22.2.8.9 Maize or Corn Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Maize Classification. Maize Yield. Corn Classification. Corn Yield.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

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

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.H.[Jia-Hua], Feng, L.L.[Li-Li], 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

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.J.[Shao-Jie], 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.D.[Zhi-Dan], Song, K.S.[Kai-Shan],
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

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

Bouchat, J.[Jean], Tronquo, E.[Emma], Orban, A.[Anne], Neyt, X.[Xavier], Verhoest, N.E.C.[Niko E. C.], Defourny, P.[Pierre],
Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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

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.H.[Song-Hao],
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.D.[Bao-Dong], 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.J.[Gui-Jun], Feng, H.K.[Hai-Kuan], Zhou, C.Q.[Cheng-Quan], Yang, H.[Hao], Xu, B.[Bo], Li, Z.H.[Zhen-Hai], 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

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.H.[Hui-Hui], Niu, Y.X.[Ya-Xiao], Han, W.T.[Wen-Ting],
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.F.[Bing-Fang], Zeng, H.W.[Hong-Wei], Zhang, X.[Xin], Xu, J.M.[Jia-Ming],
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.H.[Xiao-Han],
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
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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
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Wang, R.[Rui], Zhang, J.Q.[Ji-Quan], Wang, C.Y.[Chun-Yi], Guo, E.L.[En-Liang],
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.Y.[Tian-Ying], Zhang, X.D.[Xiao-Dong], Li, S.M.[Shao-Ming],
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

Jin, S., Su, Y., Gao, S., Wu, F., Ma, Q., Xu, K., Ma, Q., Hu, T., Liu, J., Pang, S., Guan, H., Zhang, J., Guo, Q.,
Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks,
GeoRS(58), No. 4, April 2020, pp. 2644-2658.
IEEE DOI 2004
Classification, deep learning, LiDAR, phenotype, segmentation, structural components BibRef

Ren, T.[Tianwei], Liu, Z.[Zhe], Zhang, L.[Lin], Liu, D.[Diyou], Xi, X.J.[Xiao-Jie], Kang, Y.H.[Yang-Hui], Zhao, Y.Y.[Yuan-Yuan], Zhang, C.[Chao], Li, S.M.[Shao-Ming], Zhang, X.D.[Xiao-Dong],
Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Liu, H.J.[Hao-Jie], Sun, H.[Hong], Li, M.Z.[Min-Zan], Iida, M.[Michihisa],
Application of Color Featuring and Deep Learning in Maize Plant Detection,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Sharma, A., Lang, R.H., Kurum, M., O'Neill, P.E., Cosh, M.H.,
L-Band Radar Experiment and Modeling of a Corn Canopy Over a Full Growing Season,
GeoRS(58), No. 8, August 2020, pp. 5821-5835.
IEEE DOI 2007
Backscatter, Dielectric measurement, L-band, Radar measurements, Spaceborne radar, Soil measurements, Coherent backscatter, L-band, radar BibRef

