23.2.8.1 Crop Yields

Chapter Contents (Back)
Crop Yield. Remote Sensing. Agricultural.

Panda, S., Ames, D., Panigrahi, S.,
Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques,
RS(2), No. 3, March 2010, pp. 673-696.
DOI Link 1203
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Rembold, F.[Felix], Atzberger, C.[Clement], Savin, I.[Igor], Rojas, O.[Oscar],
Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection,
RS(5), No. 4, April 2013, pp. 1704-1733.
DOI Link 1305
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And: Correction: RS(5), No. 11, 2013, pp. 5572-5573.
DOI Link 1312
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Mishra, V.[Vikalp], Cruise, J.F.[James F.], Mecikalski, J.R.[John R.], Hain, C.R.[Christopher R.], Anderson, M.C.[Martha C.],
A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields,
RS(5), No. 7, 2013, pp. 3331-3356.
DOI Link 1308
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Jiang, Z.W.[Zhi-Wei], Chen, Z.X.[Zhong-Xin], Chen, J.[Jin], Ren, J.Q.[Jian-Qiang], Li, Z.N.[Zong-Nan], Sun, L.[Liang],
The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation,
RS(6), No. 4, 2014, pp. 2664-2681.
DOI Link 1405
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Prilepova, O.[Olga], Hart, Q.[Quinn], Merz, J.[Justin], Parker, N.[Nathan], Bandaru, V.[Varaprasad], Jenkins, B.[Bryan],
Design of a GIS-Based Web Application for Simulating Biofuel Feedstock Yields,
IJGI(3), No. 3, 2014, pp. 929-941.
DOI Link 1407
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Kouadio, L.[Louis], Newlands, N.K.[Nathaniel K.], Davidson, A.[Andrew], Zhang, Y.[Yinsuo], Chipanshi, A.[Aston],
Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale,
RS(6), No. 10, 2014, pp. 10193-10214.
DOI Link 1411
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Hamada, Y.[Yuki], Ssegane, H.[Herbert], Negri, M.C.[Maria Cristina],
Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed,
RS(7), No. 8, 2015, pp. 9753.
DOI Link 1509
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Meroni, M., Fasbender, D., Balaghi, R., Dali, M., Haffani, M., Haythem, I., Hooker, J., Lahlou, M., Lopez-Lozano, R., Mahyou, H., Ben Moussa, M., Sghaier, N., Wafa, T., Leo, O.,
Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries,
GeoRS(54), No. 2, February 2016, pp. 795-804.
IEEE DOI 1601
Agriculture BibRef

Zhang, X.Y.[Xiao-Yang], Zhang, Q.Y.[Qing-Yuan],
Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations,
PandRS(114), No. 1, 2016, pp. 191-205.
Elsevier DOI 1604
Global crop yield BibRef

Bose, P.[Pritam], Kasabov, N.K.[Nikola K.], Bruzzone, L.[Lorenzo], Hartono, R.N.[Reggio N.],
Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series,
GeoRS(54), No. 11, November 2016, pp. 6563-6573.
IEEE DOI 1610
Agriculture BibRef

