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
BibRef
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
BibRef
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.
IEEE DOI
1407
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Yao, X.[Xia],
Huang, Y.[Yu],
Shang, G.Y.[Gui-Yan],
Zhou, C.[Chen],
Cheng, T.[Tao],
Tian, Y.C.[Yong-Chao],
Cao, W.X.[Wei-Xing],
Zhu, Y.[Yan],
Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen
Concentration,
RS(7), No. 11, 2015, pp. 14939.
DOI Link
1512
BibRef
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
BibRef
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
BibRef
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
BibRef
Song, X.Y.[Xiao-Yu],
Yang, G.J.[Gui-Jun],
Yang, C.H.[Cheng-Hai],
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
BibRef
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
BibRef
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
BibRef
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.K.[Yin-Kun],
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
BibRef
Wang, B.J.[Blowman J.],
Chen, J.M.[Jing M.],
Ju, W.M.[Wei-Min],
Qiu, F.[Feng],
Zhang, Q.[Qian],
Fang, M.H.[Mei-Hong],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
IEEE DOI
1808
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Fu, Y.Y.[Yuan-Yuan],
Yang, G.J.[Gui-Jun],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Lee, H.[Hwang],
Wang, J.F.[Jin-Fei],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.W.[Chang-Wen],
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
BibRef
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
BibRef
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
BibRef
Xu, X.G.[Xin-Gang],
Fan, L.L.[Ling-Ling],
Li, Z.H.[Zhen-Hai],
Meng, Y.[Yang],
Feng, H.K.[Hai-Kuan],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Jin, J.[Jia],
Wu, M.[Mengjuan],
Song, G.[Guangman],
Wang, Q.[Quan],
Genetic Algorithm Captured the Informative Bands for Partial Least
Squares Regression Better on Retrieving Leaf Nitrogen from
Hyperspectral Reflectance,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Haumont, J.[Jérémie],
Lootens, P.[Peter],
Cool, S.[Simon],
van Beek, J.[Jonathan],
Raymaekers, D.[Dries],
Ampe, E.[Eva],
de Cuypere, T.[Tim],
Bes, O.[Onno],
Bodyn, J.[Jonas],
Saeys, W.[Wouter],
Multispectral UAV-Based Monitoring of Leek Dry-Biomass and Nitrogen
Uptake across Multiple Sites and Growing Seasons,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Benmouna, B.[Brahim],
Pourdarbani, R.[Raziyeh],
Sabzi, S.[Sajad],
Fernandez-Beltran, R.[Ruben],
García-Mateos, G.[Ginés],
Molina-Martínez, J.M.[José Miguel],
Comparison of Classic Classifiers, Metaheuristic Algorithms and
Convolutional Neural Networks in Hyperspectral Classification of
Nitrogen Treatment in Tomato Leaves,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Xu, S.Z.[Si-Zhe],
Xu, X.G.[Xin-Gang],
Blacker, C.[Clive],
Gaulton, R.[Rachel],
Zhu, Q.Z.[Qing-Zhen],
Yang, M.[Meng],
Yang, G.J.[Gui-Jun],
Zhang, J.M.[Jian-Min],
Yang, Y.[Yongan],
Yang, M.[Min],
Xue, H.Y.[Han-Yu],
Yang, X.D.[Xiao-Dong],
Chen, L.P.[Li-Ping],
Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices
and Feature Variable Optimization with Information Fusion of
Multiple-Sensor Images from UAV,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Olveira, A.L.[Adrián Lapaz],
Rozas, H.S.[Hernán Saínz],
Castro-Franco, M.[Mauricio],
Carciochi, W.[Walter],
Nieto, L.[Luciana],
Balzarini, M.[Mónica],
Ciampitti, I.[Ignacio],
Calvo, N.R.[Nahuel Reussi],
Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical,
and Sensor Satellite Data Fusion,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Zhang, H.[Helin],
Bai, J.[Jia],
Sun, R.[Rui],
Wang, Y.[Yan],
Pan, Y.H.[Yu-Hao],
McGuire, P.C.[Patrick C.],
Xiao, Z.Q.[Zhi-Qiang],
Improved Global Gross Primary Productivity Estimation by Considering
Canopy Nitrogen Concentrations and Multiple Environmental Factors,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Fan, Y.G.[Yi-Guang],
Feng, H.K.[Hai-Kuan],
Yue, J.[Jibo],
Liu, Y.[Yang],
Jin, X.[Xiuliang],
Xu, X.G.[Xin-Gang],
Song, X.Y.[Xiao-Yu],
Ma, Y.P.[Yan-Peng],
Yang, G.J.[Gui-Jun],
Comparison of Different Dimensional Spectral Indices for Estimating
Nitrogen Content of Potato Plants over Multiple Growth Periods,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Çimtay, Y.[Yücel],
Estimating Plant Nitrogen by Developing an Accurate Correlation
between VNIR-Only Vegetation Indexes and the Normalized Difference
Nitrogen Index,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Gao, C.[Changlun],
Tang, T.[Ting],
Wu, W.B.[Wei-Bin],
Zhang, F.[Fangren],
Luo, Y.Q.[Yuan-Qiang],
Wu, W.H.[Wei-Hao],
Yao, B.[Beihuo],
Li, J.[Jiehao],
Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves
Based on the CEEMDAN-SR Algorithm,
RS(15), No. 20, 2023, pp. 5013.