Barzin, R.[Razieh], Pathak, R.[Rohit], Lotfi, H.[Hossein], Varco, J.[Jac], Bora, G.C.[Ganesh C.],
Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
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Qiao, L.[Lang], Gao, D.H.[De-Hua], Zhang, J.[Junyi], Li, M.[Minzan], Sun, H.[Hong], Ma, J.[Junyong],
Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Chen, J.H.[Jing-Hua], Zhang, Q.[Qian], Chen, B.[Bin], Zhang, Y.G.[Yong-Guang], Ma, L.[Li], Li, Z.H.[Zhao-Hui], Zhang, X.K.[Xiao-Kang], Wu, Y.F.[Yun-Fei], Wang, S.Q.[Shao-Qiang], Mickler, R.A.[Robert A.],
Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Zan, X.L.[Xu-Li], Zhang, X.[Xinlu], Xing, Z.Y.[Zi-Yao], Liu, W.[Wei], Zhang, X.D.[Xiao-Dong], Su, W.[Wei], Liu, Z.[Zhe], Zhao, Y.Y.[Yuan-Yuan], Li, S.M.[Shao-Ming],
Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Meng, R.[Ran], Lv, Z.G.[Zhen-Gang], Yan, J.B.[Jian-Bing], Chen, G.S.[Geng-Shen], Zhao, F.[Feng], Zeng, L.L.[Ling-Lin], Xu, B.Y.[Bin-Yuan],
Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Guan, H.X.[Hai-Xiang], Liu, H.J.[Huan-Jun], Meng, X.T.[Xiang-Tian], Luo, C.[Chong], Bao, Y.L.[Yi-Lin], Ma, Y.Y.[Yu-Yang], Yu, Z.Y.[Zi-Yang], Zhang, X.L.[Xin-Le],
A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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Yu, L.H.[Li-Hong], Shang, J.L.[Jia-Li], Cheng, Z.Q.[Zhi-Qiang], Gao, Z.B.[Ze-Bin], Wang, Z.X.[Zi-Xin], Tian, L.[Luo], Wang, D.T.[Dan-Tong], Che, T.[Tao], Jin, R.[Rui], Liu, J.G.[Jian-Gui], Dong, T.F.[Tai-Feng], Qu, Y.H.[Yong-Hua],
Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Bi, K., Xiao, S., Gao, S., Zhang, C., Huang, N., Niu, Z.,
Estimating Vertical Chlorophyll Concentrations in Maize in Different Health States Using Hyperspectral LiDAR,
GeoRS(58), No. 11, November 2020, pp. 8125-8133.
IEEE DOI 2011
Laser radar, Vegetation mapping, Remote sensing, Distance measurement, Monitoring, Indexes, Biomedical monitoring, vertical distribution BibRef

Liu, J., Ferrazzoli, P., Guerriero, L., Bai, J., Liu, Q., Zhang, Z.,
Modeling Microwave Emission of Corn Crop Considering Leaf Shape and Orientation Under the Physical Optics Approximation,
GeoRS(58), No. 12, December 2020, pp. 8316-8331.
IEEE DOI 2012
Vegetation mapping, Microwave measurement, Shape, Microwave theory and techniques, Scattering, multiple scattering modeling BibRef

