp. 17-29.
Elsevier DOI 1612
Rice BibRef

Huang, S.Y.[Shan-Yu], Miao, Y.X.[Yu-Xin], Yuan, F.[Fei], Gnyp, M.L.[Martin L.], Yao, Y.[Yinkun], Cao, Q.[Qiang], Wang, H.Y.[Hong-Ye], Lenz-Wiedemann, V.I.S.[Victoria I. S.], Bareth, G.[Georg],
Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Wang, B.J.[Blowman J.], Chen, J.M.[Jing M.], Ju, W.M.[Wei-Min], Qiu, F.[Feng], Zhang, Q.[Qian], Fang, M.[Meihong], Chen, F.[Fenge],
Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Ramoelo, A.[Abel], Cho, M.A.[Moses Azong],
Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Yang, J.[Jian], Song, S.[Shalei], Du, L.[Lin], Shi, S.[Shuo], Gong, W.[Wei], Sun, J.[Jia], Chen, B.[Biwu],
Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Zheng, H.B.[Heng-Biao], Cheng, T.[Tao], Li, D.[Dong], Zhou, X.[Xiang], Yao, X.[Xia], Tian, Y.C.[Yong-Chao], Cao, W.X.[Wei-Xing], Zhu, Y.[Yan],
Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806

Näsi, R.[Roope], Viljanen, N.[Niko], Kaivosoja, J.[Jere], Alhonoja, K.[Katja], Hakala, T.[Teemu], Markelin, L.[Lauri], Honkavaara, E.[Eija],
Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Jia, M.[Min], Zhu, J.[Jie], Ma, C.C.[Chun-Chen], Alonso, L.[Luis], Li, D.[Dong], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Yao, X.[Xia], Cao, W.X.[Wei-Xing],
Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Li, Z.H.[Zhen-Hai], Jin, X.L.[Xiu-Liang], Yang, G.J.[Gui-Jun], Drummond, J.[Jane], Yang, H.[Hao], Clark, B.[Beth], Li, Z.H.[Zhen-Hong], Zhao, C.J.[Chun-Jiang],
Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Liang, L.[Liang], Di, L.P.[Li-Ping], Huang, T.[Ting], Wang, J.H.[Jia-Hui], Lin, L.[Li], Wang, L.J.[Li-Juan], Yang, M.H.[Min-Hua],
Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Zheng, H.B.[Heng-Biao], Li, W.[Wei], Jiang, J.[Jiale], Liu, Y.[Yong], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Zhang, Y.[Yu], Yao, X.[Xia],
A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Ye, H.C.[Hui-Chun], Huang, W.J.[Wen-Jiang], Huang, S.Y.[Shan-Yu], Wu, B.[Bin], Dong, Y.Y.[Ying-Ying], Cui, B.[Bei],
Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Zhao, H.T.[Hai-Tao], Song, X.Y.[Xiao-Yu], Yang, G.J.[Gui-Jun], Li, Z.N.[Zhe-Nhai], Zhang, D.Y.[Dong-Yan], Feng, H.K.[Hai-Kuan],
Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Jiang, J.[Jiale], Cai, W.[Weidi], Zheng, H.B.[Heng-Biao], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Ehsani, R.[Reza], Hu, Y.Q.[Yong-Qiang], Niu, Q.S.[Qing-Song], Gui, L.J.[Li-Juan], Yao, X.[Xia],
Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Röll, G.[Georg], Hartung, J.[Jens], Graeff-Hönninger, S.[Simone],
Determination of Plant Nitrogen Content in Wheat Plants via Spectral Reflectance Measurements: Impact of Leaf Number and Leaf Position,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Li, H.J.[Hong-Jun], Zhang, Y.M.[Yu-Ming], Lei, Y.P.[Yu-Ping], Antoniuk, V.[Vita], Hu, C.S.[Chun-Sheng],
Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001

Brinkhoff, J.[James], Dunn, B.W.[Brian W.], Robson, A.J.[Andrew J.], Dunn, T.S.[Tina S.], Dehaan, R.L.[Remy L.],
Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908

Huang, S.Y.[Shan-Yu], Miao, Y.X.[Yu-Xin], Yuan, F.[Fei], Cao, Q.A.[Qi-Ang], Ye, H.C.[Hui-Chun], Lenz-Wiedemann, V.I.S.[Victoria I.S.], Bareth, G.[Georg],
In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909

