22.2.7.5 Wheat Crop Analysis, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Wheat Classification. Wheat Yield. Includes similar grains, barley, rye, etc.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Haralick, R.M.[Robert M.], Hlavka, C.A., Carlyle, S.M., and Yokoyama, R.,
The Discrimination of Winter Wheat Using a Growth-State Signature,
RSE(9), 1980, pp. 277-294. BibRef 8000

Sun, C.M.[Chang-Ming], Berman, M.[Mark], Coward, D.[David], Osborne, B.[Brian],
Thickness measurement and crease detection of wheat grains using stereo vision,
PRL(28), No. 12, 1 September 2007, pp. 1501-1508.
Elsevier DOI 0707
Grain thickness measurement; Grain crease detection; Stereo vision BibRef

Hosoi, F.[Fumiki], Omasa, K.[Kenji],
Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging,
PandRS(64), No. 2, March 2009, pp. 151-158.
Elsevier DOI 0903
Carbon stock; Plant area density; Portable scanning lidar; Three-dimensional imaging; Voxel-based canopy profiling BibRef

Koppe, W.[Wolfgang], Gnyp, M.L.[Martin L.], Hennig, S.D.[Simon D.], Li, F.[Fei], Miao, Y.X.[Yu-Xin], Chen, X.P.[Xin-Ping], Jia, L.L.[Liang-Liang], Bareth, G.[Georg],
Multi-Temporal Hyperspectral and Radar Remote Sensing for Estimating Winter Wheat Biomass in the North China Plain,
PFG(2012), No. 3, 2012, pp. 281-298.
WWW Link. 1211
BibRef

Koppe, W.[Wolfgang], Li, F.[Fei], Gnyp, M.L.[Martin L.], Miao, Y.X.[Yu-Xin], Jia, L.L.[Liang-Liang], Chen, X.P.[Xin-Ping], Zhang, F.[Fusuo], Bareth, G.[Georg],
Evaluating Multispectral and Hyperspectral Satellite Remote Sensing Data for Estimating Winter Wheat Growth Parameters at Regional Scale in the North China Plain,
PFG(2010), No. 3, 2010, pp. 167-178.
WWW Link. 1211
BibRef

Meroni, M., Marinho, E., Sghaier, N., Verstrate, M., Leo, O.,
Remote Sensing Based Yield Estimation in a Stochastic Framework: Case Study of Durum Wheat in Tunisia,
RS(5), No. 2, February 2013, pp. 539-557.
DOI Link 1303
BibRef

Cammarano, D.[Davide], Fitzgerald, G.J.[Glenn J.], Casa, R.[Raffaele], Basso, B.[Bruno],
Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments,
RS(6), No. 4, 2014, pp. 2827-2844.
DOI Link 1405
Nitrogen. BibRef

Li, F.[Fei], Mistele, B.[Bodo], Hu, Y.C.[Yun-Cai], Chen, X.[Xinping], Schmidhalter, U.[Urs],
Optimising three-band spectral indices to assess aerial N concentration, N uptake and aboveground biomass of winter wheat remotely in China and Germany,
PandRS(92), No. 1, 2014, pp. 112-123.
Elsevier DOI 1407
Band selection BibRef

Yuan, L.[Lin], Zhang, J.C.[Jing-Cheng], Shi, Y.[Yeyin], Nie, C.W.[Chen-Wei], Wei, L.G.[Li-Guang], Wang, J.[Jihua],
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image,
RS(6), No. 5, 2014, pp. 3611-3623.
DOI Link 1407
BibRef

Ashourloo, D.[Davoud], Mobasheri, M.R.[Mohammad Reza], Huete, A.[Alfredo],
Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina),
RS(6), No. 6, 2014, pp. 4723-4740.
DOI Link 1407
BibRef

Vincini, M., Amaducci, S., Frazzi, E.,
Empirical Estimation of Leaf Chlorophyll Density in Winter Wheat Canopies Using Sentinel-2 Spectral Resolution,
GeoRS(52), No. 6, June 2014, pp. 3220-3235.
IEEE DOI 1403
BibRef
And: Corrections: GeoRS(52), No. 6, June 2014, pp. 3753-3753.
IEEE DOI 1403
Equations Agriculture BibRef

Ashourloo, D.[Davoud], Mobasheri, M.R.[Mohammad Reza], Huete, A.[Alfredo],
Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements,
RS(6), No. 6, 2014, pp. 5107-5123.
DOI Link 1407
BibRef

Dempewolf, J.[Jan], Adusei, B.[Bernard], Becker-Reshef, I.[Inbal], Hansen, M.[Matthew], Potapov, P.[Peter], Khan, A.[Ahmad], Barker, B.[Brian],
Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics,
RS(6), No. 10, 2014, pp. 9653-9675.
DOI Link 1411
BibRef

Bendig, J.[Juliane], Bolten, A.[Andreas], Bennertz, S.[Simon], Broscheit, J.[Janis], Eichfuss, S.[Silas], Bareth, G.[Georg],
Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging,
RS(6), No. 11, 2014, pp. 10395-10412.
DOI Link 1412
BibRef

Brocks, S.[Sebastian], Bareth, G.[Georg],
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Burkart, A.[Andreas], Aasen, H.[Helge], Alonso, L.[Luis], Menz, G.[Gunter], Bareth, G.[Georg], Rascher, U.[Uwe],
Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer,
RS(7), No. 1, 2015, pp. 725-746.
DOI Link 1502
BibRef

Hernandez, J.[Javier], Lobos, G.A.[Gustavo A.], Matus, I.[Iván], del Pozo, A.[Alejandro], Silva, P.[Paola], Galleguillos, M.[Mauricio],
Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes,
RS(7), No. 2, 2015, pp. 2109-2126.
DOI Link 1503
BibRef