Pascucci, S.[Simone], Carfora, M.F.[Maria Francesca], Palombo, A.[Angelo], Pignatti, S.[Stefano], Casa, R.[Raffaele], Pepe, M.[Monica], Castaldi, F.[Fabio],
A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
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Petersen, L.K.[Lillian Kay],
Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Kim, N.[Nari], Ha, K.J.[Kyung-Ja], Park, N.W.[No-Wook], Cho, J.[Jaeil], Hong, S.[Sungwook], Lee, Y.W.[Yang-Won],
A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006-2015,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906
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Chen, Y.[Yang], Lee, W.S.[Won Suk], Gan, H.[Hao], Peres, N.[Natalia], Fraisse, C.[Clyde], Zhang, Y.C.[Yan-Chao], He, Y.[Yong],
Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
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Chen, Y.[Yang], McVicar, T.R.[Tim R.], Donohue, R.J.[Randall J.], Garg, N.[Nikhil], Waldner, F.[François], Ota, N.[Noboru], Li, L.T.[Ling-Tao], Lawes, R.[Roger],
To Blend or Not to Blend? A Framework for Nationwide Landsat-MODIS Data Selection for Crop Yield Prediction,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
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Karst, I.G.[Isabel G.], Mank, I.[Isabel], Traoré, I.[Issouf], Sorgho, R.[Raissa], Stückemann, K.J.[Kim-Jana], Simboro, S.[Séraphin], Sié, A.[Ali], Franke, J.[Jonas], Sauerborn, R.[Rainer],
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Trépos, R.[Ronan], Champolivier, L.[Luc], Dejoux, J.F.[Jean-François], Bitar, A. .A.[Ahmad Al], Casadebaig, P.[Pierre], Debaeke, P.[Philippe],
Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Nevavuori, P.[Petteri], Narra, N.[Nathaniel], Linna, P.[Petri], Lipping, T.[Tarmo],
Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
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Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi], Bhadra, S.[Sourav], Maimaitiyiming, M.[Matthew], Brown, D.R.[Davis R.], Sidike, P.[Paheding], Fritschi, F.B.[Felix B.],
Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning,
PandRS(174), 2021, pp. 265-281.
Elsevier DOI 2103
PlanetScope, WorldView-3, Deep learning, Convolutionneural network, ResNet, Artificial intelligence, Food security BibRef

Jaafar, H.[Hadi], Mourad, R.[Roya],
GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
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Abd-Elrahman, A.[Amr], Wu, F.[Feng], Agehara, S.[Shinsuke], Britt, K.[Katie],
Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
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Zhu, X.F.[Xiu-Fang], Guo, R.[Rui], Liu, T.T.[Ting-Ting], Xu, K.[Kun],
Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Chancia, R.[Robert], van Aardt, J.[Jan], Pethybridge, S.[Sarah], Cross, D.[Daniel], Henderson, J.[John],
Predicting Table Beet Root Yield with Multispectral UAS Imagery,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Saif, M.S.[Mohammad S.], Chancia, R.[Robert], Pethybridge, S.[Sarah], Murphy, S.P.[Sean P.], Hassanzadeh, A.[Amirhossein], van Aardt, J.[Jan],
Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Liu, Y.D.[Ya-Dong], Kim, J.[Junhwan], Fleisher, D.H.[David H.], Kim, K.S.[Kwang-Soo],
Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Teodoro, P.E.[Paulo Eduardo], Ribeiro Teodoro, L.P.[Larissa Pereira], Rojo Baio, F.H.[Fábio Henrique], da Silva Junior, C.A.[Carlos Antonio], dos Santos, R.G.[Regimar Garcia], Marques Ramos, A.P.[Ana Paula], Faita Pinheiro, M.M.[Mayara Maezano], Osco, L.P.[Lucas Prado], Gonçalves, W.N.[Wesley Nunes], Carneiro, A.M.[Alexsandro Monteiro], Junior, J.M.[José Marcato], Pistori, H.[Hemerson], Shiratsuchi, L.S.[Luciano Shozo],
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Johnson, D.M.[David M.], Rosales, A.[Arthur], Mueller, R.[Richard], Reynolds, C.[Curt], Frantz, R.[Ronald], Anyamba, A.[Assaf], Pak, E.[Ed], Tucker, C.[Compton],
USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Haufler, A.F.[Alex F.], Booske, J.H.[John H.], Hagness, S.C.[Susan C.],
Microwave Sensing for Estimating Cranberry Crop Yield: A Pilot Study Using Simulated Canopies and Field Measurement Testbeds,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Microwave theory and techniques, Agriculture, Machine learning, Sensors, Permittivity, Yield estimation, Soil moisture, microwave sensing BibRef

