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NumberTitleAuthorsJournal
Year of publication
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Photosynthetic contribution of the ear to grain filling in wheat: a comparison of different methodologies for evaluation
Rut Sanchez-Bragado Gemma Molero Matthew P. Reynolds Jose Luis Araus
Journal of Experimental Botany
2016
https://academic.oup.com/jxb/article/67/9/2787/2877455
3
2
A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding
Maria Tattaris, Matthew P. Reynolds and Scott C. Chapman
Front. Plant Sci.2016
https://www.frontiersin.org/articles/10.3389/fpls.2016.01131/full
4
3
Modelling and genetic dissection of staygreen under heat stress
R. Suzuky Pinto, Marta S. Lopes, Nicholas C. Collins, and Matthew P. Reynolds
Theor Appl Genet.2016
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069319/
5
4
Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat
Viridiana Silva-Perez Gemma Molero Shawn P Serbin Anthony G Condon Matthew P ReynoldsRobert T Furbank John R Evans
Journal of Experimental Botany
2018
https://academic.oup.com/jxb/article/69/3/483/4772616
6
5
Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production
M. Fernanda Dreccer, Gemma Molero, Carolina Rivera-Amado, Carus John-Bejai, Zoe Wilson
Plant Science2018
https://www.sciencedirect.com/science/article/pii/S0168945217311585
7
6
Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions
H. Webber, J.W. White, B.A. Kimball, F. Ewert, S. Asseng, E.E. Rezaei, P.J. Pinter, J.L. Hatfield, M.P. Reynolds, B. Ababaei, M. Bindi, J. Doltra, R. Ferrise, H. Kage, B.T. Kassie, K.-C. Kersebaum, A. Luig, J.E. Olesen, M.A. Semenov, P. Stratonovitch, A.M. Ratjen, R.L. LaMorte, S.W. Leavitt, D.J. Hunsaker, G.W. Wall, P. Martre
Field Crops Research2018
https://www.sciencedirect.com/science/article/pii/S0378429017313011
8
7Genomic tools to assist breeding for drought tolerance
PeterLangridge, Matthew Preynolds
Current Opinion in Biotechnology
2015
https://www.sciencedirect.com/science/article/abs/pii/S0958166914002158
9
8Crop Radiation Capture and Use Efficiency
Erik Murchie, Matthew Reynolds
Encyclopedia of Sustainability Science and Technology
2012
https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-0851-3_171
10
9
Opportunities to reduce heat damage in rain-fed wheat crops based on plant breeding and agronomic management
J.R. Hunt, P.T. Hayman, R.A. Richards, J.B. Passioura
Field Crops Research2018
https://www.sciencedirect.com/science/article/pii/S0378429018300807
11
10
Unlocking the Genetic Diversity within A Middle-East Panel of Durum Wheat Landraces for Adaptation to Semi-arid Climate
Abu-Zaitoun, S.Y.; Chandrasekhar, K.; Assili, S.; Shtaya, M.J.; Jamous, R.M.; Mallah, O.B.; Nashef, K.; Sela, H.; Distelfeld, A.; Alhajaj, N.; Ali-Shtayeh, M.S.; Peleg, Z.; Ben-David, R.
Agronomy2018
https://www.mdpi.com/2073-4395/8/10/233?type=check_update&version=1
12
11
Variation in developmental patterns among elite wheat lines and relationships with yield, yield components and spike fertility
O.E. Gonzalez-Navarro, S. Griffiths, G. Molero, M.P. Reynolds, G.A. Slafer
Field Crops Research2016
https://www.sciencedirect.com/science/article/pii/S0378429016302295
13
12
Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery
Jiang Bian, Zhitao Zhang, Junying Chen, Haiying Chen, Chenfeng Cui, Xianwen Li, Shuobo Chen and Qiuping Fu
Remote Sensing2019https://www.mdpi.com/2072-4292/11/3/267/htm
14
13
Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms
Gerhards, M.; Schlerf, M.; Rascher, U.; Udelhoven, T.; Juszczak, R.; Alberti, G.; Miglietta, F.; Inoue, Y.
