Sakamoto, T.,
Wardlow, B.D.,
Gitelson, A.A.,
Detecting Spatiotemporal Changes of Corn Developmental Stages in the
U.S. Corn Belt Using MODIS WDRVI Data,
GeoRS(49), No. 6, June 2011, pp. 1926-1936.
IEEE DOI
1106
BibRef
Taghvaeian, S.,
Chávez, J.,
Hansen, N.,
Infrared Thermometry to Estimate Crop Water Stress Index and Water Use
of Irrigated Maize in Northeastern Colorado,
RS(4), No. 11, November 2012, pp. 3619-3637.
DOI Link
1211
BibRef
Shen, Y.,
Wu, L.,
Di, L.,
Yu, G.,
Tang, H.,
Yu, G.,
Shao, Y.,
Hidden Markov Models for Real-Time Estimation of Corn Progress Stages
Using MODIS and Meteorological Data,
RS(5), No. 4, April 2013, pp. 1734-1753.
DOI Link
1305
BibRef
Udelhoven, T.,
Delfosse, P.,
Bossung, C.,
Ronellenfitsch, F.,
Mayer, F.,
Schlerf, M.,
Machwitz, M.,
Hoffmann, L.,
Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral
Remote Sensing,
RS(5), No. 1, January 2013, pp. 254-273.
DOI Link
1302
BibRef
Guerriero, L.,
Pierdicca, N.,
Pulvirenti, L.,
Ferrazzoli, P.,
Use of Satellite Radar Bistatic Measurements for Crop Monitoring:
A Simulation Study on Corn Fields,
RS(5), No. 2, February 2013, pp. 864-890.
DOI Link
1303
BibRef
Cheng, Y.B.[Yen-Ben],
Middleton, E.M.[Elizabeth M.],
Zhang, Q.Y.[Qing-Yuan],
Huemmrich, K.F.[Karl F.],
Campbell, P.K.E.[Petya K. E.],
Corp, L.A.[Lawrence A.],
Cook, B.D.[Bruce D.],
Kustas, W.P.[William P.],
Daughtry, C.S.[Craig S.],
Integrating Solar Induced Fluorescence and the Photochemical
Reflectance Index for Estimating Gross Primary Production in
a Cornfield,
RS(5), No. 12, 2013, pp. 6857-6879.
DOI Link
1412
BibRef
Rossini, M.,
Fava, F.,
Cogliati, S.,
Meroni, M.,
Marchesi, A.,
Panigada, C.,
Giardino, C.,
Busetto, L.,
Migliavacca, M.,
Amaducci, S.,
Colombo, R.,
Assessing canopy PRI from airborne imagery to map water stress in
maize,
PandRS(86), No. 1, 2013, pp. 168-177.
Elsevier DOI
1312
Hyperspectral
BibRef
Zhang, J.[Jiahua],
Feng, L.[Lili],
Yao, F.M.[Feng-Mei],
Improved maize cultivated area estimation over a large scale
combining MODIS-EVI time series data and crop phenological
information,
PandRS(94), No. 1, 2014, pp. 102-113.
Elsevier DOI
1407
MODIS imagery
BibRef
Quemada, M.[Miguel],
Gabriel, J.L.[Jose Luis],
Zarco-Tejada, P.[Pablo],
Airborne Hyperspectral Images and Ground-Level Optical Sensors As
Assessment Tools for Maize Nitrogen Fertilization,
RS(6), No. 4, 2014, pp. 2940-2962.
DOI Link
1405
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
Geipel, J.[Jakob],
Link, J.[Johanna],
Claupein, W.[Wilhelm],
Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial
Images and Crop Surface Models Acquired with an Unmanned Aircraft
System,
RS(6), No. 11, 2014, pp. 10335-10355.
DOI Link
1412
BibRef
Wang, Q.[Qi],
Chai, L.[Linna],
Zhao, S.[Shaojie],
Zhang, Z.J.[Zhong-Jun],
Gravimetric Vegetation Water Content Estimation for Corn Using L-Band
Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area
Index,
RS(7), No. 8, 2015, pp. 10543.
DOI Link
1509
BibRef
van Emmerik, T.,
Steele-Dunne, S.C.,
Judge, J.,
van de Giesen, N.,
Impact of Diurnal Variation in Vegetation Water Content on Radar
Backscatter From Maize During Water Stress,
GeoRS(53), No. 7, July 2015, pp. 3855-3869.