Li, K.Y.[Kai-Yun], Sampaio de Lima, R.[Raul], Burnside, N.G.[Niall G.], Vahtmäe, E.[Ele], Kutser, T.[Tiit], Sepp, K.[Karli], Pinheiro, V.H.C.[Victor Henrique Cabral], Yang, M.D.[Ming-Der], Vain, A.[Ants], Sepp, K.[Kalev],
Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Bojanowski, J.S.[Jedrzej S.], Sikora, S.[Sylwia], Musial, J.P.[Jan P.], Wozniak, E.[Edyta], Dabrowska-Zielinska, K.[Katarzyna], Slesinski, P.[Przemyslaw], Milewski, T.[Tomasz], Laczynski, A.[Artur],
Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-Meteorological Indicators for Operational Crop Yield Forecasting,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Muruganantham, P.[Priyanga], Wibowo, S.[Santoso], Grandhi, S.[Srimannarayana], Samrat, N.H.[Nahidul Hoque], Islam, N.[Nahina],
A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Dokoohaki, H.[Hamze], Rai, T.[Teerath], Kivi, M.[Marissa], Lewis, P.[Philip], Gómez-Dans, J.L.[Jose L.], Yin, F.[Feng],
Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Lyle, G.[Greg], Clarke, K.[Kenneth], Kilpatrick, A.[Adam], Summers, D.M.[David McCulloch], Ostendorf, B.[Bertram],
A Spatial and Temporal Evaluation of Broad-Scale Yield Predictions Created from Yield Mapping Technology and Landsat Satellite Imagery in the Australian Mediterranean Dryland Cropping Region,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Karaca, C.[Cihan], Thompson, R.B.[Rodney B.], Peña-Fleitas, M.T.[M. Teresa], Gallardo, M.[Marisa], Padilla, F.M.[Francisco M.],
Evaluation of Absolute Measurements and Normalized Indices of Proximal Optical Sensors as Estimators of Yield in Muskmelon and Sweet Pepper,
RS(15), No. 8, 2023, pp. 2174.
DOI Link 2305
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Dimov, D.[Dimo], Noack, P.[Patrick],
Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation,
RS(15), No. 16, 2023, pp. 3990.
DOI Link 2309
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Ma, Y.C.[Yu-Chi], Yang, Z.W.[Zheng-Wei], Huang, Q.Y.[Qun-Ying], Zhang, Z.[Zhou],
Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach,
RS(15), No. 18, 2023, pp. 4562.
DOI Link 2310
BibRef

Ishaq, R.A.F.[Rana Ahmad Faraz], Zhou, G.H.[Guan-Hua], Tian, C.[Chen], Tan, Y.M.[Yu-Min], Jing, G.[Guifei], Jiang, H.Z.[Hong-Zhi], ur-Rehman, O.[Obaid],
A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
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Yu, F.[Feng], Wang, M.[Ming], Xiao, J.[Jun], Zhang, Q.[Qian], Zhang, J.M.[Jin-Meng], Liu, X.[Xin], Ping, Y.[Yang], Luan, R.P.[Ru-Peng],
Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation,
RS(16), No. 6, 2024, pp. 1003.
DOI Link 2403
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Joshi, A.[Abhasha], Pradhan, B.[Biswajeet], Chakraborty, S.[Subrata], Varatharajoo, R.[Renuganth], Gite, S.[Shilpa], Alamri, A.[Abdullah],
Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data,
RS(16), No. 24, 2024, pp. 4804.
DOI Link 2501
BibRef

Rericha, J.[Jana], Kohútek, M.[Matej], Vandírková, V.[Vera], Krofta, K.[Karel], Kumhála, F.[František], Kumhálová, J.[Jitka],
Assessment of UAV Imageries for Estimating Growth Vitality, Yield and Quality of Hop (Humulus lupulus L.) Crops,
RS(17), No. 6, 2025, pp. 970.
DOI Link 2503
BibRef