Ali, M.[Muhammad], Montzka, C.[Carsten], Stadler, A.[Anja], Menz, G.[Gunter], Thonfeld, F.[Frank], Vereecken, H.[Harry],
Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany),
RS(7), No. 3, 2015, pp. 2808-2831.
DOI Link 1504
BibRef

Hank, T.B.[Tobias B.], Bach, H.[Heike], Mauser, W.[Wolfram],
Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe,
RS(7), No. 4, 2015, pp. 3934-3965.
DOI Link 1505
BibRef

Tanaka, S.[Shinya], Kawamura, K.[Kensuke], Maki, M.[Masayasu], Muramoto, Y.[Yasunori], Yoshida, K.[Kazuaki], Akiyama, T.[Tsuyoshi],
Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan,
RS(7), No. 5, 2015, pp. 5329-5346.
DOI Link 1506
BibRef

Gonzalez-Dugo, V.[Victoria], Hernandez, P.[Pilar], Solis, I.[Ignacio], Zarco-Tejada, P.J.[Pablo J.],
Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping,
RS(7), No. 10, 2015, pp. 13586.
DOI Link 1511
BibRef

Jin, X.L.[Xiu-Liang], Yang, G.J.[Gui-Jun], Xu, X.G.[Xin-Gang], Yang, H.[Hao], Feng, H.K.[Hai-Kuan], Li, Z.H.[Zhen-Hai], Shen, J.X.[Jia-Xiao], Lan, Y.B.[Yu-Bin], Zhao, C.J.[Chun-Jiang],
Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data,
RS(7), No. 10, 2015, pp. 13251.
DOI Link 1511
BibRef

Siegmann, B.[Bastian], Jarmer, T.[Thomas], Beyer, F.[Florian], Ehlers, M.[Manfred],
The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI,
RS(7), No. 10, 2015, pp. 12737.
DOI Link 1511
BibRef

Tilly, N.[Nora], Aasen, H.[Helge], Bareth, G.[George],
Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass,
RS(7), No. 9, 2015, pp. 11449.
DOI Link 1511
BibRef
And: Correction: RS(7), No. 12, 2015, pp. 15878.
DOI Link 1601
BibRef

Jin, N.[Ning], Tao, B.[Bo], Ren, W.[Wei], Feng, M.C.[Mei-Chen], Sun, R.[Rui], He, L.[Liang], Zhuang, W.[Wei], Yu, Q.A.[Qi-Ang],
Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data,
RS(8), No. 3, 2016, pp. 207.
DOI Link 1604
BibRef

Liu, L., Liu, X., Wang, Z., Zhang, B.,
Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies,
GeoRS(54), No. 5, May 2016, pp. 2640-2651.
IEEE DOI 1604
geophysical techniques BibRef

Boyle, R.D., Corke, F.M.K., Doonan, J.H.,
Automated estimation of tiller number in wheat by ribbon detection,
MVA(27), No. 5, July 2016, pp. 637-646.
Springer DOI 1608
BibRef

Schirrmann, M.[Michael], Giebel, A.[Antje], Gleiniger, F.[Franziska], Pflanz, M.[Michael], Lentschke, J.[Jan], Dammer, K.H.[Karl-Heinz],
Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery,
RS(8), No. 9, 2016, pp. 706.
DOI Link 1610
BibRef

Schirrmann, M.[Michael], Hamdorf, A.[André], Giebel, A.[Antje], Gleiniger, F.[Franziska], Pflanz, M.[Michael], Dammer, K.H.[Karl-Heinz],
Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Zheng, Y.[Yang], Zhang, M.[Miao], Zhang, X.[Xin], Zeng, H.W.[Hong-Wei], Wu, B.[Bingfang],
Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products,
RS(8), No. 10, 2016, pp. 824.
DOI Link 1609
BibRef

Zhang, X.[Xin], Zhang, M.[Miao], Zheng, Y.[Yang], Wu, B.F.[Bing-Fang],
Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China,
RS(8), No. 11, 2016, pp. 915.
DOI Link 1612
BibRef

Jain, M.[Meha], Srivastava, A.K.[Amit K.], Balwinder-Singh, Joon, R.K.[Rajiv K.], McDonald, A.[Andrew], Royal, K.[Keitasha], Lisaius, M.C.[Madeline C.], Lobell, D.B.[David B.],
Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data,
RS(8), No. 10, 2016, pp. 860.
DOI Link 1609
BibRef

Song, X.[Xiao], Feng, W.[Wei], He, L.[Li], Xu, D.Y.[Duan-Yang], Zhang, H.Y.[Hai-Yan], Li, X.[Xiao], Wang, Z.J.[Zhi-Jie], Coburn, C.A.[Craig A.], Wang, C.Y.[Chen-Yang], Guo, T.C.[Tian-Cai],
Examining view angle effects on leaf N estimation in wheat using field reflectance spectroscopy,
PandRS(122), No. 1, 2016, pp. 57-67.
Elsevier DOI 1612
Wheat BibRef

Li, Z.H.[Zhen-Hai], Wang, J.[Jihua], Xu, X.G.[Xin-Gang], Zhao, C.J.[Chun-Jiang], Jin, X.L.[Xiu-Liang], Yang, G.J.[Gui-Jun], Feng, H.[Haikuan],
Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation,
RS(7), No. 9, 2015, pp. 12400.
DOI Link 1511
BibRef

Jin, X.L.[Xiu-Liang], Kumar, L.[Lalit], Li, Z.H.[Zhen-Hai], Xu, X.G.[Xin-Gang], Yang, G.J.[Gui-Jun], Wang, J.H.[Ji-Hua],
Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data,
RS(8), No. 12, 2016, pp. 972.
DOI Link 1612
BibRef

Holman, F.H.[Fenner H.], Riche, A.B.[Andrew B.], Michalski, A.[Adam], Castle, M.[March], Wooster, M.J.[Martin J.], Hawkesford, M.J.[Malcolm J.],
High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing,
RS(8), No. 12, 2016, pp. 1031.
DOI Link 1612
BibRef

