bibliography.bib

@misc{phi,
  author       = {Michael Phi},
  title        = {Illustrated Guide to {LSTMs} and {GRUs}: A step by step explanation},
  howpublished = {\url{https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru}},
  year         = {2018}
}

@misc{boehmke,
  author       = {Brad Boehmke},
  title        = {Computer vision \& {CNNs}: {MNIST} revisited},
  howpublished = {\url{https://rstudio-conf-2020.github.io/dl-keras-tf/04-computer-vision-cnns.html}},
  year         = {2020}
}

@misc{shah,
  author       = {Saily Shah},
  title        = {Convolutional Neural Network: An Overview},
  howpublished = {\url{https://www.analyticsvidhya.com/blog/2022/01/convolutional-neural-network-an-overview}},
  year         = {2022}
}

@misc{uc,
  author       = {UC R Programming},
  title        = {{UC} Business Analytics {R} Programming Guide},
  howpublished = {\url{http://uc-r.github.io}}
}

@misc{scikit,
  author       = {scikit-learn},
  title        = {Documentation},
  howpublished = {\url{https://scikit-learn.org/stable/modules/tree.html}}
}

@misc{mltech,
  author       = {mltech},
  title        = {Blog},
  howpublished = {\url{https://mltech.ai/en/w/multivariate_time_series_forecasting_for_bitcoin_pricing}}
}

@misc{influxdata,
  author       = {influxdata},
  howpublished = {\url{https://influxdata.com}}
}

@misc{finance,
  author       = {Google finance},
  howpublished = {\url{https://www.google.com/finance}}
}

@misc{googledevelopers,
  author       = {Google Developers},
  title        = {Overview of {GAN} Structure},
  howpublished = {\url{https://developers.google.com/machine-learning/gan/gan_structure}}
}

@article{vaswani,
  author     = {Ashish Vaswani and
                Noam Shazeer and
                Niki Parmar and
                Jakob Uszkoreit and
                Llion Jones and
                Aidan N. Gomez and
                Lukasz Kaiser and
                Illia Polosukhin},
  title      = {Attention Is All You Need},
  journal    = {CoRR},
  volume     = {abs/1706.03762},
  year       = {2017},
  url        = {http://arxiv.org/abs/1706.03762},
  eprinttype = {arXiv},
  eprint     = {1706.03762},
  timestamp  = {Sat, 23 Jan 2021 01:20:40 +0100},
  biburl     = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
  bibsource  = {dblp computer science bibliography, https://dblp.org}
}

@misc{tempCNN,
  doi       = {10.48550/ARXIV.1811.10166},
  url       = {https://arxiv.org/abs/1811.10166},
  author    = {Pelletier, Charlotte and Webb, Geoffrey I. and Petitjean, Francois},
  keywords  = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series},
  publisher = {arXiv},
  year      = {2018},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{LTAE,
  doi       = {10.48550/ARXIV.2007.00586},
  url       = {https://arxiv.org/abs/2007.00586},
  author    = {Garnot, Vivien Sainte Fare and Landrieu, Loic},
  keywords  = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series},
  publisher = {arXiv},
  year      = {2020},
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@inproceedings{NIPS1989_53c3bce6,
  author    = {LeCun, Yann and Boser, Bernhard and Denker, John and Henderson, Donnie and Howard, R. and Hubbard, Wayne and Jackel, Lawrence},
  booktitle = {Advances in Neural Information Processing Systems},
  editor    = {D. Touretzky},
  pages     = {},
  publisher = {Morgan-Kaufmann},
  title     = {Handwritten Digit Recognition with a Back-Propagation Network},
  url       = {https://proceedings.neurips.cc/paper/1989/file/53c3bce66e43be4f209556518c2fcb54-Paper.pdf},
  volume    = {2},
  year      = {1989}
}

@article{JMLR:v15:srivastava14a,
  author  = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
  title   = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  number  = {56},
  pages   = {1929--1958},
  url     = {http://jmlr.org/papers/v15/srivastava14a.html}
}