Zhang, P.P.[Peng-Peng], Lu, B.[Bing], Shang, J.L.[Jia-Li], Tan, C.W.[Chang-Wei], Xu, Q.[Qihan], Shi, L.[Lei], Jin, S.J.[Shu-Jian], Wang, X.Y.[Xing-Yu], Jiang, Y.F.[Yun-Fei], Yang, Y.D.[Ya-Dong], Zang, H.D.[Hua-Dong], Ge, J.Y.[Jun-Yong], Zeng, Z.[Zhaohai],
TKSF-KAN: Transformer-enhanced oat yield modeling and transferability across major oat-producing regions in China using UAV multisource data,
PandRS(224), 2025, pp. 166-186.
Elsevier DOI 2505
Crop yield, Multimodal data, Deep learning, Stacking ensemble learning, Transfer learning BibRef

Luo, K.[Ke], Ren, J.Q.[Jian-Qiang], Bu, X.X.[Xiang-Xin], Zhao, H.W.[Hong-Wei],
Downscaling Method for Crop Yield Statistical Data Based on the Standardized Deviation from the Mean of the Comprehensive Crop Condition Index,
RS(17), No. 20, 2025, pp. 3408.
DOI Link 2510
BibRef

Yadav, S.A.[Suraj A.], Huang, Y.B.[Yan-Bo], Zhu, K.Q.[Kenny Q.], Haque, R.[Rayyan], Young, W.[Wyatt], Harvey, L.[Lorin], Hall, M.[Mark], Zhang, X.[Xin], Wijewardane, N.K.[Nuwan K.], Qin, R.J.[Rui-Jun], Feldman, M.[Max], Yao, H.B.[Hai-Bo], Brooks, J.P.[John P.],
Deep Transfer Learning for UAV-Based Cross-Crop Yield Prediction in Root Crops,
RS(17), No. 24, 2025, pp. 4054.
DOI Link 2512
BibRef


Kamangir, H.[Hamid], Hajiesmaeeli, M.[Mona], Earles, M.[Mason],
California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops,
AgriVision25(5491-5500)
IEEE DOI 2512
Adaptation models, Soil properties, Landsat, Benchmark testing, Predictive models, Satellite images, Forecasting, Crop yield, crop yield forecasting BibRef

Lin, F.D.[Fu-Dong], Crawford, S.[Summer], Guillot, K.[Kaleb], Zhang, Y.[Yihe], Chen, Y.[Yan], Yuan, X.[Xu], Chen, L.[Li], Williams, S.[Shelby], Minvielle, R.[Robert], Xiao, X.M.[Xiang-Ming], Gholson, D.[Drew], Ashwell, N.[Nicolas], Setiyono, T.[Tri], Tubana, B.[Brenda], Peng, L.[Lu], Bayoumi, M.[Magdy], Tzeng, N.F.[Nian-Feng],
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer,
ICCV23(5751-5761)
IEEE DOI Code:
WWW Link. 2401
BibRef

Casado-García, Á.[Ángela], Heras, J.[Jónathan], Martínez-Goñi, X.S.[Xabier Simon], Miranda-Apodaca, J.[Jon], Pérez-López, U.[Usue],
Estimation of Crop Production by Fusing Images and Crop Features,
CVPPA23(525-530)
IEEE DOI 2401
BibRef

Baghdasaryan, L.[Liana], Melikbekyan, R.[Razmik], Dolmajain, A.[Arthur], Hobbs, J.[Jennifer],
Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting,
WiCV22(2013-2022)
IEEE DOI 2210
Deep learning, Satellites, Government, Estimation, Crops BibRef

Narin, O.G., Sekertekin, A., Saygin, A., Balik Sanli, F., Gullu, M.,
Yield Estimation of Sunflower Plant With Cnn and Ann Using Sentinel-2,
SmartCityApp21(385-389).
DOI Link 2201
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Gross Primary Production, Net Primary Production, GPP, NPP .


Last update:Jan 8, 2026 at 12:52:16