Remote Sensing2018https://www.mdpi.com/2072-4292/10/7/1139
15
14
Water stress detection in potato plants using leaf temperature, emissivity, and reflectance
Max Gerhards, Gilles Rock, Martin Schlerf, Thomas Udelhoven
International Journal of Applied Earth Observation and Geoinformation
2016
https://www.researchgate.net/publication/306038254_Water_stress_detection_in_potato_plants_using_leaf_temperature_emissivity_and_reflectance?_sg=ELHTWyDNRow6cERlLomxI21_EMaLy2IdYiSxgBtOQbMbMl_u1PQ4btv6oXcBwXVuX7hajA3-BorWoeZ-dVgHWhqTSzNV7lba4o0Uo_9b.saU-dPMe_yhjVP3Ffoyz0nqA7T1uL71beN7m_jrW4AlY4Tm_f5wDwuVrLtn6NrAJ5uJz5rBToxMs477_-KgNpg
16
15
Advanced Thermal Remote Sensing for Water Stress Detection of Agricultural Crops
Max F. Gerhards
Thesis for: DoctoralAdvisor: Thomas Udelhoven
2018
https://www.researchgate.net/publication/324532158_Advanced_Thermal_Remote_Sensing_for_Water_Stress_Detection_of_Agricultural_Crops?_sg=ELHTWyDNRow6cERlLomxI21_EMaLy2IdYiSxgBtOQbMbMl_u1PQ4btv6oXcBwXVuX7hajA3-BorWoeZ-dVgHWhqTSzNV7lba4o0Uo_9b.saU-dPMe_yhjVP3Ffoyz0nqA7T1uL71beN7m_jrW4AlY4Tm_f5wDwuVrLtn6NrAJ5uJz5rBToxMs477_-KgNpg
17
16
CropGIS – A web application for the spatial and temporal visualization of past, present and future crop biomass development
M. Machwitz, E. Hass, J. Junk, T. Udelhoven, and M. Schlerf
Computers and Electronics in Agriculture
2018
https://www.researchgate.net/publication/325126433_CropGIS_-_A_web_application_for_the_spatial_and_temporal_visualization_of_past_present_and_future_crop_biomass_development
18
17
The 2013 FLEX—US Airborne Campaign at the Parker Tract Loblolly Pine Plantation in North Carolina, USA
Middleton, E.M.; Rascher, U.; Corp, L.A.; Huemmrich, K.F.; Cook, B.D.; Noormets, A.; Schickling, A.; Pinto, F.; Alonso, L.; Damm, A.; Guanter, L.; Colombo, R.; Campbell, P.K.E.; Landis, D.R.; Zhang, Q.; Rossini, M.; Schuettemeyer, D.; Bianchi, R.
Remote Sensing2017https://www.mdpi.com/2072-4292/9/6/612
19
18
Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements
Stefan Thomas, Mirwaes Wahabzada, Matheus Thomas Kuska, Uwe Rascher and Anne-Katrin Mahlein
Functional Plant Biology
2016http://www.publish.csiro.au/fp/FP16127
20
19
A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops
C. Van der Tol, M. Rossini, S. Cogliati, W. Verhoef, R. Colombo, U. Rascher, G. Mohammed
Remote Sensing of Environment
2016
https://www.sciencedirect.com/science/article/pii/S0034425716303649?via%3Dihub
21
20
Fluspect-B: A model for leaf fluorescence, reflectance and transmittance spectra
Nastassia Vilfan, Christiaan van der Tol, Onno Muller, Uwe Rascher, Wouter Verhoef
Remote Sensing of Environment
2016
https://www.sciencedirect.com/science/article/pii/S0034425716303601?via%3Dihub
22
21
Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments
M. Pilar Cendrero-Mateo, M. Susan Moran, Shirley A. Papuga, K.R. Thorp, L. Alonso, J. Moreno, G. Ponce-Campos, U. Rascher, G. Wang,
Journal of Experimental Botany
2016
https://academic.oup.com/jxb/article/67/1/275/2885137
23
22
Sun‐induced chlorophyll fluorescence from high‐resolution imaging spectroscopy data to quantify spatio‐temporal patterns of photosynthetic function in crop canopies
Francisco Pinto, Alexander Damm, Anke Schickling, Cinzia Panigada, Sergio Cogliati, Mark Müller‐Linow, Agim Balvora, Uwe Rascher
Plant, Cell & Environment
2016
https://onlinelibrary.wiley.com/doi/full/10.1111/pce.12710
24
23
Improving remote estimation of winter crops gross ecosystem production by inclusion of leaf area index in a spectral model
Radosław Juszczak​, Bogna Uździcka, Marcin Stróżecki, Karolina Sakowska
PEER-REVIEWED Plant Biology section
2018https://peerj.com/articles/5613/
25
24
Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence
Peiqi Yang, Christiaan van der Tol, Wout Verhoef, Alexander Dammb, Anke Schickling, Thorsten Kraska, Onno Muller, Uwe Rascher
Remote Sensing of Environment
2018
https://www.sciencedirect.com/science/article/pii/S0034425718305480
26
25
Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean
Kovar, M.; Brestic, M.; Sytar, O.; Barek, V.; Hauptvogel, P.; Zivcak, M.