@article{DBLP:journals/corr/IoffeS15,
  author     = {Sergey Ioffe and
                Christian Szegedy},
  title      = {Batch Normalization: Accelerating Deep Network Training by Reducing
                Internal Covariate Shift},
  journal    = {CoRR},
  volume     = {abs/1502.03167},
  year       = {2015},
  url        = {http://arxiv.org/abs/1502.03167},
  eprinttype = {arXiv},
  eprint     = {1502.03167},
  timestamp  = {Mon, 13 Aug 2018 16:47:06 +0200},
  biburl     = {https://dblp.org/rec/journals/corr/IoffeS15.bib},
  bibsource  = {dblp computer science bibliography, https://dblp.org}
}

@book{han2011data,
  title     = {Data Mining: Concepts and Techniques},
  author    = {Han, J. and Pei, J. and Kamber, M.},
  year      = {2011},
  publisher = {Elsevier},
  address   = {Amsterdam, The Netherlands}
}

@inproceedings{kingma2014adam,
  title     = {Adam: A method for stochastic optimization},
  author    = {Kingma, D.P. and Ba, J.},
  booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
  year      = {2014},
  address   = {Banff, AB, Canada},
  month     = {April}
}

@book{Hyndman2018,
  author    = {Hyndman, Rob J and Athanasopoulos, George},
  title     = {Forecasting: principles and practice},
  year      = {2018},
  publisher = {OTexts},
  edition   = {2nd},
  url       = {https://otexts.com/fpp2/}
}

@book{Shumway2017,
  author    = {Shumway, Robert H. and Stoffer, David S.},
  title     = {Time Series Analysis and Its Applications: With R Examples},
  year      = {2017},
  publisher = {Springer}
}

@book{Ho2009,
  author    = {Ho, Tin Kam},
  title     = {Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions},
  year      = {2009},
  publisher = {Morgan Kaufmann}
}

@book{Sklansky2013,
  author    = {Sklansky, John},
  title     = {Ensemble Machine Learning},
  year      = {2013},
  publisher = {Springer}
}

@inproceedings{Ren2015Faster,
  author    = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {{Faster r-CNN: Towards Real-Time Object Detection with Region Proposal Networks}},
  year      = {2015},
  address   = {Montreal, QC, Canada},
  month     = dec,
  pages     = {91--99},
  publisher = {Curran Associates}
}

@inproceedings{He2016Deep,
  author    = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  title     = {{Deep Residual Learning for Image Recognition}},
  year      = {2016},
  address   = {Las Vegas, Nevada, USA},
  month     = jun,
  pages     = {770--778},
  publisher = {IEEE},
  url       = {https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf}
}


@article{rs10020236,
  author         = {Scarpa, Giuseppe and Gargiulo, Massimiliano and Mazza, Antonio and Gaetano, Raffaele},
  title          = {A CNN-Based Fusion Method for Feature Extraction from Sentinel Data},
  journal        = {Remote Sensing},
  volume         = {10},
  year           = {2018},
  number         = {2},
  article-number = {236},
  url            = {https://www.mdpi.com/2072-4292/10/2/236},
  issn           = {2072-4292},
  abstract       = {Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.},
  doi            = {10.3390/rs10020236}
}

@article{fawaz2018deep,
  title   = {Deep learning for time series classification: A review},
  author  = {Fawaz, Hassan Ismail and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
  journal = {arXiv preprint arXiv:1809.04356},
  year    = {2018}
}

@inproceedings{garnot2020satellite,
  author    = {Garnot, Vivien Sainte Fare and Landrieu, Loic and Giordano, Samuel and Chehata, Nesrine},
  title     = {Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2020}
}

@article{russwurm2019self,
  title   = {Self-Attention for Raw Optical Satellite Time Series Classification},
  author  = {Rußwurm, Marc and Körner, Marco},
  journal = {arXiv preprint arXiv:1910.10536},
  year    = {2019}
}

@inproceedings{wu2018group,
  title        = {Group normalization},
  author       = {Wu, Yuxin and He, Kaiming},
  booktitle    = {European Conference on Computer Vision},
  year         = {2018},
  organization = {Springer}
}

@article{ajrnn,
  author  = {Ma, Qianli and Li, Sen and Cottrell, Garrison},
  year    = {2020},
  month   = {10},
  pages   = {},
  title   = {Adversarial Joint-Learning Recurrent Neural Network for Incomplete Time Series Classification},
  volume  = {PP},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  doi     = {10.1109/TPAMI.2020.3027975}
}

