23.2.8.5.1 Wheat Yield Estimates, Prediction

Chapter Contents (Back)
Wheat Yield.

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

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

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

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

Zheng, Y.[Yang], Zhang, M.[Miao], Zhang, X.[Xin], Zeng, H.W.[Hong-Wei], Wu, B.F.[Bing-Fang],
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

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.K.[Hai-Kuan],
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

Jin, X.L.[Xiu-Liang], Li, Z.H.[Zhen-Hai], Yang, G.J.[Gui-Jun], Yang, H.[Hao], Feng, H.K.[Hai-Kuan], 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

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
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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

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

Wang, Y.L.[Yu-Long], Xu, X.G.[Xin-Gang], Huang, L.S.[Lin-Sheng], Yang, G.J.[Gui-Jun], Fan, L.L.[Ling-Ling], Wei, P.F.[Peng-Fei], Chen, G.[Guo],
An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Skakun, S.[Sergii], Vermote, E.[Eric], Franch, B.[Belen], Roger, J.C.[Jean-Claude], Kussul, N.[Nataliia], Ju, J.C.[Jun-Chang], Masek, J.[Jeffrey],
Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Tuvdendorj, B.[Battsetseg], Wu, B.F.[Bing-Fang], Zeng, H.W.[Hong-Wei], Batdelger, G.[Gantsetseg], Nanzad, L.[Lkhagvadorj],
Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Han, J.C.[Ji-Chong], Zhang, Z.[Zhao], Cao, J.[Juan], Luo, Y.C.[Yu-Chuan], Zhang, L.L.[Liang-Liang], Li, Z.Y.[Zi-Yue], Zhang, J.[Jing],
Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Wang, Y.M.[Yu-Miao], Zhang, Z.[Zhou], Feng, L.[Luwei], Du, Q.Y.[Qing-Yun], Runge, T.[Troy],
Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

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.Y.[Zi-Yue],
Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Kamir, E.[Elisa], Waldner, F.[François], Hochman, Z.[Zvi],
Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods,
PandRS(160), 2020, pp. 124-135.
Elsevier DOI 2001
Agriculture, Machine learning, Actual yield, Yield Gap, Remote sensing, National scale, Regression BibRef

Dong, J.[Jie], Lu, H.B.[Hai-Bo], Wang, Y.W.[Ya-Wen], Ye, T.[Tao], Yuan, W.P.[Wen-Ping],
Estimating winter wheat yield based on a light use efficiency model and wheat variety data,
PandRS(160), 2020, pp. 18-32.
Elsevier DOI 2001
Landsat, Crop yield, Gross primary productivity, Crop variety, Light use efficiency BibRef

Fu, Z.P.[Zhao-Peng], Jiang, J.[Jie], Gao, Y.[Yang], Krienke, B.[Brian], Wang, M.[Meng], Zhong, K.T.[Kai-Tai], Cao, Q.A.[Qi-Ang], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Liu, X.J.[Xiao-Jun],
Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Zhao, Y.[Yan], Potgieter, A.B.[Andries B.], Zhang, M.[Miao], Wu, B.F.[Bing-Fang], Hammer, G.L.[Graeme L.],
Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Wang, X.L.[Xin-Lei], Huang, J.X.[Jian-Xi], Feng, Q.L.[Quan-Long], Yin, D.Q.[Dong-Qin],
Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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Vannoppen, A.[Astrid], Gobin, A.[Anne], Kotova, L.[Lola], Top, S.[Sara], de Cruz, L.[Lesley], Viksna, A.[Andris], Aniskevich, S.[Svetlana], Bobylev, L.[Leonid], Buntemeyer, L.[Lars], Caluwaerts, S.[Steven], de Troch, R.[Rozemien], Gnatiuk, N.[Natalia], Hamdi, R.[Rafiq], Remedio, A.R.[Armelle Reca], Sakalli, A.[Abdulla], van de Vyver, H.[Hans], van Schaeybroeck, B.[Bert], Termonia, P.[Piet],
Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Segarra, J.[Joel], González-Torralba, J.[Jon], Aranjuelo, Í.[Íker], Araus, J.L.[Jose Luis], Kefauver, S.C.[Shawn C.],
Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Song, Y.[Yang], Wang, J.F.[Jin-Fei], Shang, J.L.[Jia-Li], Liao, C.H.[Chun-Hua],
Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

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
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

Zhuo, W.[Wen], Huang, H.[Hai], Gao, X.R.[Xin-Ran], Li, X.C.[Xue-Cao], Huang, J.X.[Jian-Xi],
An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model,
RS(15), No. 7, 2023, pp. 1825.
DOI Link 2304
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
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

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

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
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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