Qiu, B.W.[Bing-Wen], Luo, Y.H.[Yu-Han], Tang, Z.H.[Zheng-Hong], Chen, C.C.[Chong-Cheng], Lu, D.F.[Di-Fei], Huang, H.Y.[Hong-Yu], Chen, Y.Z.[Yun-Zhi], Chen, N.[Nan], Xu, W.M.[Wei-Ming],
Winter wheat mapping combining variations before and after estimated heading dates,
PandRS(123), No. 1, 2017, pp. 35-46.
Elsevier DOI 1612
Time series analysis BibRef

Zhao, C., Li, H., Li, P., Yang, G., Gu, X., Lan, Y.,
Effect of Vertical Distribution of Crop Structure and Biochemical Parameters of Winter Wheat on Canopy Reflectance Characteristics and Spectral Indices,
GeoRS(55), No. 1, January 2017, pp. 236-247.
IEEE DOI 1701
vegetation BibRef

Goulas, Y.[Yves], Fournier, A.[Antoine], Daumard, F.[Fabrice], Champagne, S.[Sébastien], Ounis, A.[Abderrahmane], Marloie, O.[Olivier], Moya, I.[Ismael],
Gross Primary Production of a Wheat Canopy Relates Stronger to Far Red Than to Red Solar-Induced Chlorophyll Fluorescence,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Dahms, T.[Thorsten], Seissiger, S.[Sylvia], Borg, E.[Erik], Vajen, H.[Hermann], Fichtelmann, B.[Bernd], Conrad, C.[Christopher],
Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat,
PFG(2016), No. 5-6, 2016, pp. 285-299.
DOI Link 1703

See also Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series. BibRef

Jin, X.L.[Xiu-Liang], Li, Z.H.[Zhen-Hai], Yang, G.J.[Gui-Jun], Yang, H.[Hao], Feng, H.[Haikuan], Xu, X.G.[Xin-Gang], Wang, J.[Jihua], Li, X.C.[Xin-Chuan], Luo, J.[Juhua],
Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm,
PandRS(126), No. 1, 2017, pp. 24-37.
Elsevier DOI 1704
Optical spectral vegetation indices (OSVIs) BibRef

Du, M.M.[Meng-Meng], Noguchi, N.[Noboru],
Monitoring of Wheat Growth Status and Mapping of Wheat Yield's within-Field Spatial Variations Using Color Images Acquired from UAV-camera System,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Silvestro, P.C.[Paolo Cosmo], Pignatti, S.[Stefano], Pascucci, S.[Simone], Yang, H.[Hao], Li, Z.[Zhenhai], Yang, G.J.[Gui-Jun], Huang, W.J.[Wen-Jiang], Casa, R.[Raffaele],
Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Liu, Z.J.[Zheng-Jia], Wu, C.Y.[Chao-Yang], Liu, Y.S.[Yan-Sui], Wang, X.Y.[Xiao-Yue], Fang, B.[Bin], Yuan, W.P.[Wen-Ping], Ge, Q.S.[Quan-Sheng],
Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain,
PandRS(130), No. 1, 2017, pp. 81-91.
Elsevier DOI 1708
Spring, phenology BibRef

Yue, J.[Jibo], Yang, G.J.[Gui-Jun], Li, C.C.[Chang-Chun], Li, Z.H.[Zhen-Hai], Wang, Y.J.[Yan-Jie], Feng, H.K.[Hai-Kuan], Xu, B.[Bo],
Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Li, H.[He], Chen, Z.X.[Zhong-Xin], Liu, G.H.[Gao-Huan], Jiang, Z.W.[Zhi-Wei], Huang, C.[Chong],
Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Yue, J.[Jibo], Feng, H.[Haikuan], Yang, G.[Guijun], Li, Z.[Zhenhai],
A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Liu, T.[Tao], Li, R.[Rui], Jin, X.[Xiuliang], Ding, J.F.[Jin-Feng], Zhu, X.[Xinkai], Sun, C.M.[Cheng-Ming], Guo, W.[Wenshan],
Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Yao, X.[Xia], Wang, N.[Ni], Liu, Y.[Yong], Cheng, T.[Tao], Tian, Y.[Yongchao], Chen, Q.[Qi], Zhu, Y.[Yan],
Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Zhou, C.Q.[Cheng-Quan], Liang, D.[Dong], Yang, X.D.[Xiao-Dong], Xu, B.[Bo], Yang, G.[Guijun],
Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Jin, X.[Xiu], Jie, L.[Lu], Wang, S.[Shuai], Qi, H.J.[Hai Jun], Li, S.W.[Shao Wen],
Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Khan, A.[Ahmad], Hansen, M.C.[Matthew C.], Potapov, P.V.[Peter V.], Adusei, B.[Bernard], Pickens, A.[Amy], Krylov, A.[Alexander], Stehman, S.V.[Stephen V.],
Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Shi, Y.[Yue], Huang, W.J.[Wen-Jiang], González-Moreno, P.[Pablo], Luke, B.[Belinda], Dong, Y.Y.[Ying-Ying], Zheng, Q.[Qiong], Ma, H.Q.[Hui-Qin], Liu, L.[Linyi],
Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host-Pathogen Interaction of Yellow Rust on Wheat,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Hassan, M.A.[Muhammad Adeel], Yang, M.J.[Meng-Jiao], Rasheed, A.[Awais], Jin, X.L.[Xiu-Liang], Xia, X.C.[Xian-Chun], Xiao, Y.G.[Yong-Gui], He, Z.H.[Zhong-Hu],
Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Sui, J.[Juan], Qin, Q.M.[Qi-Ming], Ren, H.Z.[Hua-Zhong], Sun, Y.H.[Yuan-Heng], Zhang, T.Y.[Tian-Yuan], Wang, J.D.[Jian-Dong], Gong, S.H.[Shi-Hong],
Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Khan, Z.[Zohaib], Chopin, J.[Joshua], Cai, J.H.[Jin-Hai], Eichi, V.R.[Vahid-Rahimi], Haefele, S.[Stephan], Miklavcic, S.J.[Stanley J.],
Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef
And: Erratum: RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Liu, W.W.[Wei-Wei], Huang, J.F.[Jing-Feng], Wei, C.W.[Chuan-Wen], Wang, X.Z.[Xiu-Zhen], Mansaray, L.R.[Lamin R.], Han, J.[Jiahui], Zhang, D.D.[Dong-Dong], Chen, Y.[Yaoliang],
Mapping Water-Logging Damage on Winter Wheat at Parcel Level Using High Spatial Resolution Satellite Data,
PandRS(142), 2018, pp. 243-256.
Elsevier DOI 1807
Winter wheat, Water-logging, Parcel scale, High resolution satellite data, Biophysical parameters
See also Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions. BibRef