IEEE DOI
1503
Backscatter
BibRef
Rossini, M.[Micol],
Panigada, C.[Cinzia],
Cilia, C.[Chiara],
Meroni, M.[Michele],
Busetto, L.[Lorenzo],
Cogliati, S.[Sergio],
Amaducci, S.[Stefano],
Colombo, R.[Roberto],
Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence
and Canopy Temperature Airborne Maps,
IJGI(4), No. 2, 2015, pp. 626-646.
DOI Link
1505
BibRef
Jin, X.L.[Xiu-Liang],
Ma, J.H.[Jian-Hang],
Wen, Z.[Zhidan],
Song, K.[Kaishan],
Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral
Information and Textural Features,
RS(7), No. 11, 2015, pp. 14559.
DOI Link
1512
BibRef
Zhang, F.[Feng],
Zhou, G.S.[Guang-Sheng],
Estimation of Canopy Water Content by Means of Hyperspectral Indices
Based on Drought Stress Gradient Experiments of Maize in the North
Plain China,
RS(7), No. 11, 2015, pp. 15203.
DOI Link
1512
BibRef
Bériaux, E.[Emilie],
Waldner, F.[François],
Collienne, F.[François],
Bogaert, P.[Patrick],
Defourny, P.[Pierre],
Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time
Series Using the Water Cloud Model,
RS(7), No. 12, 2015, pp. 15818.
DOI Link
1601
BibRef
Garrido, M.[Miguel],
Paraforos, D.S.[Dimitris S.],
Reiser, D.[David],
Arellano, M.V.[Manuel Vázquez],
Griepentrog, H.W.[Hans W.],
Valero, C.[Constantino],
3D Maize Plant Reconstruction Based on Georeferenced Overlapping
LiDAR Point Clouds,
RS(7), No. 12, 2015, pp. 15870.
DOI Link
1601
BibRef
Kelly, D.[Derek],
Vatsa, A.[Avimanyou],
Mayham, W.[Wade],
Kazic, T.[Toni],
Extracting complex lesion phenotypes in Zea mays,
MVA(27), No. 1, January 2016, pp. 145-156.
Springer DOI
1601
Diseases in maize crops.
BibRef
Crommelinck, S.[Sophie],
Höfle, B.[Bernhard],
Simulating an Autonomously Operating Low-Cost Static Terrestrial
LiDAR for Multitemporal Maize Crop Height Measurements,
RS(8), No. 3, 2016, pp. 205.
DOI Link
1604
BibRef
Durgun, Y.Ö.[Yetkin Özüm],
Gobin, A.[Anne],
Gilliams, S.[Sven],
Duveiller, G.[Grégory],
Tychon, B.[Bernard],
Testing the Contribution of Stress Factors to Improve Wheat and Maize
Yield Estimations Derived from Remotely-Sensed Dry Matter
Productivity,
RS(8), No. 3, 2016, pp. 170.
DOI Link
1604
BibRef
Wang, R.[Ruoyu],
Cherkauer, K.[Keith],
Bowling, L.[Laura],
Corn Response to Climate Stress Detected with Satellite-Based NDVI
Time Series,
RS(8), No. 4, 2016, pp. 269.
DOI Link
1604
BibRef
Cheng, Z.Q.[Zhi-Qiang],
Meng, J.[Jihua],
Wang, Y.M.[Yi-Ming],
Improving Spring Maize Yield Estimation at Field Scale by
Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a
New Method with Fast Algorithms,
RS(8), No. 4, 2016, pp. 303.
DOI Link
1604
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
Peralta, N.R.[Nahuel R.],
Assefa, Y.[Yared],
Du, J.[Juan],
Barden, C.J.[Charles J.],
Ciampitti, I.A.[Ignacio A.],
Mid-Season High-Resolution Satellite Imagery for Forecasting
Site-Specific Corn Yield,
RS(8), No. 10, 2016, pp. 848.
DOI Link
1609
BibRef
Maresma, Á.[Ángel],
Ariza, M.[Mar],
Martínez, E.[Elías],
Lloveras, J.[Jaume],
Martínez-Casasnovas, J.A.[José A.],
Analysis of Vegetation Indices to Determine Nitrogen Application and
Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service,
RS(8), No. 12, 2016, pp. 973.