@book{breiman2001random,
  title     = {Random forests},
  author    = {Breiman, Leo and Cutler, Adele},
  year      = {2001},
  publisher = {Chapman and Hall/CRC}
}

@article{charles2020random,
  title     = {Random Forests: A Simple Introduction},
  author    = {Charles, Kellep},
  journal   = {Towards Data Science},
  volume    = {},
  number    = {},
  pages     = {},
  year      = {2020},
  publisher = {Medium}
}

@book{goodfellow2016deep,
  title     = {Deep Learning},
  author    = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
  year      = {2016},
  publisher = {MIT Press},
  series    = {Adaptive Computation and Machine Learning}
}

@book{aggarwal2018neural,
  title     = {Neural networks and deep learning: a text with integrated labs},
  author    = {Aggarwal, Charu},
  year      = {2018},
  publisher = {Springer}
}

@book{graves2013generating,
  title     = {Generating sequences with recurrent neural networks},
  author    = {Graves, Alex},
  year      = {2013},
  publisher = {arXiv preprint arXiv:1308.0850}
}

@article{goodfellow2014generative,
  title   = {Generative adversarial networks},
  author  = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal = {Advances in neural information processing systems},
  volume  = {27},
  pages   = {2672--2680},
  year    = {2014}
}

@article{mirza2014conditional,
  title   = {Conditional generative adversarial nets},
  author  = {Mirza, Mehdi and Osindero, Simon},
  journal = {arXiv preprint arXiv:1411.1784},
  year    = {2014}
}

@inproceedings{ledig2017photo,
  title     = {Photo-realistic single image super-resolution using a generative adversarial network},
  author    = {Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew P and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  volume    = {2},
  year      = {2017}
}

@article{ganin2017domain,
  title   = {Domain-adversarial training of neural networks},
  author  = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Lempitsky, Victor},
  journal = {Journal of Machine Learning Research},
  volume  = {17},
  number  = {1},
  pages   = {2096--2030},
  year    = {2017}
}

@article{yang2018video,
  title     = {Video captioning by adversarial lstm},
  author    = {Yang, Ying and Zhou, Jie and Ai, Jia and Yi, Bo and Hanjalic, Alan and Shen, Heng Tao and Ji, Yushuang},
  journal   = {IEEE Transactions on Image Processing},
  volume    = {PP},
  number    = {99},
  pages     = {1--1},
  year      = {2018},
  publisher = {IEEE}
}

@inproceedings{yoon2018gain,
  title        = {GAIN: Missing data imputation using generative adversarial nets},
  author       = {Yoon, Juncheol and Jordon, James and van der Schaar, Mihaela},
  booktitle    = {International Conference on Machine Learning},
  pages        = {5689--5698},
  year         = {2018},
  organization = {PMLR}
}


@inproceedings{li2018learning,
  title     = {Learning from Incomplete Data with Generative Adversarial Networks},
  author    = {Steven Cheng-Xian Li and Bo Jiang and Benjamin Marlin},
  booktitle = {International Conference on Learning Representations},
  year      = {2019},
  url       = {https://openreview.net/forum?id=S1lDV3RcKm}
}

@inproceedings{luo2018multivariate,
  title     = {Multivariate time series imputation with generative adversarial networks},
  author    = {Luo, Yuyang and Cai, Xiao and Zhang, Yu and Xu, Jian and Xiaojie, Yuan},
  booktitle = {Advances in Neural Information Processing Systems 31},
  pages     = {1601--1612},
  year      = {2018}
}

@article{IENCO201911,
  title    = {Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture},
  journal  = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume   = {158},
  pages    = {11-22},
  year     = {2019},
  issn     = {0924-2716},
  doi      = {https://doi.org/10.1016/j.isprsjprs.2019.09.016},
  url      = {https://www.sciencedirect.com/science/article/pii/S0924271619302278},
  author   = {Ienco, D. and Interdonato, R. and Gaetano, R. and {Ho Tong Minh}, D.},
  keywords = {Satellite Image Time Series, Deep learning, Land cover classification, Sentinel-2, Sentinel-1, Data fusion}
}