Liu, J.H.[Jian-Hong], Zhu, W.Q.[Wen-Quan], Atzberger, C.[Clement], Zhao, A.Z.[An-Zhou], Pan, Y.Z.[Yao-Zhong], Huang, X.[Xin],
A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Becker-Reshef, I.[Inbal], Franch, B.[Belen], Barker, B.[Brian], Murphy, E.[Emilie], Santamaria-Artigas, A.[Andres], Humber, M.[Michael], Skakun, S.[Sergii], Vermote, E.[Eric],
Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Meyer, T.[Thomas], Weihermüller, L.[Lutz], Vereecken, H.[Harry], Jonard, F.[François],
Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Chen, H.[Hanyue], Huang, W.J.[Wen-Jiang], Li, W.[Wang], Niu, Z.[Zheng], Zhang, L.M.[Li-Ming], Xing, S.[Shihe],
Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Kuester, T.[Theres], Spengler, D.[Daniel],
Structural and Spectral Analysis of Cereal Canopy Reflectance and Reflectance Anisotropy,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Kanning, M.[Martin], Kühling, I.[Insa], Trautz, D.[Dieter], Jarmer, T.[Thomas],
High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zhang, Y.[Yao], Qin, Q.M.[Qi-Ming], Ren, H.Z.[Hua-Zhong], Sun, Y.H.[Yuan-Heng], Li, M.Z.[Min-Zan], Zhang, T.Y.[Tian-Yuan], Ren, S.L.[Shi-Long],
Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

El Hajj, M.[Mohammad], Baghdadi, N.[Nicolas], Bazzi, H.[Hassan], Zribi, M.[Mehrez],
Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Liu, L.Y.[Lin-Yi], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Du, X.P.[Xiao-Ping], Luo, J.[Juhua], Shi, Y.[Yue], Ma, H.Q.[Hui-Qin],
Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

He, L., Coburn, C.A., Wang, Z., Feng, W., Guo, T.,
Reduced Prediction Saturation and View Effects for Estimating the Leaf Area Index of Winter Wheat,
GeoRS(57), No. 3, March 2019, pp. 1637-1652.
IEEE DOI 1903
crops, soil, vegetation mapping, reduced prediction saturation, view effects, leaf area index, winter wheat, vegetation indices, LAI, winter wheat BibRef

Yue, J.[Jibo], Yang, G.[Guijun], Tian, Q.J.[Qing-Jiu], Feng, H.[Haikuan], Xu, K.[Kaijian], Zhou, C.Q.[Cheng-Quan],
Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices,
PandRS(150), 2019, pp. 226-244.
Elsevier DOI 1903
Unmanned aerial vehicle, Vegetation indices, Ultrahigh ground-resolution image, Image textures, Reproductive growth stages BibRef

Song, Y.[Yang], Wang, J.[Jing],
Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

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Wang, Y.M.[Yu-Miao], Zhang, Z.[Zhou], Feng, L.[Luwei], Du, Q.Y.[Qing-Yun], Runge, T.[Troy],
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Cao, J.[Juan], Zhang, Z.[Zhao], Tao, F.[Fulu], Zhang, L.L.[Liang-Liang], Luo, Y.C.[Yu-Chuan], Han, J.C.[Ji-Chong], Li, Z.[Ziyue],
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Xing, N.C.[Nai-Chen], Huang, W.J.[Wen-Jiang], Xie, Q.Y.[Qiao-Yun], Shi, Y.[Yue], Ye, H.C.[Hui-Chun], Dong, Y.Y.[Ying-Ying], Wu, M.Q.[Ming-Quan], Sun, G.[Gang], Jiao, Q.J.[Quan-Jun],
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Xing, N.C.[Nai-Chen], Huang, W.J.[Wen-Jiang], Ye, H.C.[Hui-Chun], Ren, Y.[Yu], Xie, Q.Y.[Qiao-Yun],
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Agriculture, Machine learning, Actual yield, Yield Gap, Remote sensing, National scale, Regression BibRef

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Landsat, Crop yield, Gross primary productivity, Crop variety, Light use efficiency BibRef