Zhang, J.[Junyi], Sun, H.[Hong], Gao, D.H.[De-Hua], Qiao, L.[Lang], Liu, N.[Ning], Li, M.[Minzan], Zhang, Y.[Yao],
Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Chen, X.X.[Xin-Xin], Feng, L.[Lan], Yao, R.[Rui], Wu, X.J.[Xiao-Jun], Sun, J.[Jia], Gong, W.[Wei],
Prediction of Maize Yield at the City Level in China Using Multi-Source Data,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Denis, A.[Antoine], Desclee, B.[Baudouin], Migdall, S.[Silke], Hansen, H.[Herbert], Bach, H.[Heike], Ott, P.[Pierre], Kouadio, A.L.[Amani Louis], Tychon, B.[Bernard],
Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zhu, B.X.[Bing-Xue], Chen, S.[Shengbo], Cao, Y.J.[Yi-Jing], Xu, Z.Y.[Zheng-Yuan], Yu, Y.[Yan], Han, C.[Cheng],
A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Niu, Y.X.[Ya-Xiao], Zhang, H.H.[Hui-Hui], Han, W.T.[Wen-Ting], Zhang, L.Y.[Li-Yuan], Chen, H.P.[Hai-Peng],
A Fixed-Threshold Method for Estimating Fractional Vegetation Cover of Maize under Different Levels of Water Stress,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Wu, B.[Bin], Ye, H.C.[Hui-Chun], Huang, W.J.[Wen-Jiang], Wang, H.Y.[Hong-Ye], Luo, P.L.[Pei-Lei], Ren, Y.[Yu], Kong, W.P.[Wei-Ping],
Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Skakun, S.[Sergii], Kalecinski, N.I.[Natacha I.], Brown, M.G.L.[Meredith G. L.], Johnson, D.M.[David M.], Vermote, E.F.[Eric F.], Roger, J.C.[Jean-Claude], Franch, B.[Belen],
Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Peng, X.S.[Xing-Shuo], Han, W.T.[Wen-Ting], Ao, J.Y.[Jian-Yi], Wang, Y.[Yi],
Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Yang, Y.J.[Yan-Jun], Tao, B.[Bo], Liang, L.[Liang], Huang, Y.[Yawen], Matocha, C.[Chris], Lee, C.D.[Chad D.], Sama, M.[Michael], El Masri, B.[Bassil], Ren, W.[Wei],
Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Mashaba-Munghemezulu, Z.[Zinhle], Chirima, G.J.[George Johannes], Munghemezulu, C.[Cilence],
Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wang, Z.X.[Zi-Xu], Nie, C.W.[Chen-Wei], Wang, H.W.[Hong-Wu], Ao, Y.[Yong], Jin, X.L.[Xiu-Liang], Yu, X.[Xun], Bai, Y.[Yi], Liu, Y.D.[Ya-Dong], Shao, M.C.[Ming-Chao], Cheng, M.[Minghan], Liu, S.B.[Shuai-Bing], Wang, S.[Siyu], Tuohuti, N.[Nuremanguli],
Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Adak, A.[Alper], Murray, S.C.[Seth C], Božinovic, S.[Sofija], Lindsey, R.[Regan], Nakasagga, S.[Shakirah], Chatterjee, S.[Sumantra], Anderson, S.L.[Steven L.], Wilde, S.[Scott],
Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Hu, X.Q.[Xue-Qian], Sun, L.[Lin], Gu, X.H.[Xiao-He], Sun, Q.[Qian], Wei, Z.H.[Zhong-Hui], Pan, Y.C.[Yu-Chun], Chen, L.P.[Li-Ping],
Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Xie, Q.H.[Qing-Hua], Wang, J.[Jinfei], Lopez-Sanchez, J.M.[Juan M.], Peng, X.[Xing], Liao, C.H.[Chun-Hua], Shang, J.L.[Jia-Li], Zhu, J.J.[Jian-Jun], Fu, H.[Haqiang], Ballester-Berman, J.D.[J. David],
Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Geng, L.Y.[Li-Ying], Che, T.[Tao], Ma, M.G.[Ming-Guo], Tan, J.L.[Jun-Lei], Wang, H.B.[Hai-Bo],
Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Tao, W.C.[Wan-Cheng], Xie, Z.X.[Zi-Xuan], Zhang, Y.[Ying], Li, J.[Jiayu], Xuan, F.[Fu], Huang, J.X.[Jian-Xi], Li, X.C.[Xue-Cao], Su, W.[Wei], Yin, D.Q.[Dong-Qin],
Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Chen, Y.[Yansi], Hou, J.[Jinliang], Huang, C.L.[Chun-Lin], Zhang, Y.[Ying], Li, X.H.[Xiang-Hua],
Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Karami, A.[Azam], Quijano, K.[Karoll], Crawford, M.[Melba],
Advancing Tassel Detection and Counting: Annotation and Algorithms,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Feng, Z.Z.[Zhuang-Zhuang], Zheng, X.M.[Xing-Ming], Li, L.[Lei], Li, B.Z.[Bing-Ze], Chen, S.[Si], Guo, T.H.[Tian-Hao], Wang, X.G.[Xi-Gang], Jiang, T.[Tao], Li, X.J.[Xiao-Jie], Li, X.F.[Xiao-Feng],
Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Chai, L.[Linna], Jiang, H.Y.[Hai-Ying], Crow, W.T.[Wade T.], Liu, S.M.[Shao-Min], Zhao, S.J.[Shao-Jie], Liu, J.[Jin], Yang, S.Q.[Shi-Qi],
Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC,
GeoRS(59), No. 10, October 2021, pp. 8168-8181.
IEEE DOI 2109
Vegetation mapping, Indexes, Moisture, Biological system modeling, Table lookup, Analytical models, Agriculture, vegetation water content (VWC) BibRef