Water2019https://www.mdpi.com/2073-4441/11/3/443
27
26
Impact of warming and reduced precipitation on photosynthetic and remote sensing properties of peatland vegetation
Anshu Rastogi, Marcin Stróżecki , Hazem M.Kalaji, Dominika Łuców, Mariusz Lamentowicz, Radosław Juszczak
Environmental and Experimental Botany
2019
https://www.sciencedirect.com/science/article/pii/S0098847218316149
28
27
Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management
M. Prabhakar, Y. G. Prasad, Mahesh N. Rao
Crop Stress and its Management: Perspectives and Strategies
2011
https://link.springer.com/chapter/10.1007/978-94-007-2220-0_16
29
28
Detecting and Monitoring Plant Nutrient Stress Using Remote Sensing Approaches: A Review
Chong Yen Mee, Siva Kumar Balasundram and Ahmad Husni Mohd Hanif
Asian Journal of Plant Sciences
2017
https://scialert.net/fulltextmobile/?doi=ajps.2017.1.8
30
29
Water stress detection based on Optical Multisensor Fusion with a Least Squares Support Vector Machine Classifier
Dimitrios E. Moshou, Ioannis G. Gravalos, Dimitrios L. Kateris, Xanthoula-Eirini Pantazi
Biosystems Engineering
2014
https://www.sciencedirect.com/science/article/pii/S1537511013001153
31
30
A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding
G. Bai, Y. Ge, W. Hussain, P.S. Baenziger, G. Graef
Computers and Electronics in Agriculture
2016
https://www.sciencedirect.com/science/article/pii/S0168169916302289
32
31Optical Sensors Applied in Agricultural Crops
Fabrício Pinheiro Povh and Wagner de Paula Gusmão dos Anjos
Optical Sensors - New Developments and Practical Applications
2014
https://www.intechopen.com/books/optical-sensors-new-developments-and-practical-applications/optical-sensors-applied-in-agricultural-crops
33
32Sensor Fusion for Precision Agriculture
Viacheslav I. Adamchuk, Raphael A. Viscarra Rossel, Kenneth A. Sudduth and Peter Schulze Lammers
Sensor Fusion - Foundation and Applications
2011
https://www.intechopen.com/books/sensor-fusion-foundation-and-applications/sensor-fusion-for-precision-agriculture
34
33
Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review
Mohammad Raju Ahmed, Jannat Yasmin, Changyeun Mo, Hoonsoo Lee, Moon S. Kim, Soon-Jung Hong, Byoung-Kwan Cho
Journal of Biosystems Engineering
2016
https://www.e-sciencecentral.org/articles/SC000021738
35
34
Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
Anne-Katrin Mahlein
The American Phytopathological Society Journal
2016
https://apsjournals.apsnet.org/doi/full/10.1094/PDIS-03-15-0340-FE
36
35
Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System
Imran Ali Lakhiar, Gao Jianmin, Tabinda Naz Syed, Farman Ali Chandio, Noman Ali Buttar, and Waqar Ahmed Qureshi,
Journal of Sensors2018
https://www.hindawi.com/journals/js/2018/8672769/
37
36
Hyperspatial mapping of land surface water, energy and CO2 fluxes from Unmanned Aerial Systems.