Tilly, N.[Nora], Bareth, G.[Georg],
Estimating Nitrogen from Structural Crop Traits at Field Scale: A Novel Approach Versus Spectral Vegetation Indices,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Ling, B.[Bohua], Raynor, E.J.[Edward J.], Goodin, D.G.[Douglas G.], Joern, A.[Anthony],
Effects of Fire and Large Herbivores on Canopy Nitrogen in a Tallgrass Prairie,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906

Gao, J.L.[Jin-Long], Liang, T.G.[Tian-Gang], Yin, J.P.[Jian-Peng], Ge, J.[Jing], Feng, Q.S.[Qi-Sheng], Wu, C.X.[Cai-Xia], Hou, M.J.[Meng-Jing], Liu, J.[Jie], Xie, H.J.[Hong-Jie],
Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

de Souza, R.[Romina], Peńa-Fleitas, M.T.[M. Teresa], Thompson, R.B.[Rodney B.], Gallardo, M.[Marisa], Padilla, F.M.[Francisco M.],
Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Li, D.L.[Dao-Liang], Zhang, P.[Pan], Chen, T.[Tao], Qin, W.[Wei],
Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
Survey, Nitrogen. BibRef

Basak, R.[Rinku], Wahid, K.[Khan], Dinh, A.[Anh],
Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Mutowo, G.[Godfrey], Mutanga, O.[Onisimo], Masocha, M.[Mhosisi],
Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
Nitrogen in leaves. BibRef

Sulistyo, S.B., Woo, W.L., Dlay, S.S., Gao, B.,
Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants,
IEEE_Int_Sys(33), No. 3, May 2018, pp. 15-26.
Image color analysis, Nitrogen, Feature extraction, Image segmentation, Estimation, Machine learning, Neural networks, image processing and computer vision BibRef

Watt, M.S.[Michael S.], Buddenbaum, H.[Henning], Leonardo, E.M.C.[Ellen Mae C.], Estarija, H.J.C.[Honey Jane C.], Bown, H.E.[Horacio E.], Gomez-Gallego, M.[Mireia], Hartley, R.[Robin], Massam, P.[Peter], Wright, L.[Liam], Zarco-Tejada, P.J.[Pablo J.],
Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition,
PandRS(169), 2020, pp. 406-420.
Elsevier DOI 2011
High resolution hyperspectral, N:P ratio, Nitrogen, Nutrient limitation, Phosphorus, Reflectance BibRef

Osco, L.P.[Lucas Prado], Junior, J.M.[José Marcato], Marques Ramos, A.P.[Ana Paula], Garcia Furuya, D.E.[Danielle Elis], Cordeiro Santana, D.[Dthenifer], Ribeiro Teodoro, L.P.[Larissa Pereira], Nunes Gonçalves, W.[Wesley], Rojo Baio, F.H.[Fábio Henrique], Pistori, H.[Hemerson], da Silva Junior, C.A.[Carlos Antonio], Teodoro, P.E.[Paulo Eduardo],
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010

Liu, S.[Shishi], Yang, X.[Xin], Guan, Q.F.[Qing-Feng], Lu, Z.F.[Zhi-Feng], Lu, J.W.[Jian-Wei],
An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Du, L.[Lin], Yang, J.[Jian], Chen, B.[Bowen], Sun, J.[Jia], Chen, B.[Biwu], Shi, S.[Shuo], Song, S.[Shalei], Gong, W.[Wei],
Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001

