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
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 .