Wang, Sheng
Kgs. Lyngby, Denmark: Technical University of Denmark (DTU)
2019
http://orbit.dtu.dk/ws/files/166318050/Thesis_online_version_Sheng_Wang.pdf
38
37
Potential of using satellite based vegetation indices and biophysical variables for the assessment of the water footprint of crops
Stancalie, G., Nertan A. T., Toulios, L., Spiliotopoulos, M.
Proceedings of SPIE - The International Society for Optical Engineering
2014
http://spie.org/Publications/Proceedings/Paper/10.1117/12.2066392
39
38
Identification of the key variables that can be estimated using remote sensing data and needed for Water Footprint (WF) assessment
Mireia Romaguera M., Toulios, L., Stancalie, G., Nertan, A., Spiliotopoulos, M., Struzik P., Calleja, E. J., Papadavid, G.
Proc. SPIE 9229, Second International Conference on Remote Sensing and Geoinformation of the Environment
2014
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9229/922912/Identification-of-the-key-variables-that-can-be-estimated-using/10.1117/12.2066120.short?SSO=1
40
39
Possibilities of Deriving Crop Evapotranspiration from Satellite Data with the Integration with Other Sources of Information
Stancalie, G. and Nertan, A.,
Evapotranspiration – Remote Sensing and Modeling
2012http://cdn.intechweb.org/pdfs/26116.pdf
41
40
Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management
F Baret, V Houlès, M Guérif
Journal of Experimental Botany
2007
https://academic.oup.com/jxb/article/58/4/869/428811
42
41
Thermal infra-red remote sensing for water stress estimation in agriculture
S. Labbé, V. Lebourgeois, A. Jolivot and R. Marti
The use of remote sensing and geographic information systems for irrigation management in Southwest Europe
2012http://om.ciheam.org/om/pdf/b67/00006607.pdf
43
42
The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system
Norbert Kirchgessner, Frank Liebisch , Kang Yu , Johannes Pfeifer , Michael Friedli , Andreas Hund and Achim Walter
Functional Plant Biology
2016https://www.publish.csiro.au/fp/FP16165
44
43
Water and nutrient management: the Austria case study of the FATIMA H2020 project
Vuolo, F., Essl, L., Zappa, L., Sandén, T., & Spiegel, H.
Advances in Animal Biosciences
2017
https://www.cambridge.org/core/journals/advances-in-animal-biosciences/article/water-and-nutrient-management-the-austria-case-study-of-the-fatima-h2020-project/D672088E856E29BF6B5CA57CAC8FCEEB
45
44
Temperature stress and redox homeostasis in agricultural crops
Awasthi R, Bhandari K and Nayyar H
Front. Environ. Sci.2015
https://www.frontiersin.org/articles/10.3389/fenvs.2015.00011/full
46
45
Experimental approach to detect water stress in ornamental plants using sUAS-imagery
Ana I. de Castro, Joe Mari Maja, Jim Owen, James Robbins, and Jose M. Peña
Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
2018
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10664/106640N/Experimental-approach-to-detect-water-stress-in-ornamental-plants-using/10.1117/12.2304739.short?SSO=1
47
46
A non-invasive plant-based probe for continuous monitoring of water stress in real time: a new tool for irrigation scheduling and deeper insight into drought and salinity stress physiology
Zimmermann, Ulrich, Bitter, Rebecca, Marchiori, Paulo Eduardo Ribeiro, Rüger, Simon, Ehrenberger, Wilhelm, Sukhorukov, Vladimir L., Schüttler, Annika, & Ribeiro, Rafael Vasconcelos.
