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

Chapter Contents (Back)
Classification. Maize Classification. Maize Yield. Corn Classification. Corn Yield.

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.

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

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

Abdi, A.M.[Abdulhakim M.],
Integrating Open Access Geospatial Data to Map the Habitat Suitability of the Declining Corn Bunting (Miliaria calandra),
IJGI(2), No. 4, 2013, pp. 935-954.
DOI Link 1310

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

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

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

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

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

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

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

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.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.
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

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

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

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

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

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

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

Cui, T.[Tianxiang], 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

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

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

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

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

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

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

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

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.
autonomous aerial vehicles, crops, feature extraction, geophysical image processing, image classification, skeleton-burr removal 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,
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,
Springer DOI 1611

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

Lu, H., Cao, Z., Xiao, Y., Fang, Z., Zhu, Y.,
Fine-grained maize cultivar identification using filter-specific convolutional activations,
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,
DOI Link 1404

Tilly, N., Hoffmeister, D., Schiedung, H., Hütt, C., Brands, J., Bareth, G.,
Terrestrial laser scanning for plant height measurement and biomass estimation of maize,
DOI Link 1404

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Sugar Cane Crop Analysis, Production, Detection, Health, Change .

Last update:Aug 16, 2018 at 18:22:30