Zhang, S.[Sha], Bai, Y.[Yun], Zhang, J.H.[Jia-Hua],
Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Ali, B.[Bitam], Zhao, F.[Feng], Li, Z.J.[Zhen-Jiang], Zhao, Q.C.[Qi-Chao], Gong, J.B.[Jia-Bei], Wang, L.[Lin], Tong, P.[Peng], Jiang, Y.H.[Yan-Hong], Su, W.[Wei], Bao, Y.F.[Yun-Fei], Li, J.[Juan],
Sensitivity Analysis of Canopy Structural and Radiative Transfer Parameters to Reconstructed Maize Structures Based on Terrestrial LiDAR Data,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Meng, L.H.[Ling-Hua], Liu, H.J.[Huan-Jun], Ustin, S.L.[Susan L.], Zhang, X.L.[Xin-Le],
Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Danilevicz, M.F.[Monica F.], Bayer, P.E.[Philipp E.], Boussaid, F.[Farid], Bennamoun, M.[Mohammed], Edwards, D.[David],
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Sunoj, S., Cho, J.[Jason], Guinness, J.[Joe], van Aardt, J.[Jan], Czymmek, K.J.[Karl J.], Ketterings, Q.M.[Quirine M.],
Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Khun, K.[Kosal], Tremblay, N.[Nicolas], Panneton, B.[Bernard], Vigneault, P.[Philippe], Lord, E.[Etienne], Cavayas, F.[François], Codjia, C.[Claude],
Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Ndlovu, H.S.[Helen S.], Odindi, J.[John], Sibanda, M.[Mbulisi], Mutanga, O.[Onisimo], Clulow, A.[Alistair], Chimonyo, V.G.P.[Vimbayi G. P.], Mabhaudhi, T.[Tafadzwanashe],
A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Barber, M.E.[Matías Ernesto], Rava, D.S.[David Sebastián], López-Martínez, C.[Carlos],
L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Gu, S.[Shujie], Liao, Q.[Qi], Gao, S.Y.[Shao-Yu], Kang, S.Z.[Shao-Zhong], Du, T.S.[Tai-Sheng], Ding, R.[Risheng],
Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Yu, H.[Huinan], Yin, G.[Gaofei], Liu, G.X.[Guo-Xiang], Ye, Y.X.[Yuan-Xin], Qu, Y.H.[Yong-Hua], Xu, B.D.[Bao-Dong], Verger, A.[Aleixandre],
Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Nazeri, B.[Behrokh], Crawford, M.[Melba],
Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Bi, K.Y.[Kai-Yi], Niu, Z.[Zheng], Xiao, S.[Shunfu], Bai, J.[Jie], Sun, G.[Gang], Wang, J.[Ji], Han, Z.[Zeying], Gao, S.[Shuai],
Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zan, X.L.[Xu-Li], Xing, Z.Y.[Zi-Yao], Gao, X.[Xiang], Liu, W.[Wei], Zhang, X.D.[Xiao-Dong], Liu, Z.[Zhe], Li, S.M.[Shao-Ming],
Risk Assessment of Different Maize (Zea mays L.) Lodging Types in the Northeast and the North China Plain Based on a Joint Probability Distribution Model,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Bi, K.Y.[Kai-Yi], Niu, Z.[Zheng], Xiao, S.F.[Shun-Fu], Bai, J.[Jie], Sun, G.[Gang], Wang, J.[Ji], Han, Z.[Zeying], Gao, S.[Shuai],
Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Barzin, R.[Razieh], Lotfi, H.[Hossein], Varco, J.J.[Jac J.], Bora, G.C.[Ganesh C.],
Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Nigon, T.[Tyler], Paiao, G.D.[Gabriel Dias], Mulla, D.J.[David J.], Fernández, F.G.[Fabián G.], Yang, C.[Ce],
The Influence of Aerial Hyperspectral Image Processing Workflow on Nitrogen Uptake Prediction Accuracy in Maize,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Guo, Y.H.[Ya-Hui], Chen, S.Z.[Shou-Zhi], Fu, Y.S.H.[Yong-Shuo H.], Xiao, Y.[Yi], Wu, W.X.[Wen-Xiang], Wang, H.X.[Han-Xi], de Beurs, K.[Kirsten],
Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Zhang, X.[Xuewei], Zhang, K.[Kefei], Sun, Y.[Yaqin], Zhao, Y.[Yindi], Zhuang, H.[Huifu], Ban, W.[Wei], Chen, Y.[Yu], Fu, E.[Erjiang], Chen, S.[Shuo], Liu, J.X.[Jin-Xiang], Hao, Y.[Yumeng],
Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Qiao, B.[Baiyu], He, X.[Xiongkui], Liu, Y.[Yajia], Zhang, H.[Hao], Zhang, L.[Lanting], Liu, L.M.[Li-Min], Reineke, A.J.[Alice-Jacqueline], Liu, W.X.[Wen-Xin], Müller, J.[Joachim],
Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Brewer, K.[Kiara], Clulow, A.[Alistair], Sibanda, M.[Mbulisi], Gokool, S.[Shaeden], Naiken, V.[Vivek], Mabhaudhi, T.[Tafadzwanashe],
Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Li, M.[Minhui], Shamshiri, R.R.[Redmond R.], Schirrmann, M.[Michael], Weltzien, C.[Cornelia], Shafian, S.[Sanaz], Laursen, M.S.[Morten Stigaard],
UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhou, H.[Huailin], Zhou, G.S.[Guang-Sheng], Song, X.Y.[Xing-Yang], He, Q.J.[Qi-Jin],
Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Shen, Q.[Qianxi], Niu, J.[Jun], Sivakumar, B.[Bellie], Lu, N.[Na],
Effects of Mulching on Maize Yield and Evapotranspiration in the Heihe River Basin, Northwest China,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Nieto, L.[Luciana], Houborg, R.[Rasmus], Zajdband, A.[Ariel], Jumpasut, A.[Arin], Prasad, P.V.V.[P. V. Vara], Olson, B.J.S.C.[Brad J. S. C.], Ciampitti, I.A.[Ignacio A.],
Impact of High-Cadence Earth Observation in Maize Crop Phenology Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Varela, S.[Sebastian], Pederson, T.L.[Taylor L.], Leakey, A.D.B.[Andrew D. B.],
Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Fan, Z.Q.[Zheng-Qiang], Sun, N.[Na], Qiu, Q.[Quan], Li, T.[Tao], Feng, Q.C.[Qing-Chun], Zhao, C.J.[Chun-Jiang],
In Situ Measuring Stem Diameters of Maize Crops with a High-Throughput Phenotyping Robot,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Chakhvashvili, E.[Erekle], Siegmann, B.[Bastian], Muller, O.[Onno], Verrelst, J.[Jochem], Bendig, J.[Juliane], Kraska, T.[Thorsten], Rascher, U.[Uwe],
Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Koutsos, T.M.[Thomas M.], Menexes, G.C.[Georgios C.], Eleftherohorinos, I.G.[Ilias G.],
The Use of Spatial Interpolation to Improve the Quality of Corn Silage Data in Case of Presence of Extreme or Missing Values,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Masiza, W.[Wonga], Chirima, J.G.[Johannes George], Hamandawana, H.[Hamisai], Kalumba, A.M.[Ahmed Mukalazi], Magagula, H.B.[Hezekiel Bheki],
A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Guo, Y.H.[Ya-Hui], Chen, S.Z.[Shou-Zhi], Li, X.X.[Xin-Xi], Cunha, M.[Mario], Jayavelu, S.[Senthilnath], Cammarano, D.[Davide], Fu, Y.[Yongshuo],
Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yang, B.[Bin], Zhu, W.[Wanxue], Rezaei, E.E.[Ehsan Eyshi], Li, J.[Jing], Sun, Z.G.[Zhi-Gang], Zhang, J.Q.[Jun-Qiang],
The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Olson, M.B.[Monica B.], Crawford, M.M.[Melba M.], Vyn, T.J.[Tony J.],
Hyperspectral Indices for Predicting Nitrogen Use Efficiency in Maize Hybrids,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Candiani, G.[Gabriele], Tagliabue, G.[Giulia], Panigada, C.[Cinzia], Verrelst, J.[Jochem], Picchi, V.[Valentina], Caicedo, J.P.R.[Juan Pablo Rivera], Boschetti, M.[Mirco],
Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Yang, H.Y.[Hong-Ye], Ming, B.[Bo], Nie, C.W.[Chen-Wei], Xue, B.B.[Bei-Bei], Xin, J.F.[Jiang-Feng], Lu, X.L.[Xing-Li], Xue, J.[Jun], Hou, P.[Peng], Xie, R.Z.[Rui-Zhi], Wang, K.[Keru], Li, S.[Shaokun],
Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Gao, M.[Min], Yang, F.[Fengbao], Wei, H.[Hong], Liu, X.X.[Xiao-Xia],
Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Chen, S.[Shuo], Liu, W.H.[Wei-Hang], Feng, P.[Puyu], Ye, T.[Tao], Ma, Y.[Yuchi], Zhang, Z.[Zhou],
Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Ma, Y.[Yining], Guga, S.[Suri], Xu, J.[Jie], Liu, X.[Xingpeng], Tong, Z.J.[Zhi-Jun], Zhang, J.[Jiquan],
Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Li, C.X.[Cheng-Xiu], Chimimba, E.G.[Ellasy Gulule], Kambombe, O.[Oscar], Brown, L.A.[Luke A.], Chibarabada, T.P.[Tendai Polite], Lu, Y.[Yang], Anghileri, D.[Daniela], Ngongondo, C.[Cosmo], Sheffield, J.[Justin], Dash, J.[Jadunandan],
Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Spišic, J.[Josip], Šimic, D.[Domagoj], Balen, J.[Josip], Jambrovic, A.[Antun], Galic, V.[Vlatko],
Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhu, Y.[Yan], Ludwig, E.M.[Elaina M.], Cherkauer, K.A.[Keith A.],
Estimation of Corn Latent Heat Flux from High Resolution Thermal Imagery,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Gao, J.[Jianmeng], Ding, M.L.[Ming-Liang], Sun, Q.[Qiuyu], Dong, J.[Jiayu], Wang, H.[Huanyi], Ma, Z.[Zhanhong],
Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Wei, J.[Jian], Ma, Q.[Qin], Wang, W.T.[Wei-Tao], Guo, H.[Hao], Liu, Z.[Zhe], Zhang, J.[Jiajing],
Abnormal area identification of corn ear based on semi-supervised learning,
IET-IPR(16), No. 9, 2022, pp. 2351-2360.
DOI Link 2206
BibRef