Theoretical and Experimental Plant Physiology
2013
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2197-00252013000100002
48
47
UAVs challenge to assess water stress for sustainable agriculture
J. Gagoa, C. Douthe, R.E. Coopman, P.P. Gallego, M. Ribas-Carbo, J. Flexas, J. Escalona, H. Medrano
Agricultural Water Management
2015
http://www.ask-force.org/web/Precision-Biotechnology/Gago-UAVs-Challenge-Water-Stress-2015.pdf
49
48
Machine Learning for High-Throughput Stress Phenotyping in Plants
A. Singh, B. Ganapathysubramanian, A.K. Singh, S. Sarkar
Trends in Plant Science
2016
https://www.sciencedirect.com/science/article/pii/S1360138515002630?via%3Dihub
50
49
Synergy of optical and radar remote sensing in agricultural applications
Jiaguo Qi, Cuizhen Wang, Yoshio Inoue, Renduo Zhang, and Wei Gao
Proceedings of SPIE - The International Society for Optical Engineering
2004
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/5153/0000/Synergy-of-optical-and-radar-remote-sensing-in-agricultural-applications/10.1117/12.514562.short?SSO=1
51
50
Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges
Tobias B. Hank, Katja BergerHeike BachJan G. P. W. CleversAnatoly GitelsonPablo Zarco-TejadaWolfram Mauser
Surveys in Geophysics2018
https://link.springer.com/article/10.1007/s10712-018-9492-0
52
51
Remote sensing of agricultural drought monitoring: A state of art review
Khaled Hazaymeh, Quazi K. Hassan
Applications of remote sensing and Geographic Information Systems in environmental monitoring
2016
http://www.aimspress.com/article/10.3934/environsci.2016.4.604/fulltext.html
53
52Remote sensing imagery in vegetation mapping: a review
Yichun Xie, Zongyao Sha, Mei Yu
Journal of Plant Ecology
2008
https://academic.oup.com/jpe/article/1/1/9/1132900
54
53
Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements
Navarro, A.; Rolim, J.; Miguel, I.; Catalão, J.; Silva, J.; Painho, M.; Vekerdy, Z.
Remote sensing2016https://www.mdpi.com/2072-4292/8/6/525/htm
55
54
Toward an integrated approach to crop production and pollination ecology through the application of remote sensing
Willcox BK, Robson AJ, Howlett BG, Rader R.
PeerJ, Ecology section2018https://peerj.com/articles/5806/
56
55
Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges
Tripodi, P.; Massa, D.; Venezia, A.; Cardi, T.
Agronomy2018https://www.mdpi.com/2073-4395/8/4/57
57
56
Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series
Inglada, J.; Vincent, A.; Arias, M.; Marais-Sicre, C.
Remote sensing2016https://www.mdpi.com/2072-4292/8/5/362/htm
58
57
A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
Sofia Siachalou , Giorgos Mallinis and Maria Tsakiri-Strati
Remote sensing2015
https://pdfs.semanticscholar.org/2c41/388c8a548a9cfb5fdfb0a6b58267a48f75ee.pdf
59
58
Sensing crop nitrogen status with fluorescence indicators. A review
Nicolas Tremblay, Zhijie Wang, Zoran G. Cerovic
Agron. Sustain. Dev2012
https://hal.archives-ouvertes.fr/hal-00930513/document
60
59Remote sensing of nitrogen and water stress in wheat
A.K. Tilling, G.J. O'Leary, J.G. Ferwerda, S.D. Jones, G.J. Fitzgerald, D. Rodriguez, R. Belford
Field Crops Research2007
https://www.sciencedirect.com/science/article/pii/S0378429007001141
61
60Application of remote sensing methods in agriculture
Wójtowicz M., Wójtowicz A., Piekarczyk J.
COMMUNICATIONS IN BIOMETRY AND CROP SCIENCE
2016
http://agrobiol.sggw.waw.pl/~cbcs/articles/CBCS_11_1_3.pdf
62
61
Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar.
Kawamura, K.; Tsujimoto, Y.; Nishigaki, T.; Andriamananjara, A.; Rabenarivo, M.; Asai, H.; Rakotoson, T.; Razafimbelo, T.
Remote sensing2019https://www.mdpi.com/2072-4292/11/5/506
63
62
Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information.
Yuan, J.; Wang, X.; Yan, C.-X.; Wang, S.-R.; Ju, X.-P.; Li, Y.
Remote sensing2019https://www.mdpi.com/2072-4292/11/3/366
64
63Precision nitrogen management of wheat. A review.