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Li, F.[Feng], Zhang, C.M.[Cheng-Ming], Zhang, W.W.[Wen-Wen], Xu, Z.G.[Zhi-Gang], Wang, S.[Shouyi], Sun, G.[Genyun], Wang, Z.J.[Zhen-Jie],
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Hu, Y.C.[Yun-Cai], Knapp, S.[Samuel], Schmidhalter, U.[Urs],
Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles,
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Murphy, M.E.[Mary E.], Boruff, B.[Bryan], Callow, J.N.[J. Nikolaus], Flower, K.C.[Ken C.],
Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing,
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Song, Y.[Yang], Wang, J.[Jing], Yu, Q.A.[Qi-Ang], Huang, J.[Jianxi],
Using MODIS LAI Data to Monitor Spatio-Temporal Changes of Winter Wheat Phenology in Response to Climate Warming,
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Wang, S.Y.[Shou-Yi], Xu, Z.G.[Zhi-Gang], Zhang, C.M.[Cheng-Ming], Zhang, J.H.[Jing-Han], Mu, Z.S.[Zhong-Shan], Zhao, T.Y.[Tian-Yu], Wang, Y.Y.[Yuan-Yuan], Gao, S.[Shuai], Yin, H.[Hao], Zhang, Z.Y.[Zi-Yun],
Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field,
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Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling,
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Tewes, A.[Andreas], Hoffmann, H.[Holger], Nolte, M.[Manuel], Krauss, G.[Gunther], Schäfer, F.[Fabian], Kerkhoff, C.[Christian], Gaiser, T.[Thomas],
How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?,
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Dong, Q.[Qi], Chen, X.H.[Xue-Hong], Chen, J.[Jin], Zhang, C.S.[Chi-Shan], Liu, L.[Licong], Cao, X.[Xin], Zang, Y.Z.[Yun-Ze], Zhu, X.F.[Xiu-Fang], Cui, X.H.[Xi-Hong],
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Chen, S.[Shi], Fan, L.L.[Ling-Ling], Liang, S.[Shefang], Chen, H.[Hao], Sun, X.[Xiao], Hu, Y.[Yanan], Liu, Z.[Zhenhuan], Sun, J.[Jing], Yang, P.[Peng],
Spatiotemporal Dynamics of the Northern Limit of Winter Wheat in China Using MODIS Time Series Images,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
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Vavlas, N.C.[Nikolaos-Christos], Waine, T.W.[Toby W.], Meersmans, J.[Jeroen], Burgess, P.J.[Paul J.], Fontanelli, G.[Giacomo], Richter, G.M.[Goetz M.],
Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
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Zhou, X.F.[Xian-Feng], Zhang, J.C.[Jing-Cheng], Chen, D.M.[Dong-Mei], Huang, Y.[Yanbo], Kong, W.P.[Wei-Ping], Yuan, L.[Lin], Ye, H.[Huichun], Huang, W.J.[Wen-Jiang],
Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
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Upreti, D.[Deepak], Pignatti, S.[Stefano], Pascucci, S.[Simone], Tolomio, M.[Massimo], Huang, W.J.[Wen-Jiang], Casa, R.[Raffaele],
Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VENµS Satellite Data,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
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Lang, T.T.[Ting-Ting], Yang, Y.Z.[Yan-Zhao], Jia, K.[Kun], Zhang, C.[Chao], You, Z.[Zhen], Liang, Y.B.[Yu-Bin],
Estimation of Winter Wheat Production Potential Based on Remotely-Sensed Imagery and Process-Based Model Simulations,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Aranguren, M.[Marta], Castellón, A.[Ander], Aizpurua, A.[Ana],
Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Cicuéndez, V.[Víctor], Rodríguez-Rastrero, M.[Manuel], Recuero, L.[Laura], Huesca, M.[Margarita], Schmid, T.[Thomas], Inclán, R.[Rosa], Litago, J.[Javier], Sánchez-Girón, V.[Víctor], Palacios-Orueta, A.[Alicia],
First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Wang, S.[Shuai], Chen, J.[Jin], Rao, Y.H.[Yu-Han], Liu, L.C.[Li-Cong], Wang, W.Q.[Wen-Qing], Dong, Q.[Qi],
Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors,
PandRS(168), 2020, pp. 221-235.
Elsevier DOI 2009
Spring frost, Winter wheat, Damage assessment, Remote sensing, North China BibRef

Zhu, K.Y.[Kang-Ying], Sun, Z.G.[Zhi-Gang], Zhao, F.H.[Feng-Hua], Yang, T.[Ting], Tian, Z.R.[Zhen-Rong], Lai, J.B.[Jian-Bin], Long, B.[Buju], Li, S.[Shiji],
Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat,
PandRS(168), 2020, pp. 197-207.
Elsevier DOI 2009
Remote sensing model, Canopy resistance, Stomatal behavior, Dry-hot wind stress, Salt stress, Winter wheat BibRef

