23.2.8.1 Field Level Phenological Characteristics, Crop Phenology

Chapter Contents (Back)
Crop Phenology. Remote Sensing. Phenology. Agricultural. 2609
Precision Agriculture topics: phenotype-scetion.

Mattiuzzi, M.[Matteo], Bussink, C.[Coen], Bauer, T.[Thomas],
Analysing Phenological Characteristics Extracted from Landsat NDVI Time Series to Identify Suitable Image Acquisition Dates for Cannabis Mapping in Afghanistan,
PFG(2014), No. 5, 2014, pp. 383-392.
DOI Link 1411
BibRef

Alemu, W.G.[Woubet G.], Henebry, G.M.[Geoffrey M.],
Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data,
RS(8), No. 12, 2016, pp. 1016.
DOI Link 1612
BibRef

Alemu, W.G.[Woubet G.], Henebry, G.M.[Geoffrey M.],
Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Liu, L.L.[Ling-Ling], Zhang, X.Y.[Xiao-Yang], Yu, Y.Y.[Yun-Yue], Gao, F.[Feng], Yang, Z.W.[Zheng-Wei],
Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Liu, L.C.[Li-Cong], Cao, R.[Ruyin], Shen, M.G.[Miao-Gen], Chen, J.[Jin], Wang, J.M.[Jian-Min], Zhang, X.Y.[Xiao-Yang],
How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Qiu, T.[Tong], Song, C.H.[Cong-He], Li, J.X.[Jun-Xiang],
Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Diao, C.Y.[Chun-Yuan], Yang, Z.J.[Zi-Jun], Gao, F.[Feng], Zhang, X.Y.[Xiao-Yang], Yang, Z.W.[Zheng-Wei],
Hybrid phenology matching model for robust crop phenological retrieval,
PandRS(181), 2021, pp. 308-326.
Elsevier DOI 2110
Phenology, Remote sensing, Agriculture, Crop progress, Planting date BibRef

Taylor, S.D.[Shawn D.], Browning, D.M.[Dawn M.],
Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Gobin, A.[Anne], Sallah, A.H.M.[Abdoul-Hamid Mohamed], Curnel, Y.[Yannick], Delvoye, C.[Cindy], Weiss, M.[Marie], Wellens, J.[Joost], Piccard, I.[Isabelle], Planchon, V.[Viviane], Tychon, B.[Bernard], Goffart, J.P.[Jean-Pierre], Defourny, P.[Pierre],
Crop Phenology Modelling Using Proximal and Satellite Sensor Data,
RS(15), No. 8, 2023, pp. 2090.
DOI Link 2305
BibRef

Wu, Y.C.[Yong-Chuang], Wu, P.H.[Peng-Hai], Wu, Y.[Yanlan], Yang, H.[Hui], Wang, B.[Biao],
Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Liu, Y.[Yin], Diao, C.Y.[Chun-Yuan], Yang, Z.J.[Zi-Jun],
CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation,
PandRS(202), 2023, pp. 334-355.
Elsevier DOI 2308
Planting date, Remote sensing, Crop growth model, Phenology BibRef

Lu, J.[Jun], He, T.[Tao], Song, D.X.[Dan-Xia], Wang, C.Q.[Cai-Qun],
Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology,
RS(16), No. 12, 2024, pp. 2173.
DOI Link 2406
BibRef

Cao, R.[Ruyin], Li, L.[Luchun], Liu, L.[Licong], Liang, H.Y.[Hong-Yi], Zhu, X.L.[Xiao-Lin], Shen, M.G.[Miao-Gen], Zhou, J.[Ji], Li, Y.C.[Yue-Chen], Chen, J.[Jin],
A spatiotemporal shape model fitting method for within-season crop phenology detection,
PandRS(217), 2024, pp. 179-198.
Elsevier DOI 2409
Crop phenology, Crop management, In-season, Near real-time, Phenology prediction BibRef

