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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deeply synergistic optical and SAR time series for crop dynamic monitoring
Wenzhi Zhao, Yang Qu, Jiage Chen, et al.
Remote Sensing of Environment (2020) Vol. 247, pp. 111952-111952
Closed Access | Times Cited: 90

Showing 1-25 of 90 citing articles:

A Review of Deep Learning in Multiscale Agricultural Sensing
Dashuai Wang, Wujing Cao, Fan Zhang, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 559-559
Open Access | Times Cited: 137
Transfer learning in environmental remote sensing
Yuchi Ma, Shuo Chen, Stefano Ermon, et al.
Remote Sensing of Environment (2023) Vol. 301, pp. 113924-113924
Closed Access | Times Cited: 125
SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classification
Yuan Yuan, Lei Lin, Qingshan Liu, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 106, pp. 102651-102651
Open Access | Times Cited: 61
Recurrent-based regression of Sentinel time series for continuous vegetation monitoring
Anatol Garioud, Silvia Valero, Sébastien Giordano, et al.
Remote Sensing of Environment (2021) Vol. 263, pp. 112419-112419
Open Access | Times Cited: 59
Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage
Licong Liu, Ruyin Cao, Jin Chen, et al.
Remote Sensing of Environment (2022) Vol. 277, pp. 113060-113060
Closed Access | Times Cited: 48
Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions
Jingbo Li, Changchun Li, Weimeng Xu, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 112, pp. 102818-102818
Open Access | Times Cited: 46
Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification
Yuan Yuan, Lei Lin, Zeng-Guang Zhou, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 195, pp. 222-232
Closed Access | Times Cited: 46
A joint learning Im-BiLSTM model for incomplete time-series Sentinel-2A data imputation and crop classification
Baili Chen, Hongwei Zheng, Lili Wang, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102762-102762
Open Access | Times Cited: 39
Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes
Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2621-2621
Open Access | Times Cited: 38
Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images
Lu Xu, Dongping Ming, Tongyao Du, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106611-106611
Closed Access | Times Cited: 42
Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery
Zhu Han, Ce Zhang, Lianru Gao, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 206, pp. 293-310
Open Access | Times Cited: 17
Deep learning in cropland field identification: A review
Fan Xu, Xiaochuang Yao, Kangxin Zhang, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109042-109042
Closed Access | Times Cited: 7
Review of synthetic aperture radar with deep learning in agricultural applications
Mahya G.Z. Hashemi, Ehsan Jalilvand, Hamed Alemohammad, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 218, pp. 20-49
Closed Access | Times Cited: 6
Soil Moisture Estimation Using Sentinel-1/-2 Imagery Coupled With CycleGAN for Time-Series Gap Filing
Natalia Efremova, Mohamed El Amine Seddik, Esra Erten
IEEE Transactions on Geoscience and Remote Sensing (2021) Vol. 60, pp. 1-11
Open Access | Times Cited: 35
Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data
Yanyan Wang, Shenghui Fang, Lingli Zhao, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 108, pp. 102720-102720
Open Access | Times Cited: 25
An improved fusion of Landsat-7/8, Sentinel-2, and Sentinel-1 data for monitoring alfalfa: Implications for crop remote sensing
Jiang Chen, Zhou Zhang
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 124, pp. 103533-103533
Open Access | Times Cited: 15
A spectral-temporal constrained deep learning method for tree species mapping of plantation forests using time series Sentinel-2 imagery
Zehua Huang, Liheng Zhong, Feng Zhao, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 204, pp. 397-420
Closed Access | Times Cited: 13
A new Bayesian semi-supervised active learning framework for large-scale crop mapping using Sentinel-2 imagery
Yijia Xu, Jing Zhou, Zhou Zhang
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 209, pp. 17-34
Closed Access | Times Cited: 5
A survey of unmanned aerial vehicles and deep learning in precision agriculture
Dashuai Wang, Minghu Zhao, Zhuolin Li, et al.
European Journal of Agronomy (2024) Vol. 164, pp. 127477-127477
Closed Access | Times Cited: 5
Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data
Ryota Nomura, Kazuo Oki
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 732-732
Open Access | Times Cited: 28
A deep learning-based method for mapping alpine intermittent rivers and ephemeral streams of the Tibetan Plateau from Sentinel-1 time series and DEMs
Junyuan Fei, Jintao Liu, Linghong Ke, et al.
Remote Sensing of Environment (2022) Vol. 282, pp. 113271-113271
Closed Access | Times Cited: 21
In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model
Mingxiang Mao, Hongwei Zhao, Gula Tang, et al.
Agronomy (2023) Vol. 13, Iss. 7, pp. 1723-1723
Open Access | Times Cited: 12
Grassland mowing event detection using combined optical, SAR, and weather time series
Ann-Kathrin Holtgrave, Felix Lobert, Stefan Erasmi, et al.
Remote Sensing of Environment (2023) Vol. 295, pp. 113680-113680
Open Access | Times Cited: 11
Crop NDVI time series construction by fusing Sentinel-1, Sentinel-2, and environmental data with an ensemble-based framework
Dairong Chen, Haoxuan Hu, Chunhua Liao, et al.
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108388-108388
Closed Access | Times Cited: 11
Mapping tobacco planting areas in smallholder farmlands using Phenological-Spatial-Temporal LSTM from time-series Sentinel-1 SAR images
Mengmeng Li, Xiaomin Feng, Mariana Belgiu
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 129, pp. 103826-103826
Open Access | Times Cited: 4
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