Mariangela Diacono, Pietro Rubino, Francesco Montemurro
Agronomy for Sustainable Development
2012
https://hal.archives-ouvertes.fr/hal-01201355/document
65
64Sensors for measuring plant phenotyping: A review
Qiu, Ruicheng; Wei, Shuang; Zhang, Man; Li, Han; Sun, Hong; et al.
International Journal of Agricultural and Biological Engineering;
2018
http://www.ijabe.org/index.php/ijabe/article/view/2696/pdf
66
65
In-field methods for rapid detection of frost damage in Australian dryland wheat during the reproductive and grain-filling phase
Eileen M. Perry, James G. Nuttall, Ashley J. Wallace, Glenn J. Fitzgerald
Crop and Pasture Science
2017http://www.publish.csiro.au/cp/CP17135
67
66
Field spectroscopy for weed detection in wheat and chickpea fields
Shapira, Uri Herrmann, Ittai Karnieli, Arnon Bonfil, David J.
International journal of remote sensing
2013
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.714.2341&rep=rep1&type=pdf
68
67
In vivo diagnostics of early abiotic plant stress response via Raman spectroscopy
Narangerel Altangerel, Gombojav O. Ariunbold, Connor Gorman, Masfer H. Alkahtani, Eli J. Borrego, Dwight Bohlmeyer, Philip Hemmer, Michael V. Kolomiets, Joshua S. Yuan, and Marlan O. Scully
PNAS2017https://www.pnas.org/content/114/13/3393
69
68
SPECTRAL ANALYSIS OF AGRICULTURAL CROPS USING A UAV- MOUNTED MICRO-SPECTROMETER
Jacques Demange
University of Tasmania KGG355, - Spatial Research Project
2015
https://www.academia.edu/27360670/SPECTRAL_ANALYSIS_OF_AGRICULTURAL_CROPS_USING_A_UAV-_MOUNTED_MICRO-SPECTROMETER
70
69
Applications of hyperspectral image analysis for precision agriculture
Stanton L. Martin, Thomas George
Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X
2018
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10639/1063916/Applications-of-hyperspectral-image-analysis-for-precision-agriculture/10.1117/12.2303921.short?SSO=1
71
70Using Near-Infrared Spectroscopy in Agricultural Systems
Francisco García-Sánchez, Luis Galvez-Sola, Juan J. Martínez- Nicolás, Raquel Muelas-Domingo and Manuel Nieves
Developments in Near-Infrared SpectroscopyChapter: Using Near-Infrared Spectroscopy in Agricultural Systems
2017
https://www.intechopen.com/books/developments-in-near-infrared-spectroscopy/using-near-infrared-spectroscopy-in-agricultural-systems
72
71
Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany
GuidoWaldhoff, Ulrike Lussem, Georg Bareth
International Journal of Applied Earth Observation and Geoinformation
2017
https://www.sciencedirect.com/science/article/pii/S0303243417300934
73
72
A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics
Gohar Ghazaryan, Olena Dubovyk, Fabian Löw, Mykola Lavreniuk, Andrii Kolotii, Jürgen Schellberg, Nataliia Kussul.
European Journal of Remote Sensing
2018
https://www.tandfonline.com/doi/full/10.1080/22797254.2018.1455540
74
73
GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton
Yu Jiang, Changying Li, Jon S. Robertson, Shangpeng Sun, Rui Xu & Andrew H. Paterson
Scientific Reportsvolume
2018
https://www.nature.com/articles/s41598-018-19142-2
75
74
Earth Observation Imaging Spectroscopy for Terrestrial Systems: An Overview of Its History, Techniques, and Applications of Its Missions
Michael Rast, Thomas H. Painter
Surveys in Geophysics2019
https://www.researchgate.net/publication/331472520_Earth_Observation_Imaging_Spectroscopy_for_Terrestrial_Systems_An_Overview_of_Its_History_Techniques_and_Applications_of_Its_Missions
76
75
DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence
J. Gastellu-Etchegorry et al.,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2017https://ieeexplore.ieee.org/document/7900403
77
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Comparison of measurements and FluorMOD simulations for solar‐induced chlorophyll fluorescence and reflectance of a corn crop under nitrogen treatments
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