Flynn, K.C.[K. Colton], Frazier, A.E.[Amy E.], Admas, S.[Sintayehu],
Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Flynn, K.C.[K. Colton], Lee, T.[Trey], Endale, D.[Dinku], Franzluebbers, A.[Alan], Ma, S.F.[Sheng-Fang], Zhou, Y.T.[Yu-Ting],
Assessing Remote Sensing Vegetation Index Sensitivities for Tall Fescue (Schedonorus arundinaceus) Plant Health with Varying Endophyte and Fertilizer Types: A Case for Improving Poultry Manuresheds,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhuo, W.[Wen], Huang, J.X.[Jian-Xi], Gao, X.R.[Xin-Ran], Ma, H.Y.[Hong-Yuan], Huang, H.[Hai], Su, W.[Wei], Meng, J.[Jihua], Li, Y.[Ying], Chen, H.L.[Huai-Liang], Yin, D.Q.[Dong-Qin],
Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Sapkota, B.[Bishwa], Singh, V.[Vijay], Neely, C.[Clark], Rajan, N.[Nithya], Bagavathiannan, M.[Muthukumar],
Detection of Italian Ryegrass in Wheat and Prediction of Competitive Interactions Using Remote-Sensing and Machine-Learning Techniques,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Weiß, T.[Thomas], Ramsauer, T.[Thomas], Löw, A.[Alexander], Marzahn, P.[Philip],
Evaluation of Different Radiative Transfer Models for Microwave Backscatter Estimation of Wheat Fields,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Xiao, Y.X.[Ying-Xin], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Liu, L.Y.[Lin-Yi], Ma, H.Q.[Hui-Qin], Ye, H.C.[Hui-Chun], Wang, K.[Kun],
Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Chen, P.F.[Peng-Fei],
Estimation of Winter Wheat Grain Protein Content Based on Multisource Data Assimilation,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Song, Y.[Yang], Wang, J.[Jing], Wang, L.X.[Li-Xin],
Satellite Solar-Induced Chlorophyll Fluorescence Reveals Heat Stress Impacts on Wheat Yield in India,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
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Huang, X.[Xin], Zhu, W.Q.[Wen-Quan], Wang, X.Y.[Xiao-Ying], Zhan, P.[Pei], Liu, Q.F.[Qiu-Feng], Li, X.Y.[Xue-Ying], Sun, L.X.[Li-Xin],
A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
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Tian, H.F.[Hai-Feng], Pei, J.[Jie], Huang, J.[Jianxi], Li, X.[Xuecao], Wang, J.[Jian], Zhou, B.[Boyan], Qin, Y.[Yaochen], Wang, L.[Li],
Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Liu, L.[Linyi], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Du, X.P.[Xiao-Ping], Ma, H.Q.[Hui-Qin],
Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Ma, H.Q.[Hui-Qin], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Liu, L.Y.[Lin-Yi], Guo, A.[Anting],
Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Dehkordi, R.H.[Ramin Heidarian], El Jarroudi, M.[Moussa], Kouadio, L.[Louis], Meersmans, J.[Jeroen], Beyer, M.[Marco],
Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Shawon, A.R.[Ashifur Rahman], Ko, J.[Jonghan], Jeong, S.[Seungtaek], Shin, T.[Taehwan], Lee, K.D.[Kyung Do], Shim, S.I.[Sang In],
Two-Dimensional Simulation of Barley Growth and Yield Using a Model Integrated with Remote-Controlled Aerial Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Aharon, S.[Shlomi], Peleg, Z.[Zvi], Argaman, E.[Eli], Ben-David, R.[Roi], Lati, R.N.[Ran N.],
Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
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Su, W.H.[Wen-Hao], Zhang, J.[Jiajing], Yang, C.[Ce], Page, R.[Rae], Szinyei, T.[Tamas], Hirsch, C.D.[Cory D.], Steffenson, B.J.[Brian J.],
Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

He, L., Qi, S.L., Duan, J.Z., Guo, T.C., Feng, W., He, D.X.,
Monitoring of Wheat Powdery Mildew Disease Severity Using Multiangle Hyperspectral Remote Sensing,
GeoRS(59), No. 2, February 2021, pp. 979-990.
IEEE DOI 2101
Diseases, Monitoring, Remote sensing, Indexes, Agriculture, Correlation, Vegetation mapping, Band combination, wheat BibRef

Zhu, K.Y.[Kang-Ying], Sun, Z.G.[Zhi-Gang], Zhao, F.[Fenghua], Yang, T.[Ting], Tian, Z.[Zhenrong], Lai, J.B.[Jian-Bin], Zhu, W.[Wanxue], Long, B.[Buju],
Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zheng, Q.[Qiong], Ye, H.C.[Hui-Chun], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Jiang, H.[Hao], Wang, C.Y.[Chong-Yang], Li, D.[Dan], Wang, L.[Li], Chen, S.[Shuisen],
Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Das, S.[Sumanta], Christopher, J.[Jack], Apan, A.[Armando], Roy Choudhury, M.[Malini], Chapman, S.[Scott], Menzies, N.W.[Neal W.], Dang, Y.P.[Yash P.],
UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil,
PandRS(173), 2021, pp. 221-237.
Elsevier DOI 2102
Canopy temperature, Plant water stress, Vegetation indices, Agglomerative hierarchical clustering, Wheat genotypes, Sodic soil BibRef

Bates, J.S.[Jordan Steven], Montzka, C.[Carsten], Schmidt, M.[Marius], Jonard, F.[François],
Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Fu, Y.Y.[Yuan-Yuan], Yang, G.[Guijun], Song, X.Y.[Xiao-Yu], Li, Z.H.[Zhen-Hong], Xu, X.G.[Xin-Gang], Feng, H.[Haikuan], Zhao, C.J.[Chun-Jiang],
Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Gorrab, A.[Azza], Ameline, M.[Maël], Albergel, C.[Clément], Baup, F.[Frédéric],
Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Marino, S.[Stefano], Alvino, A.[Arturo],
Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Liu, Y.[Yu], Hatou, K.[Kenji], Aihara, T.[Takanori], Kurose, S.[Sakuya], Akiyama, T.[Tsutomu], Kohno, Y.S.[Yasu-Shi], Lu, S.[Shan], Omasa, K.[Kenji],
A Robust Vegetation Index Based on Different UAV RGB Images to Estimate SPAD Values of Naked Barley Leaves,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Mouret, F.[Florian], Albughdadi, M.[Mohanad], Duthoit, S.[Sylvie], Kouamé, D.[Denis], Rieu, G.[Guillaume], Tourneret, J.Y.[Jean-Yves],
Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Sadeghi-Tehran, P.[Pouria], Virlet, N.[Nicolas], Hawkesford, M.J.[Malcolm J.],
A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Bhandari, M.[Mahendra], Baker, S.[Shannon], Rudd, J.C.[Jackie C.], Ibrahim, A.M.H.[Amir M. H.], Chang, A.[Anjin], Xue, Q.W.[Qing-Wu], Jung, J.H.[Jin-Ha], Landivar, J.[Juan], Auvermann, B.[Brent],
Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Li, F.J.[Fang-Jie], Ren, J.Q.[Jian-Qiang], Wu, S.[Shangrong], Zhao, H.W.[Hong-Wei], Zhang, N.[Ningdan],
Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Rufo, R.[Rubén], Soriano, J.M.[Jose Miguel], Villegas, D.[Dolors], Royo, C.[Conxita], Bellvert, J.[Joaquim],
Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Zhang, W.M.[Wen-Min], Brandt, M.[Martin], Prishchepov, A.V.[Alexander V.], Li, Z.F.[Zhao-Fu], Lyu, C.G.[Chun-Guang], Fensholt, R.[Rasmus],
Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019),
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Raya-Sereno, M.D.[María D.], Ortiz-Monasterio, J.I.[J. Ivan], Alonso-Ayuso, M.[María], Rodrigues, F.A.[Francelino A.], Rodríguez, A.A.[Arlet A.], González-Perez, L.[Lorena], Quemada, M.[Miguel],
High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Dandrifosse, S.[Sébastien], Carlier, A.[Alexis], Dumont, B.[Benjamin], Mercatoris, B.[Benoît],
Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ayari, E.[Emna], Kassouk, Z.[Zeineb], Lili-Chabaane, Z.[Zohra], Baghdadi, N.[Nicolas], Bousbih, S.[Safa], Zribi, M.[Mehrez],
Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wu, B.[Bin], Huang, W.J.[Wen-Jiang], Ye, H.[Huichun], Luo, P.[Peilei], Ren, Y.[Yu], Kong, W.P.[Wei-Ping],
Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Pour, M.K.[Majid Khak], Fotouhi, R.[Reza], Hucl, P.[Pierre], Zhang, Q.[Qianwei],
Development of a Mobile Platform for Field-Based High-Throughput Wheat Phenotyping,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Qu, C.[Chang], Li, P.J.[Pei-Jun], Zhang, C.M.[Cheng-Ming],
A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages,
PandRS(175), 2021, pp. 431-447.
Elsevier DOI 2105
Winter wheat mapping, Multi-temporal Landsat NDVI, Winter wheat index (WWI), Growth stages BibRef