Khan, S.N.[Shahid Nawaz], Li, D.P.[Da-Peng], Maimaitijiang, M.[Maitiniyazi],
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Li, B.Z.[Bing-Ze], Ma, M.[Ming], Chen, S.[Shengbo], Li, X.F.[Xiao-Feng], Chen, S.[Si], Zheng, X.[Xingming],
Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Fan, J.H.[Jia-Hao], Zhou, J.[Jing], Wang, B.[Biwen], de Leon, N.[Natalia], Kaeppler, S.M.[Shawn M.], Lima, D.C.[Dayane C.], Zhang, Z.[Zhou],
Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wen, Y.[Yanan], Li, X.C.[Xue-Cao], Mu, H.W.[Hao-Wei], Zhong, L.[Liheng], Chen, H.[Han], Zeng, Y.[Yelu], Miao, S.X.[Shuang-Xi], Su, W.[Wei], Gong, P.[Peng], Li, B.G.[Bao-Guo], Huang, J.X.[Jian-Xi],
Mapping corn dynamics using limited but representative samples with adaptive strategies,
PandRS(190), 2022, pp. 252-266.
Elsevier DOI 2208
Corn classification, Annual dynamics, Crop Data Layers, Morphological processing, Landsat imagery BibRef

Li, H.[He], Wang, P.[Peng], Huang, C.[Chong],
Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Ma, Y.N.[Yi-Ning], Guga, S.[Suri], Xu, J.[Jie], Liu, X.P.[Xing-Peng], Tong, Z.J.[Zhi-Jun], Zhang, J.[Jiquan],
Evaluation of Drought Vulnerability of Maize and Influencing Factors in Songliao Plain Based on the SE-DEA-Tobit Model,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Rolle, M.[Matteo], Tamea, S.[Stefania], Claps, P.[Pierluigi], Ayari, E.[Emna], Baghdadi, N.[Nicolas], Zribi, M.[Mehrez],
Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Guo, R.[Rui], Zhu, X.F.[Xiu-Fang], Zhang, C.[Ce], Cheng, C.X.[Chang-Xiu],
Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Xu, C.[Chi], Ding, Y.L.[Yan-Ling], Zheng, X.[Xingming], Wang, Y.Q.[Ye-Qiao], Zhang, R.[Rui], Zhang, H.Y.[Hong-Yan], Dai, Z.[Zewen], Xie, Q.[Qiaoyun],
A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yang, B.[Bing], Wu, S.[Sensen], Yan, Z.[Zhen],
Effects of Climate Change on Corn Yields: Spatiotemporal Evidence from Geographically and Temporally Weighted Regression Model,
IJGI(11), No. 8, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Ahmad, A.[Aanis], Aggarwal, V.[Varun], Saraswat, D.[Dharmendra], El Gamal, A.[Aly], Johal, G.S.[Gurmukh S.],
GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Mota-Delfin, C.[Canek], López-Canteñs, G.D.[Gilberto De_Jesús], López-Cruz, I.L.[Irineo Lorenzo], Romantchik-Kriuchkova, E.[Eugenio], Olguín-Rojas, J.C.[Juan Carlos],
Detection and Counting of Corn Plants in the Presence of Weeds with Convolutional Neural Networks,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Siqueira, R.[Rafael], Mandal, D.[Dipankar], Longchamps, L.[Louis], Khosla, R.[Raj],
Assessing Nitrogen Variability at Early Stages of Maize Using Mobile Fluorescence Sensing,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Luo, P.[Peilei], Ye, H.C.[Hui-Chun], Huang, W.J.[Wen-Jiang], Liao, J.J.[Jing-Juan], Jiao, Q.J.[Quan-Jun], Guo, A.[Anting], Qian, B.X.[Bin-Xiang],
Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Sudu, B.[Bilige], Rong, G.Z.[Guang-Zhi], Guga, S.[Suri], Li, K.[Kaiwei], Zhi, F.[Feng], Guo, Y.[Ying], Zhang, J.[Jiquan], Bao, Y.L.[Yu-Long],
Retrieving SPAD Values of Summer Maize Using UAV Hyperspectral Data Based on Multiple Machine Learning Algorithm,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Xu, X.[Xingmei], Wang, L.[Lu], Shu, M.[Meiyan], Liang, X.[Xuewen], Ghafoor, A.Z.[Abu Zar], Liu, Y.[Yunling], Ma, Y.T.[Yun-Tao], Zhu, J.[Jinyu],
Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Olson, M.B.[Monica B.], Crawford, M.M.[Melba M.], Vyn, T.J.[Tony J.],
Predicting Nitrogen Efficiencies in Mature Maize with Parametric Models Employing In-Season Hyperspectral Imaging,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Nandan, R.[Rohit], Bandaru, V.[Varaprasad], He, J.Y.[Jia-Ying], Daughtry, C.[Craig], Gowda, P.[Prasanna], Suyker, A.E.[Andrew E.],
Evaluating Optical Remote Sensing Methods for Estimating Leaf Area Index for Corn and Soybean,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef


Cai, E.[Enyu], Luo, Z.[Zhankun], Baireddy, S.[Sriram], Guo, J.Q.[Jia-Qi], Yang, C.[Changye], Delp, E.J.[Edward J.],
High-Resolution UAV Image Generation for Sorghum Panicle Detection,
AgriVision22(1675-1684)
IEEE DOI 2210
Training, Deep learning, Head, Image synthesis, Training data, Autonomous aerial vehicles BibRef

Stylianou, A.[Abby], Pless, R.[Robert], Shakoor, N.[Nadia], Mockler, T.[Todd],
Classification and Visualization of Genotype-Phenotype Interactions in Biomass Sorghum,
CVPPA21(1352-1361)
IEEE DOI 2112
Training, Visualization, Pipelines, Genetics, Biomass BibRef

Mudereri, B.T., Abdel-Rahman, E.M., Dube, T., Landmann, T., Niassy, S., Tonnang, H.E.Z., Khan, Z.R.,
Potential of Resampled Multispectral Data for Detecting Desmodium-brachiaria Intercropped With Maize In A 'push-pull' System,
ISPRS20(B3:1017-1022).
DOI Link 2012
BibRef

Mufungizi, A.A., Musakwa, W., Gumbo, T.,
A Land Suitability Analysis of the Vhembe District, South Africa, The Case of Maize and Sorghum,
ISPRS20(B3:1023-1030).
DOI Link 2012
BibRef

Stroppiana, D., Pepe, M., Boschetti, M., Crema, A., Candiani, G., Giordan, D., Baldo, M., Allasia, P., Monopoli, L.,
Estimating Crop Density From Multi-spectral UAV Imagery in Maize Crop,
UAV-g19(619-624).
DOI Link 1912
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

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 General Issue, Land Use, Land Cover continues in
Sugar Cane Crop Analysis, Production, Detection, Health, Change .


Last update:Dec 4, 2022 at 15:58:45