DOI Link
1612
BibRef
Wang, C.[Cheng],
Nie, S.[Sheng],
Xi, X.H.[Xiao-Huan],
Luo, S.[Shezhou],
Sun, X.F.[Xiao-Feng],
Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link
1702
BibRef
Xie, D.,
Qin, W.,
Wang, P.,
Shuai, Y.,
Zhou, Y.,
Zhu, Q.,
Influences of Leaf-Specular Reflection on Canopy BRF Characteristics:
A Case Study of Real Maize Canopies With a 3-D Scene BRDF Model,
GeoRS(55), No. 2, February 2017, pp. 619-631.
IEEE DOI
1702
crops
BibRef
Ban, H.Y.[Ho-Young],
Kim, K.S.[Kwang Soo],
Park, N.W.[No-Wook],
Lee, B.W.[Byun-Woo],
Using MODIS Data to Predict Regional Corn Yields,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link
1702
BibRef
Cerrudo, D.[Diego],
Pérez, L.G.[Lorena González],
Lugo, J.A.M.[José Alberto Mendoza],
Trachsel, S.[Samuel],
Stay-Green and Associated Vegetative Indices to Breed Maize Adapted
to Heat and Combined Heat-Drought Stresses,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link
1704
BibRef
Gnädinger, F.[Friederike],
Schmidhalter, U.[Urs],
Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs),
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Chou, S.[Shuren],
Chen, J.M.[Jing M.],
Yu, H.[Hua],
Chen, B.[Bin],
Zhang, X.[Xiuying],
Croft, H.[Holly],
Khalid, S.[Shoaib],
Li, M.[Meng],
Shi, Q.[Qin],
Canopy-Level Photochemical Reflectance Index from Hyperspectral
Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early
Indicators of Water Stress in Maize,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Yu, B.[Bing],
Shang, S.[Songhao],
Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation
District of China with High Spatial and Temporal Resolution
Vegetation Index Series,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Chu, T.X.[Tian-Xing],
Starek, M.J.[Michael J.],
Brewer, M.J.[Michael J.],
Murray, S.C.[Seth C.],
Pruter, L.S.[Luke S.],
Assessing Lodging Severity over an Experimental Maize (Zea mays L.)
Field Using UAS Images,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link
1711
BibRef
Zhao, J.[Jing],
Li, J.[Jing],
Liu, Q.H.[Qin-Huo],
Wang, H.Y.[Hong-Yan],
Chen, C.[Chen],
Xu, B.[Baodong],
Wu, S.[Shanlong],
Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX
Sensor Data for Leaf Area Index Estimations for Maize,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link
1802
BibRef
Cui, T.X.[Tian-Xiang],
Sun, R.[Rui],
Qiao, C.[Chen],
Zhang, Q.A.[Qi-Ang],
Yu, T.[Tao],
Liu, G.[Gang],
Liu, Z.G.[Zhi-Gang],
Estimating Diurnal Courses of Gross Primary Production for Maize:
A Comparison of Sun-Induced Chlorophyll Fluorescence, Light-Use
Efficiency and Process-Based Models,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
BibRef
Martín, C.S.[Carolina San],
Milne, A.E.[Alice E.],
Webster, R.[Richard],
Storkey, J.[Jonathan],
Andújar, D.[Dionisio],
Fernández-Quintanilla, C.[Cesar],
Dorado, J.[José],
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop,
IJGI(7), No. 2, 2018, pp. xx-yy.