Li, S.L.[Shi-Lei], Li, F.J.[Fang-Jie], Gao, M.F.[Mao-Fang], Li, Z.L.[Zhao-Liang], Leng, P.[Pei], Duan, S.[Sibo], Ren, J.Q.[Jian-Qiang],
A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Zhao, F.[Fa], Yang, G.J.[Gui-Jun], Yang, X.D.[Xiao-Dong], Cen, H.Y.[Hai-Yan], Zhu, Y.O.[Ya-Ohui], Han, S.Y.[Shao-Yu], Yang, H.[Hao], He, Y.[Yong], Zhao, C.J.[Chun-Jiang],
Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wu, X.C.[Xiao-Cui], Xiao, X.M.[Xiang-Ming], Steiner, J.[Jean], Yang, Z.W.[Zheng-Wei], Qin, Y.W.[Yuan-Wei], Wang, J.[Jie],
Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008-2018,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

de Camargo, T.[Tibor], Schirrmann, M.[Michael], Landwehr, N.[Niels], Dammer, K.H.[Karl-Heinz], Pflanz, M.[Michael],
Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Jenal, A.[Alexander], Hüging, H.[Hubert], Ahrends, H.E.[Hella Ellen], Bolten, A.[Andreas], Bongartz, J.[Jens], Bareth, G.[Georg],
Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits: A Case Study for Winter Wheat,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Shiff, S.[Shilo], Lensky, I.M.[Itamar M.], Bonfil, D.J.[David J.],
Using Satellite Data to Optimize Wheat Yield and Quality under Climate Change,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Shen, J.X.[Jian-Xiu], Evans, F.H.[Fiona H.],
The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Romano, E.[Elio], Bergonzoli, S.[Simone], Pecorella, I.[Ivano], Bisaglia, C.[Carlo], de Vita, P.[Pasquale],
Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Weiß, T.[Thomas], Ramsauer, T.[Thomas], Jagdhuber, T.[Thomas], Löw, A.[Alexander], Marzahn, P.[Philip],
Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Fei, S.P.[Shuai-Peng], Hassan, M.A.[Muhammad Adeel], He, Z.H.[Zhong-Hu], Chen, Z.[Zhen], Shu, M.Y.[Mei-Yan], Wang, J.K.[Jian-Kang], Li, C.C.[Chang-Chun], Xiao, Y.G.[Yong-Gui],
Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Ji, J.C.[Jing-Chun], Liu, J.[Jianli], Chen, J.J.[Jing-Jing], Niu, Y.J.[Yu-Jie], Xuan, K.F.[Ke-Fan], Jiang, Y.F.[Yi-Fei], Jia, R.[Renhao], Wang, C.[Can], Li, X.P.[Xiao-Peng],
Optimization of Topdressing for Winter Wheat by Accurate Growth Monitoring and Improved Production Estimation,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Manivasagam, V.S., Sadeh, Y.[Yuval], Kaplan, G.[Gregoriy], Bonfil, D.J.[David J.], Rozenstein, O.[Offer],
Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Evans, F.H.[Fiona H.], Shen, J.X.[Jian-Xiu],
Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Xiao, Y.X.[Ying-Xin], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Liu, L.[Linyi], Ma, H.Q.[Hui-Qin],
Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Wengert, M.[Matthias], Piepho, H.P.[Hans-Peter], Astor, T.[Thomas], Graß, R.[Rüdiger], Wijesingha, J.[Jayan], Wachendorf, M.[Michael],
Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Herzig, P.[Paul], Borrmann, P.[Peter], Knauer, U.[Uwe], Klück, H.C.[Hans-Christian], Kilias, D.[David], Seiffert, U.[Udo], Pillen, K.[Klaus], Maurer, A.[Andreas],
Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zeng, L.L.[Ling-Lin], Peng, G.Z.[Guo-Zhang], Meng, R.[Ran], Man, J.G.[Jian-Guo], Li, W.[Weibo], Xu, B.[Binyuan], Lv, Z.G.[Zhen-Gang], Sun, R.[Rui],
Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red-Green-Blue Imagery,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Colaço, A.F.[André Freitas], Schaefer, M.[Michael], Bramley, R.G.V.[Robert G. V.],
Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhao, J.Q.[Jian-Qing], Zhang, X.[Xiaohu], Yan, J.W.[Jia-Wei], Qiu, X.L.[Xiao-Lei], Yao, X.[Xia], Tian, Y.[Yongchao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing],
A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhao, L.C.[Li-Cheng], Guo, W.[Wei], Wang, J.[Jian], Wang, H.[Haozhou], Duan, Y.L.[Yu-Lin], Wang, C.[Cong], Wu, W.B.[Wen-Bin], Shi, Y.[Yun],
An Efficient Method for Estimating Wheat Heading Dates Using UAV Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Choudhury, M.R.[Malini Roy], Das, S.[Sumanta], Christopher, J.[Jack], Apan, A.[Armando], Chapman, S.[Scott], Menzies, N.W.[Neal W.], Dang, Y.P.[Yash P.],
Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Khan, I.H.[Imran Haider], Liu, H.Y.[Hai-Yan], Li, W.[Wei], Cao, A.Z.[Ai-Zhong], Wang, X.[Xue], Liu, H.Y.[Hong-Yan], Cheng, T.[Tao], Tian, Y.[Yongchao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Yao, X.[Xia],
Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