Zha, H.[Hainie], Miao, Y.X.[Yu-Xin], Wang, T.T.[Tian-Tian], Li, Y.[Yue], Zhang, J.[Jing], Sun, W.C.[Wei-Chao], Feng, Z.Q.[Zheng-Qi], Kusnierek, K.[Krzysztof],
Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Li, D.[Dan], Miao, Y.X.[Yu-Xin], Ransom, C.J.[Curtis J.], Bean, G.M.[Gregory Mac], Kitchen, N.R.[Newell R.], Fernández, F.G.[Fabián G.], Sawyer, J.E.[John E.], Camberato, J.J.[James J.], Carter, P.R.[Paul R.], Ferguson, R.B.[Richard B.], Franzen, D.W.[David W.], Laboski, C.A.M.[Carrie A. M.], Nafziger, E.D.[Emerson D.], Shanahan, J.F.[John F.],
Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Zheng, H.B.[Heng-Biao], Ma, J.F.[Ji-Feng], Zhou, M.[Meng], Li, D.[Dong], Yao, X.[Xia], Cao, W.X.[Wei-Xing], Zhu, Y.[Yan], Cheng, T.[Tao],
Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Lu, J.J.[Jun-Jun], Miao, Y.X.[Yu-Xin], Shi, W.[Wei], Li, J.X.[Jing-Xin], Hu, X.Y.[Xiao-Yi], Chen, Z.C.[Zhi-Chao], Wang, X.B.[Xin-Bing], Kusnierek, K.[Krzysztof],
Developing a Proximal Active Canopy Sensor-based Precision Nitrogen Management Strategy for High-Yielding Rice,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Colorado, J.D.[Julian D.], Cera-Bornacelli, N.[Natalia], Caldas, J.S.[Juan S.], Petro, E.[Eliel], Rebolledo, M.C.[Maria C.], Cuellar, D.[David], Calderon, F.[Francisco], Mondragon, I.F.[Ivan F.], Jaramillo-Botero, A.[Andres],
Estimation of Nitrogen in Rice Crops from UAV-Captured Images,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Fu, Y.Y.[Yuan-Yuan], Yang, G.[Guijun], Li, Z.H.[Zhen-Hai], Song, X.Y.[Xiao-Yu], Li, Z.H.[Zhen-Hong], Xu, X.G.[Xin-Gang], Wang, P.[Pei], Zhao, C.J.[Chun-Jiang],
Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Jiang, J.[Jie], Zhang, Z.[Zeyu], Cao, Q.A.[Qi-Ang], Liang, Y.[Yan], Krienke, B.[Brian], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Liu, X.J.[Xiao-Jun],
Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Xu, K.[Ke], Zhang, J.C.[Jing-Chao], Li, H.M.[Huai-Min], Cao, W.X.[Wei-Xing], Zhu, Y.[Yan], Jiang, X.P.[Xiao-Ping], Ni, J.[Jun],
Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Gao, J.L.[Jin-Long], Liang, T.G.[Tian-Gang], Liu, J.[Jie], Yin, J.P.[Jian-Peng], Ge, J.[Jing], Hou, M.J.[Meng-Jing], Feng, Q.S.[Qi-Sheng], Wu, C.X.[Cai-Xia], Xie, H.J.[Hong-Jie],
Potential of hyperspectral data and machine learning algorithms to estimate the forage carbon-nitrogen ratio in an alpine grassland ecosystem of the Tibetan Plateau,
PandRS(163), 2020, pp. 362-374.
Elsevier DOI 2005
Forage nutrition, Random forest, Absorption bands, Estimation model, Growth stage BibRef

Gao, J.L.[Jin-Long], Liu, J.[Jie], Liang, T.G.[Tian-Gang], Hou, M.J.[Meng-Jing], Ge, J.[Jing], Feng, Q.S.[Qi-Sheng], Wu, C.X.[Cai-Xia], Li, W.L.[Wen-Long],
Mapping the Forage Nitrogen-Phosphorus Ratio Based on Sentinel-2 MSI Data and a Random Forest Algorithm in an Alpine Grassland Ecosystem of the Tibetan Plateau,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Dong, R.[Rui], Miao, Y.X.[Yu-Xin], Wang, X.B.[Xin-Bing], Chen, Z.C.[Zhi-Chao], Yuan, F.[Fei], Zhang, W.[Weina], Li, H.G.[Hai-Gang],
Estimating Plant Nitrogen Concentration of Maize using a Leaf Fluorescence Sensor across Growth Stages,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

Nigon, T.J.[Tyler J.], Yang, C.[Ce], Paiao, G.D.[Gabriel Dias], Mulla, D.J.[David J.], Knight, J.F.[Joseph F.], Fernández, F.G.[Fabián G.],
Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Thompson, L.J.[Laura J.], Puntel, L.A.[Laila A.],
Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006

Siqueira, R.[Rafael], Longchamps, L.[Louis], Dahal, S.[Subash], Khosla, R.[Raj],
Use of Fluorescence Sensing to Detect Nitrogen and Potassium Variability in Maize,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Lee, H.[Hwang], Wang, J.[Jinfei], Leblon, B.[Brigitte],
Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007

Crema, A.[Alberto], Boschetti, M.[Mirco], Nutini, F.[Francesco], Cillis, D.[Donato], Casa, R.[Raffaele],
Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007