DOI Link
1802
BibRef
Tang, K.[Ke],
Zhu, W.Q.[Wen-Quan],
Zhan, P.[Pei],
Ding, S.[Siyang],
An Identification Method for Spring Maize in Northeast China Based on
Spectral and Phenological Features,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Makanza, R.[Richard],
Zaman-Allah, M.[Mainassara],
Cairns, J.E.[Jill E.],
Magorokosho, C.[Cosmos],
Tarekegne, A.[Amsal],
Olsen, M.[Mike],
Prasanna, B.M.[Boddupalli M.],
High-Throughput Phenotyping of Canopy Cover and Senescence in Maize
Field Trials Using Aerial Digital Canopy Imaging,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Gracia-Romero, A.[Adrian],
Vergara-Díaz, O.[Omar],
Thierfelder, C.[Christian],
Cairns, J.E.[Jill E.],
Kefauver, S.C.[Shawn C.],
Araus, J.L.[José L.],
Phenotyping Conservation Agriculture Management Effects on Ground and
Aerial Remote Sensing Assessments of Maize Hybrids Performance in
Zimbabwe,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Maresma, Á.[Ángel],
Ariza, M.[Mar],
Martínez, E.[Elías],
Lloveras, J.[Jaume],
Martínez-Casasnovas, J.A.[José A.],
Erratum: Maresma, A., et al. Analysis of Vegetation Indices to
Determine Nitrogen Application and Yield Prediction in Maize (Zea
mays L.) from a Standard UAV Service. Remote Sens. 2017, 9, 648,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Varela, S.[Sebastian],
Dhodda, P.R.[Pruthvidhar Reddy],
Hsu, W.H.[William H.],
Prasad, P.V.V.[P. V. Vara],
Assefa, Y.[Yared],
Peralta, N.R.[Nahuel R.],
Griffin, T.[Terry],
Sharda, A.[Ajay],
Ferguson, A.[Allison],
Ciampitti, I.A.[Ignacio A.],
Early-Season Stand Count Determination in Corn via Integration of
Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning
Techniques,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Maresma, Á.[Ángel],
Lloveras, J.[Jaume],
Martínez-Casasnovas, J.A.[José A.],
Use of Multispectral Airborne Images to Improve In-Season Nitrogen
Management, Predict Grain Yield and Estimate Economic Return of Maize
in Irrigated High Yielding Environments,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link
1805
BibRef
Zhou, C.,
Yang, G.,
Liang, D.,
Yang, X.,
Xu, B.,
An Integrated Skeleton Extraction and Pruning Method for Spatial
Recognition of Maize Seedlings in MGV and UAV Remote Images,
GeoRS(56), No. 8, August 2018, pp. 4618-4632.
IEEE DOI
1808
autonomous aerial vehicles, crops, feature extraction,
geophysical image processing, image classification,
skeleton-burr removal
BibRef
Monsivais-Huertero, A.,
Liu, P.,
Judge, J.,
Phenology-Based Backscattering Model for Corn at L-Band,
GeoRS(56), No. 9, September 2018, pp. 4989-5005.
IEEE DOI
1809
Soil, Backscatter, Vegetation mapping, L-band, Scattering,
Rough surfaces, Surface roughness, Coherent scattering model,
phenology-based backscatter model
BibRef
Xu, S.[Shan],
Liu, Z.G.[Zhi-Gang],
Zhao, L.[Liang],
Zhao, H.R.[Hua-Rong],
Ren, S.X.[San-Xue],
Diurnal Response of Sun-Induced Fluorescence and PRI to Water Stress
in Maize Using a Near-Surface Remote Sensing Platform,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Han, L.[Liang],
Yang, G.[Guijun],
Feng, H.[Haikuan],
Zhou, C.Q.[Cheng-Quan],
Yang, H.[Hao],
Xu, B.[Bo],
Li, Z.[Zhenhai],
Yang, X.D.[Xiao-Dong],
Quantitative Identification of Maize Lodging-Causing Feature Factors
Using Unmanned Aerial Vehicle Images and a Nomogram Computation,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Michez, A.[Adrien],
Bauwens, S.[Sébastien],
Brostaux, Y.[Yves],
Hiel, M.P.[Marie-Pierre],
Garré, S.[Sarah],
Lejeune, P.[Philippe],
Dumont, B.[Benjamin],
How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production?
A 3D and Multitemporal Modeling Approach Applied to Zea mays,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link
1812
BibRef
Mananze, S.[Sosdito],
Pôças, I.[Isabel],
Cunha, M.[Mario],
Retrieval of Maize Leaf Area Index Using Hyperspectral and
Multispectral Data,
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
Wang, Y.J.[Yong-Jian],
Wen, W.L.[Wei-Liang],
Wu, S.[Sheng],
Wang, C.Y.[Chuan-Yu],
Yu, Z.[Zetao],
Guo, X.Y.[Xin-Yu],
Zhao, C.J.[Chun-Jiang],
Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View
Stereo Reconstruction, and 3D Digitizing Estimates,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Jin, S.,
Su, Y.,
Wu, F.,
Pang, S.,
Gao, S.,
Hu, T.,
Liu, J.,
Guo, Q.,
Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual
Maize Using Terrestrial LiDAR Data,
GeoRS(57), No. 3, March 2019, pp. 1336-1346.