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

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

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

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

Eyre, R.[Riley], Lindsay, J.[John], Laamrani, A.[Ahmed], Berg, A.[Aaron],
Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Wu, S.R.[Shang-Rong], Ren, J.Q.[Jian-Qiang], Chen, Z.X.[Zhong-Xin], Yang, P.[Peng], Li, H.[He], Liu, J.[Jia],
Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved From Networked Optical and SAR Remotely Sensed Images Into the WOFOST Model,
GeoRS(59), No. 11, November 2021, pp. 9071-9085.
IEEE DOI 2111
Agriculture, Remote sensing, Synthetic aperture radar, Optical sensors, Optical imaging, Data models, Yield estimation, yield simulation BibRef

Fu, Y.Y.[Yang-Yang], Huang, J.X.[Jian-Xi], Shen, Y.J.[Yan-Jun], Liu, S.M.[Shao-Min], Huang, Y.[Yong], Dong, J.[Jie], Han, W.[Wei], Ye, T.[Tao], Zhao, W.Z.[Wen-Zhi], Yuan, W.P.[Wen-Ping],
A Satellite-Based Method for National Winter Wheat Yield Estimating in China,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Xie, Y.[Yi], Huang, J.X.[Jian-Xi],
Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ganeva, D.[Dessislava], Roumenina, E.[Eugenia], Dimitrov, P.[Petar], Gikov, A.[Alexander], Jelev, G.[Georgi], Dragov, R.[Rangel], Bozhanova, V.[Violeta], Taneva, K.[Krasimira],
Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Ahmed, A.A.M.[A. A. Masrur], Sharma, E.[Ekta], Jui, S.J.J.[S. Janifer Jabin], Deo, R.C.[Ravinesh C.], Nguyen-Huy, T.[Thong], Ali, M.[Mumtaz],
Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Bian, C.F.[Chao-Fa], Shi, H.T.[Hong-Tao], Wu, S.[Suqin], Zhang, K.F.[Ke-Fei], Wei, M.[Meng], Zhao, Y.[Yindi], Sun, Y.Q.[Ya-Qin], Zhuang, H.F.[Hui-Fu], Zhang, X.W.[Xue-Wei], Chen, S.[Shuo],
Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Zare, H.[Hossein], Weber, T.K.D.[Tobias K. D.], Ingwersen, J.[Joachim], Nowak, W.[Wolfgang], Gayler, S.[Sebastian], Streck, T.[Thilo],
Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Cao, J.J.[Jun-Jun], Wang, H.J.[Hui-Jing], Li, J.X.[Jin-Xiao], Tian, Q.[Qun], Niyogi, D.[Dev],
Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning-Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Deng, Q.Z.[Qiu-Zhuo], Wu, M.X.[Meng-Xuan], Zhang, H.Y.[Hai-Yang], Cui, Y.T.[Yun-Tian], Li, M.Z.[Min-Zan], Zhang, Y.[Yao],
Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Lu, J.J.[Jun-Jun], Wang, H.Y.[Hong-Ye], Miao, Y.X.[Yu-Xin], Zhao, L.Q.[Li-Qin], Zhao, G.M.[Guang-Ming], Cao, Q.[Qiang], Kusnierek, K.[Krzysztof],
Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Bognár, P.[Péter], Kern, A.[Anikó], Pásztor, S.[Szilárd], Steinbach, P.[Péter], Lichtenberger, J.[János],
Testing the Robust Yield Estimation Method for Winter Wheat, Corn, Rapeseed, and Sunflower with Different Vegetation Indices and Meteorological Data,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Meraj, G.[Gowhar], Kanga, S.[Shruti], Ambadkar, A.[Abhijeet], Kumar, P.[Pankaj], Singh, S.K.[Suraj Kumar], Farooq, M.[Majid], Johnson, B.A.[Brian Alan], Rai, A.[Akshay], Sahu, N.[Netrananda],
Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wu, Y.T.[Yan-Tong], Xu, W.B.[Wen-Bo], Huang, H.[Hai], Huang, J.X.[Jian-Xi],
Bayesian Posterior-Based Winter Wheat Yield Estimation at the Field Scale through Assimilation of Sentinel-2 Data into WOFOST Model,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Bebie, M.[Maria], Cavalaris, C.[Chris], Kyparissis, A.[Aris],
Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Feng, H.K.[Hai-Kuan], Tao, H.L.[Hui-Lin], Fan, Y.G.[Yi-Guang], Liu, Y.[Yang], Li, Z.H.[Zhen-Hai], Yang, G.J.[Gui-Jun], Zhao, C.J.[Chun-Jiang],
Comparison of Winter Wheat Yield Estimation Based on Near-Surface Hyperspectral and UAV Hyperspectral Remote Sensing Data,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Hassan, M.A.[Muhammad Adeel], Fei, S.[Shuaipeng], Li, L.[Lei], Jin, Y.R.[Yi-Rong], Liu, P.[Peng], Rasheed, A.[Awais], Shawai, R.S.[Rabiu Sani], Zhang, L.[Liang], Ma, A.[Aimin], Xiao, Y.G.[Yong-Gui], He, Z.[Zhonghu],
Stacking of Canopy Spectral Reflectance from Multiple Growth Stages Improves Grain Yield Prediction under Full and Limited Irrigation in Wheat,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Tian, Z.Z.[Ze-Zhong], Zhang, Y.[Yao], Liu, K.D.[Kai-Di], Li, Z.H.[Zhen-Hai], Li, M.Z.[Min-Zan], Zhang, H.Y.[Hai-Yang], Wu, J.[Jiangmei],
UAV Remote Sensing Prediction Method of Winter Wheat Yield Based on the Fused Features of Crop and Soil,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Huang, H.[Hai], Huang, J.X.[Jian-Xi], Feng, Q.L.[Quan-Long], Liu, J.M.[Jun-Ming], Li, X.C.[Xue-Cao], Wang, X.L.[Xin-Lei], Niu, Q.[Quandi],
Developing a Dual-Stream Deep-Learning Neural Network Model for Improving County-Level Winter Wheat Yield Estimates in China,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Zhao, Y.[Yu], Han, S.Y.[Shao-Yu], Meng, Y.[Yang], Feng, H.K.[Hai-Kuan], Li, Z.H.[Zhen-Hai], Chen, J.L.[Jing-Li], Song, X.Y.[Xiao-Yu], Zhu, Y.[Yan], Yang, G.J.[Gui-Jun],
Transfer-Learning-Based Approach for Yield Prediction of Winter Wheat from Planet Data and SAFY Model,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Karimli, N.[Nilufar], Selbesoglu, M.O.[Mahmut Oguz],
Remote Sensing-Based Yield Estimation of Winter Wheat Using Vegetation and Soil Indices in Jalilabad, Azerbaijan,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Uribeetxebarria, A.[Asier], Castellón, A.[Ander], Aizpurua, A.[Ana],
Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm,
RS(15), No. 6, 2023, pp. 1640.
DOI Link 2304
BibRef