Jiang, J.[Jiale], Zhu, J.[Jie], Wang, X.[Xue], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Yao, X.[Xia],
Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Tahmasbian, I.[Iman], Morgan, N.K.[Natalie K.], Bai, S.H.[Shahla Hosseini], Dunlop, M.W.[Mark W.], Moss, A.F.[Amy F.],
Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104

Ge, H.X.[Hai-Xiao], Xiang, H.T.[Hai-Tao], Ma, F.[Fei], Li, Z.W.[Zhen-Wang], Qiu, Z.C.[Zheng-Chao], Tan, Z.Z.[Zheng-Zheng], Du, C.[Changwen],
Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Saberioon, M.M., Gholizadeh, A.,
Novel Approach For Estimating Nitrogen Content In Paddy Fields Using Low Altitude Remote Sensing System,
ISPRS16(B1: 1011-1015).
DOI Link 1610

Cummings, C.[Cadan], Miao, Y.X.[Yu-Xin], Paiao, G.D.[Gabriel Dias], Kang, S.[Shujiang], Fernández, F.G.[Fabián G.],
Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Xu, X.G.[Xin-Gang], Fan, L.L.[Ling-Ling], Li, Z.H.[Zhen-Hai], Meng, Y.[Yang], Feng, H.[Haikuan], Yang, H.[Hao], Xu, B.[Bo],
Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Perich, G.[Gregor], Aasen, H.[Helge], Verrelst, J.[Jochem], Argento, F.[Francesco], Walter, A.[Achim], Liebisch, F.[Frank],
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Verrelst, J.[Jochem], Rivera-Caicedo, J.P.[Juan Pablo], Reyes-Muńoz, P.[Pablo], Morata, M.[Miguel], Amin, E.[Eatidal], Tagliabue, G.[Giulia], Panigada, C.[Cinzia], Hank, T.[Tobias], Berger, K.[Katja],
Mapping landscape canopy nitrogen content from space using PRISMA data,
PandRS(178), 2021, pp. 382-395.
Elsevier DOI 2108
Canopy nitrogen content, PRISMA, CHIME, Hybrid retrieval, Gaussian process regression, Dimensionality reduction, Imaging spectroscopy BibRef

Wang, L.[Li], Chen, S.[Shuisen], Li, D.[Dan], Wang, C.Y.[Chong-Yang], Jiang, H.[Hao], Zheng, Q.[Qiong], Peng, Z.P.[Zhi-Ping],
Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Yu, J.[Jody], Wang, J.F.[Jin-Fei], Leblon, B.[Brigitte],
Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

He, W.[Wen], Li, Y.Q.[Yan-Qiong], Wang, J.Y.[Jin-Ye], Yao, Y.F.[Yue-Feng], Yu, L.[Ling], Gu, D.X.[Da-Xing], Ni, L.K.[Long-Kang],
Using Field Spectroradiometer to Estimate the Leaf N/P Ratio of Mixed Forest in a Karst Area of Southern China: A Combined Model to Overcome Overfitting,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Rehman, T.H.[Telha H.], Lundy, M.E.[Mark E.], Linquist, B.A.[Bruce A.],
Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206

Holzhauser, K.[Katja], Räbiger, T.[Thomas], Rose, T.[Till], Kage, H.[Henning], Kühling, I.[Insa],
Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Pylianidis, C.[Christos], Snow, V.[Val], Holzworth, D.[Dean], Bryant, J.[Jeremy], Athanasiadis, I.N.[Ioannis N.],
Location-specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate,
Springer DOI 2103

Montes Condori, R.H., Romualdo, L.M., Martinez Bruno, O., de Cerqueira Luz, P.H.,
Comparison Between Traditional Texture Methods and Deep Learning Descriptors for Detection of Nitrogen Deficiency in Maize Crops,
convolution, crops, feedforward neural nets, image texture, learning (artificial intelligence), nitrogen, CNN model, transfer learning BibRef

Wang, Y.J.[Yan-Jie], Liao, Q.H.[Qin-Hong], Yang, G.J.[Gui-Jun], Feng, H.[Haikuan], Yang, X.D.[Xiao-Dong], Yue, J.[Jibo],
Comparing Broad-band And Red Edge-based Spectral Vegetation Indices To Estimate Nitrogen Concentration Of Crops Using Casi Data,
ISPRS16(B7: 137-143).
DOI Link 1610

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Surface Fractional Vegetation Cover .

Last update:Sep 28, 2022 at 16:10:08