IEEE DOI
1903
agriculture, crops, feature extraction, image segmentation,
optical radar, vectors, individual levels,
skeleton
BibRef
Zhang, L.Y.[Li-Yuan],
Zhang, H.[Huihui],
Niu, Y.[Yaxiao],
Han, W.[Wenting],
Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Alehegn, E.[Enquhone],
Ethiopian maize diseases recognition and classification using support
vector machine,
IJCVR(9), No. 1, 2019, pp. 90-109.
DOI Link
1903
BibRef
Su, W.[Wei],
Huang, J.X.[Jian-Xi],
Liu, D.S.[De-Sheng],
Zhang, M.Z.[Ming-Zheng],
Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat
Imagery and Terrestrial LiDAR Data,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Tian, F.[Fuyou],
Wu, B.[Bingfang],
Zeng, H.W.[Hong-Wei],
Zhang, X.[Xin],
Xu, J.[JiaMing],
Efficient Identification of Corn Cultivation Area with Multitemporal
Synthetic Aperture Radar and Optical Images in the Google Earth
Engine Cloud Platform,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
BibRef
Zhang, M.Z.[Ming-Zheng],
Zhu, D.H.[De-Hai],
Su, W.[Wei],
Huang, J.X.[Jian-Xi],
Zhang, X.D.[Xiao-Dong],
Liu, Z.[Zhe],
Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth
Monitoring,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Niu, Y.X.[Ya-Xiao],
Zhang, L.Y.[Li-Yuan],
Zhang, H.H.[Hui-Hui],
Han, W.T.[Wen-Ting],
Peng, X.S.[Xing-Shuo],
Estimating Above-Ground Biomass of Maize Using Features Derived from
UAV-Based RGB Imagery,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Gilcher, M.[Mario],
Ruf, T.[Thorsten],
Emmerling, C.[Christoph],
Udelhoven, T.[Thomas],
Remote Sensing Based Binary Classification of Maize. Dealing with
Residual Autocorrelation in Sparse Sample Situations,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Baup, F.[Frédéric],
Ameline, M.[Maël],
Fieuzal, R.[Rémy],
Frappart, F.[Frédéric],
Corgne, S.[Samuel],
Berthoumieu, J.F.[Jean-François],
Temporal Evolution of Corn Mass Production Based on
Agro-Meteorological Modelling Controlled by Satellite Optical and SAR
Images,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Su, W.[Wei],
Zhang, M.Z.[Ming-Zheng],
Bian, D.H.[Da-Hong],
Liu, Z.[Zhe],
Huang, J.X.[Jian-Xi],
Wang, W.[Wei],
Wu, J.Y.[Jia-Yu],
Guo, H.[Hao],
Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Su, W.[Wei],
Sun, Z.P.[Zhong-Ping],
Chen, W.H.[Wen-Hua],
Zhang, X.D.[Xiao-Dong],
Yao, C.[Chan],
Wu, J.[Jiayu],
Huang, J.X.[Jian-Xi],
Zhu, D.[Dehai],
Joint Retrieval of Growing Season Corn Canopy LAI and Leaf
Chlorophyll Content by Fusing Sentinel-2 and MODIS Images,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Tang, J.D.[Jian-Dong],
Han, W.T.[Wen-Ting],
Zhang, L.Y.[Li-Yuan],
UAV Multispectral Imagery Combined with the FAO-56 Dual Approach for
Maize Evapotranspiration Mapping in the North China Plain,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Myers, E.[Emily],
Kerekes, J.[John],
Daughtry, C.[Craig],
Russ, A.[Andrew],
Assessing the Impact of Satellite Revisit Rate on Estimation of Corn
Phenological Transition Timing through Shape Model Fitting,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Zhu, W.X.[Wan-Xue],
Sun, Z.G.[Zhi-Gang],
Peng, J.B.[Jin-Bang],
Huang, Y.[Yaohuan],
Li, J.[Jing],
Zhang, J.Q.A.[Jun-Qi-Ang],
Yang, B.[Bin],
Liao, X.[Xiaohan],
Estimating Maize Above-Ground Biomass Using 3D Point Clouds of
Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Kayad, A.[Ahmed],
Sozzi, M.[Marco],
Gatto, S.[Simone],
Marinello, F.[Francesco],
Pirotti, F.[Francesco],
Monitoring Within-Field Variability of Corn Yield using Sentinel-2
and Machine Learning Techniques,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Zhang, L.L.[Liang-Liang],
Zhang, Z.[Zhao],
Luo, Y.[Yuchuan],
Cao, J.[Juan],
Tao, F.[Fulu],
Combining Optical, Fluorescence, Thermal Satellite, and Environmental
Data to Predict County-Level Maize Yield in China Using Machine
Learning Approaches,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Wang, R.[Rui],
Zhang, J.[Jiquan],
Wang, C.[Chunyi],
Guo, E.[Enliang],
Characteristic Analysis of Droughts and Waterlogging Events for Maize
Based on a New Comprehensive Index through Coupling of Multisource
Data in Midwestern Jilin Province, China,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Liu, Y.L.[Yun-Ling],
Cen, C.J.[Chao-Jun],
Che, Y.P.[Ying-Pu],
Ke, R.[Rui],
Ma, Y.[Yan],
Ma, Y.T.[Yun-Tao],
Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Sakamoto, T.[Toshihiro],
Incorporating environmental variables into a MODIS-based crop yield
estimation method for United States corn and soybeans through the use
of a random forest regression algorithm,
PandRS(160), 2020, pp. 208-228.