Belmahi, M.[Mohamed], Hanchane, M.[Mohamed], Krakauer, N.Y.[Nir Y.], Kessabi, R.[Ridouane], Bouayad, H.[Hind], Mahjoub, A.[Aziz], Zouhri, D.[Driss],
Analysis of Relationship between Grain Yield and NDVI from MODIS in the Fez-Meknes Region, Morocco,
RS(15), No. 11, 2023, pp. 2707.
DOI Link 2306
BibRef

Garcia-Perez, M.A., Rodriguez-Galiano, V., Sanchez-Rodriguez, E., Egea-Cobrero, V.,
Yield Estimation of Wheat Using Cropland Masks from European Common Agrarian Policy: Comparing the Performance of Enhanced Vegetation Index 2, Normalized Difference Vegetation Index, and MERIS Terrestrial Chlorophyll Index in Spanish Nomenclature of Territorial Units for Statistics Level 2 Regions,
RS(15), No. 22, 2023, pp. 5423.
DOI Link 2311
BibRef

Ma, J.C.[Jun-Cheng], Wu, Y.F.[Yong-Feng], Liu, B.[Binhui], Zhang, W.Y.[Wen-Ying], Wang, B.[Bianyin], Chen, Z.Y.[Zhao-Yang], Wang, G.C.[Guang-Cai], Guo, A.[Anqiang],
Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples,
RS(15), No. 23, 2023, pp. 5444.
DOI Link 2312
BibRef

Ganeva, D.[Dessislava], Roumenina, E.[Eugenia], Dimitrov, P.[Petar], Gikov, A.[Alexander], Bozhanova, V.[Violeta], Dragov, R.[Rangel], Jelev, G.[Georgi], Taneva, K.[Krasimira],
Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pleiades Satellite Data in Field Breeding Experiments,
RS(16), No. 3, 2024, pp. 559.
DOI Link 2402
BibRef

Zhang, M.[Meng], Sun, P.J.[Pei-Jun], Sun, Z.[Zhangli],
Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine,
RS(16), No. 4, 2024, pp. 659.
DOI Link 2402
BibRef

Kamenova, I.[Ilina], Chanev, M.[Milen], Dimitrov, P.[Petar], Filchev, L.[Lachezar], Bonchev, B.[Bogdan], Zhu, L.[Liang], Dong, Q.[Qinghan],
Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria,
RS(16), No. 7, 2024, pp. 1144.
DOI Link 2404
BibRef

Zhao, Y.X.[Yan-Xi], He, J.[Jiaoyang], Yao, X.[Xia], Cheng, T.[Tao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Tian, Y.C.[Yong-Chao],
Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events,
RS(16), No. 7, 2024, pp. 1259.
DOI Link 2404
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

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.
DOI Link 1209
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Wheat Rust, Blight, Disease, Damage .


Last update:May 6, 2024 at 15:50:14