DOI Link
2310
BibRef
Munnaf, M.A.[Muhammad Abdul],
Guerrero, A.[Angela],
Calera, M.[Maria],
Mouazen, A.M.[Abdul Mounem],
Precision Nitrogen Fertilization for Opium Poppy Using Combined
Proximal and Remote Sensor Data Fusion,
RS(15), No. 23, 2023, pp. 5442.
DOI Link
2312
BibRef
Maina, A.W.[Angeline Wanjiku],
Becker, M.[Mathias],
Oerke, E.C.[Erich-Christian],
Assessing Interactions between Nitrogen Supply and Leaf Blast in Rice
by Hyperspectral Imaging,
RS(16), No. 6, 2024, pp. 939.
DOI Link
2403
BibRef
Yang, Y.J.[Yong-Jun],
Dong, J.[Jing],
Tang, J.J.[Jia-Jia],
Zhao, J.[Jiao],
Lei, S.G.[Shao-Gang],
Zhang, S.[Shaoliang],
Chen, F.[Fu],
Mapping Foliar C, N, and P Concentrations in An Ecological
Restoration Area with Mixed Plant Communities Based on LiDAR and
Hyperspectral Data,
RS(16), No. 9, 2024, pp. 1624.
DOI Link
2405
BibRef
Wu, Y.[Yin],
Lu, J.[Jingshan],
Liu, H.[Huahao],
Gou, T.[Tingyu],
Chen, F.[Fadi],
Fang, W.M.[Wei-Min],
Chen, S.[Sumei],
Zhao, S.[Shuang],
Jiang, J.[Jiafu],
Guan, Z.Y.[Zhi-Yong],
Monitoring the Nitrogen Nutrition Index Using Leaf-Based
Hyperspectral Reflectance in Cut Chrysanthemums,
RS(16), No. 16, 2024, pp. 3062.
DOI Link
2408
BibRef
Feng, H.L.[Hai-Lin],
Zhou, T.[Tong],
Wang, K.[Ketao],
Huang, J.Q.[Jian-Qin],
Liang, H.[Hao],
Lu, C.H.[Cheng-Hao],
Ruan, Y.[Yaoping],
Xu, L.[Liuchang],
From Spectral Characteristics to Index Bands: Utilizing UAV
Hyperspectral Index Optimization on Algorithms for Estimating Canopy
Nitrogen Concentration in Carya Cathayensis Sarg,
RS(16), No. 20, 2024, pp. 3780.
DOI Link
2411
BibRef
Wu, J.[Jing],
Tao, R.[Ran],
Zhao, P.[Pan],
Martin, N.F.[Nicolas F.],
Hovakimyan, N.[Naira],
Optimizing Nitrogen Management with Deep Reinforcement Learning and
Crop Simulations,
AgriVision22(1711-1719)
IEEE DOI
2210
Decision support systems, Training, Crops, Reinforcement learning,
Soil, Data models, Nitrogen
BibRef
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,
MAES20(45-54).
Springer DOI
2103
BibRef
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,
WVC17(7-12)
IEEE DOI
1804
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.K.[Hai-Kuan],
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
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Surface Fractional Vegetation Cover, FVC .