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Peron-Danaher, R.[Raquel], Russell, B.[Blake], Cotrozzi, L.[Lorenzo], Mohammadi, M.[Mohsen], Couture, J.J.[John J.],
Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Zhang, T.X.[Tian-Xiang], Xu, Z.Y.[Zhi-Yong], Su, J.[Jinya], Yang, Z.F.[Zhi-Fang], Liu, C.[Cunjia], Chen, W.H.[Wen-Hua], Li, J.[Jiangyun],
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Kong, W.P.[Wei-Ping], Huang, W.J.[Wen-Jiang], Ma, L.L.[Ling-Ling], Tang, L.[Lingli], Li, C.R.[Chuan-Rong], Zhou, X.F.[Xian-Feng], Casa, R.[Raffaele],
Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence,
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Eyre, R.[Riley], Lindsay, J.[John], Laamrani, A.[Ahmed], Berg, A.[Aaron],
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Wu, S.R.[Shang-Rong], Ren, J.Q.[Jian-Qiang], Chen, Z.X.[Zhong-Xin], Yang, P.[Peng], Li, H.[He], Liu, J.[Jia],
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IEEE DOI 2111
Agriculture, Remote sensing, Synthetic aperture radar, Optical sensors, Optical imaging, Data models, Yield estimation, yield simulation BibRef


Gansukh, B., Batsaikhan, B., Dorjsuren, A., Jamsran, C., Batsaikhan, N.,
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Chauhan, S., Darvishzadeh, R., Lu, Y., Stroppiana, D., Boschetti, M., Pepe, M., Nelson, A.,
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Aich, S., Josuttes, A., Ovsyannikov, I., Strueby, K., Ahmed, I., Duddu, H.S., Pozniak, C., Shirtliffe, S., Stavness, I.,
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning,
WACV18(323-332)
IEEE DOI 1806
crops, deconvolution, feature extraction, image colour analysis, learning (artificial intelligence), regression analysis, Task analysis BibRef

Wellens, J., Sallah, A.H., Tychon, B., Piccard, I., Gobin, A., Curnel, Y., Leclef, A., Goffart, D., Planchon, V., Goffart, J.P., Delloye, C., Defourny, P.,
Assessment of AquaCrop for winter wheat using satellite derived fCover data,
MultiTemp17(1-3)
IEEE DOI 1712
crops, mean square error methods, remote sensing, AquaCrop assessment, AquaCrop plug-in model, Belgium, yield estimate BibRef

Pryzant, R., Ermon, S., Lobell, D.,
Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning,
EarthVision17(1524-1532)
IEEE DOI 1709
Agriculture, Diseases, Monitoring, Remote sensing, Satellites, Spatial, resolution BibRef

Siricharoen, P.[Punnarai], Scotney, B.[Bryan], Morrow, P.[Philip], Parr, G.[Gerard],
Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition,
ICIAR15(456-464).
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Nakanishi, T., Imai, Y., Morita, T., Akamatsu, Y., Odagawa, S., Takeda, T., Kashimura, O.,
Evaluation Of Wheat Growth Monitoring Methods Based On Hyperspectral Data Of Later Grain Filling And Heading Stages In Western Australia,
ISPRS12(XXXIX-B8:295-300).
DOI Link 1209
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Zhang, H., Chen, H., Zhou, G.,
The Model Of Wheat Yield Forecast Based On Modis-ndvi: A Case Study Of Xinxiang,
AnnalsPRS(I-7), No. 2012, pp. 25-28.
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d'Andrimont, R.[Raphael], Duveiller, G.[Gregory], Defourny, P.[Pierre],
Exploring the capacity to grasp multi-annual seasonal variability of winter wheat in Continental Climates with MODIS,
MultiTemp11(221-224).
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Chacon, M.I.[Mario I.], Manickavasagan, A.[Annamalai], Flores-Tapia, D.[Daniel], Thomas, G.[Gabriel], Jayas, D.S.[Digvir S.],
Segmentation of Wheat Grains in Thermal Images Based on Pulse Coupled Neural Networks,
ICIP07(II: 273-276).
IEEE DOI 0709
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Cataltepe, Z., Cetin, E., Pearson, T.,
Identification of insect damaged wheat kernels using transmittance images,
ICIP04(V: 2917-2920).
IEEE DOI 0505
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Guzman-Arenas, A., Seco, R.M.[Rosa Ma], and Sanchez, V.G.[Victor G.],
Computer Analysis of Images for Crop Identification in Mexico,
TRIIMAS, Vol. 7, no. 135, 1976, UNAM. Crop id - wheat/cotton in NW Mexico; standard classification techniques; spectral signature and set of heuristic functions that the user defines. BibRef 7600

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Potato Crop Analysis, Production, Detection, Health, Change .


Last update:Nov 30, 2021 at 22:19:38