Yang, Z.J.[Zi-Jun], Diao, C.Y.[Chun-Yuan], Gao, F.[Feng], Li, B.[Bo],
EMET: An emergence-based thermal phenological framework for near real-time crop type mapping,
PandRS(215), 2024, pp. 271-291.
Elsevier DOI 2408
Crop mapping, Crop phenology, Near real-time, Deep learning, Agriculture BibRef

Wang, X.C.[Xin-Cheng], Wang, Q.[Qinfei], Lai, H.Y.[Hong-Yan], Zhang, Z.W.[Zhen-Wen], Yun, T.[Ting], Lu, X.J.[Xiao-Jing], Wang, G.Z.[Gui-Zhen], Lao, S.[Shangye], Liao, Q.[Qi], Lu, S.[Saiqing], Chen, R.R.[Rui-Rui], Fang, S.[Shijing], Pan, F.[Feng], Yan, H.[Huabin], Li, K.[Kaimian], Chen, B.Q.[Bang-Qian],
A multi-sensor, phenology-based approach framework for mapping cassava cultivation dynamics and intercropping in highly fragmented agricultural landscapes,
PandRS(228), 2025, pp. 44-63.
Elsevier DOI 2509
Cassava, Remote sensing, Phenology, Intercropping, Multi-sensor BibRef

Tian, Q.Y.[Qi-Yu], Jiang, H.[Hao], Zhong, R.H.[Ren-Hai], Xiong, X.G.[Xing-Guo], Wang, X.H.[Xu-Hui], Huang, J.F.[Jing-Feng], Du, Z.H.[Zhen-Hong], Lin, T.[Tao],
PSeqNet: A crop phenology monitoring model accounting for phenological associations,
PandRS(225), 2025, pp. 257-274.
Elsevier DOI 2505
Crop phenology, Correlative rhythm, Data fusion, Deep learning BibRef

You, Z.[Ziyin], Wu, J.J.[Jia-Jun], Wang, X.R.[Xin-Rui], Wang, B.[Bo], Xu, X.[Xuan], Zhan, P.[Pei], Li, N.[Nan], Yan, C.[Chitfai],
On the Spectral-Phenological Features for Crop Mapping Under Complex Planting Patterns: A Case Study in Jiangsu Province, China,
RS(18), No. 13, 2026, pp. 2244.
DOI Link 2607
BibRef

Li, A.X.[Ai-Xuan], Yang, K.J.[Kai-Jing], Li, T.[Tao], Lei, B.[Bo], Bai, M.H.[Ming-Hao], Lu, D.Z.[De-Zhi], Yang, B.[Bin],
Phenology-Guided Weakly Supervised Cropping Structure Mapping with Phenological Similarity Constraints,
RS(18), No. 18, 2026, pp. 3130.
DOI Link 2609
BibRef

Zhang, Y.[Yong], Ren, Q.[Qianhua], Xu, F.H.[Frank Hang], Zheng, X.M.[Xing-Ming], Tao, Z.[Zui], Wu, Z.[Zhuo],
Improving Crop-Type Mapping in Fragmented Agricultural Landscapes with Parcel Constraints and HLSS30-Derived Phenological Features,
RS(18), No. 18, 2026, pp. 3149.
DOI Link 2609
BibRef


Wang, Z.Q.[Zi-Qiao], Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei],
Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification,
AgriVision21(2934-2943)
IEEE DOI 2109
Training, Adaptation models, Time series analysis, Feature extraction, Agriculture BibRef

Conti, J.C.[Jose C.], Farial, F.A.[Fabio A.], Almeida, J.[Jurandy], Alberton, B.[Bruna], Morellato, L.P.C.[Leonor P.C.], Camolesi, L.[Luiz], da Silva Torres, R.[Ricardo],
Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns,
ICPR14(3126-3131)
IEEE DOI 1412
Accuracy BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Crop Yields .


Last update:Sep 30, 2026 at 11:45:00