Elsevier DOI
2001
MODIS, NLDAS-2, Phenology, Machine learning, WDRVI
BibRef
Zhang, L.[Lin],
Liu, Z.[Zhe],
Ren, T.[Tianwei],
Liu, D.[Diyou],
Ma, Z.[Zhe],
Tong, L.[Liang],
Zhang, C.[Chao],
Zhou, T.[Tianying],
Zhang, X.D.[Xiao-Dong],
Li, S.[Shaoming],
Identification of Seed Maize Fields With High Spatial Resolution and
Multiple Spectral Remote Sensing Using Random Forest Classifier,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Lewis-Beck, C.[Colin],
Walker, V.A.[Victoria A.],
Niemi, J.[Jarad],
Caragea, P.[Petruta],
Hornbuckle, B.K.[Brian K.],
Extracting Agronomic Information from SMOS Vegetation Optical Depth
in the US Corn Belt Using a Nonlinear Hierarchical Model,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
Soil Moisture and Ocean Salinity data. Analyze stage of growth.
BibRef
Sibanda, M.,
Mutanga, O.,
Dube, T.,
Odindi, J.,
Mafongoya, P.L.,
Usability, Strength and Practicality of The Upcoming Hyspiri In
Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 Msi, Venµs
And Landsat 8 Oli Spectral Band-settings,
SMPR19(1015-1022).
DOI Link
1912
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
Campos, Y.[Yerania],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Sossa, H.[Humberto],
Pajares, G.[Gonzalo],
Vegetation Segmentation in Cornfield Images Using Bag of Words,
ACIVS16(193-204).
Springer DOI
1611
BibRef
Dahms, T.[Thorsten],
Seissiger, S.[Sylvia],
Conrad, C.[Christopher],
Borg, E.[Erik],
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series,
ISPRS16(B2: 171-175).
DOI Link
1610
See also Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat.
BibRef
Hütt, C.,
Tilly, N.,
Schiedung, H.,
Bareth, G.,
Potential Of Multitemporal Tandem-x Derived Crop Surface Models For
Maize Growth Monitoring,
ISPRS16(B7: 803-808).
DOI Link
1610
BibRef
Lu, H.,
Cao, Z.,
Xiao, Y.,
Fang, Z.,
Zhu, Y.,
Fine-grained maize cultivar identification using filter-specific
convolutional activations,
ICIP16(3718-3722)
IEEE DOI
1610
Agriculture
BibRef
Hütt, C.,
Schiedung, H.,
Tilly, N.,
Bareth, G.,
Fusion of high resolution remote sensing images and terrestrial laser
scanning for improved biomass estimation of maize,
Thematic14(101-108).
DOI Link
1404
BibRef
Tilly, N.,
Hoffmeister, D.,
Schiedung, H.,
Hütt, C.,
Brands, J.,
Bareth, G.,
Terrestrial laser scanning for plant height measurement and biomass
estimation of maize,
Thematic14(181-187).
DOI Link
1404
BibRef
Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Sugar Cane Crop Analysis, Production, Detection, Health, Change .