@misc{tensorflow2015-whitepaper,
  title  = { {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
  url    = {https://www.tensorflow.org/},
  note   = {Software available from tensorflow.org},
  author = {
            Mart\'{i}n~Abadi and
            Ashish~Agarwal and
            Paul~Barham and
            Eugene~Brevdo and
            Zhifeng~Chen and
            Craig~Citro and
            Greg~S.~Corrado and
            Andy~Davis and
            Jeffrey~Dean and
            Matthieu~Devin and
            Sanjay~Ghemawat and
            Ian~Goodfellow and
            Andrew~Harp and
            Geoffrey~Irving and
            Michael~Isard and
            Yangqing Jia and
            Rafal~Jozefowicz and
            Lukasz~Kaiser and
            Manjunath~Kudlur and
            Josh~Levenberg and
            Dandelion~Man\'{e} and
            Rajat~Monga and
            Sherry~Moore and
            Derek~Murray and
            Chris~Olah and
            Mike~Schuster and
            Jonathon~Shlens and
            Benoit~Steiner and
            Ilya~Sutskever and
            Kunal~Talwar and
            Paul~Tucker and
            Vincent~Vanhoucke and
            Vijay~Vasudevan and
            Fernanda~Vi\'{e}gas and
            Oriol~Vinyals and
            Pete~Warden and
            Martin~Wattenberg and
            Martin~Wicke and
            Yuan~Yu and
            Xiaoqiang~Zheng},
  year   = {2015}
}

@incollection{NEURIPS2019_9015,
  title     = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
  author    = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages     = {8024--8035},
  year      = {2019},
  publisher = {Curran Associates, Inc.},
  url       = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@misc{wandb,
  title  = {Weight and Biases: A Tool for Visualizing and Tracking Deep Learning Experiments},
  author = {Lukas, Biewald and Chris, Van Pelt},
  url    = {https://wandb.ai/}
}

@misc{TensorFlow:rf,
  author = {Guillame-Bert, Mathieu and Bruch, Sebastian and Stotz, Richard and Pfeifer, Jan},
  year   = {2022},
  month  = {12},
  pages  = {},
  title  = {Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library},
  doi    = {10.48550/arXiv.2212.02934}
}

@article{doi:10.1080/10095020.2019.1643609,
  author    = {Yong Gao and Jing Cheng and Haohan Meng and Yu Liu},
  title     = {Measuring spatio-temporal autocorrelation in time series data of collective human mobility},
  journal   = {Geo-spatial Information Science},
  volume    = {22},
  number    = {3},
  pages     = {166-173},
  year      = {2019},
  publisher = {Taylor & Francis},
  doi       = {10.1080/10095020.2019.1643609},
  url       = {https://doi.org/10.1080/10095020.2019.1643609},
  eprint    = {https://doi.org/10.1080/10095020.2019.1643609}
}

@article{rosenblatt1958perceptron,
  title     = {The perceptron: A probabilistic model for information storage and organization in the brain},
  author    = {Rosenblatt, Frank},
  journal   = {Psychological review},
  volume    = {65},
  number    = {6},
  pages     = {386},
  year      = {1958},
  publisher = {American Psychological Association}
}

@misc{Bhattacharyya,
  title  = {Hands-On Implementation Of Perceptron Algorithm in Python},
  author = {Jayita, Bhattacharyya}
}

@misc{convolutional:operation,
  author = {Podareanu, Damian and Codreanu, Valeriu and Aigner, Sandra and Leeuwen, Caspar and Weinberg, Volker},
  year   = {2019},
  month  = {02},
  pages  = {},
  title  = {Best Practice Guide - Deep Learning},
  doi    = {10.13140/RG.2.2.31564.05769}
}

@misc{arxiv.1603.07285,
  doi       = {10.48550/ARXIV.1603.07285},
  url       = {https://arxiv.org/abs/1603.07285},
  author    = {Dumoulin, Vincent and Visin, Francesco},
  keywords  = {Machine Learning (stat.ML), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {A guide to convolution arithmetic for deep learning},
  publisher = {arXiv},
  year      = {2016},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{RNN:Unrolled,
  author = {Exxact},
  title  = {A Friendly Introduction to Graph Neural Networks},
  year   = {2020},
  url    = {https://www.exxactcorp.com/blog/Deep-Learning/a-friendly-introduction-to-graph-neural-networks}
}

@misc{arxiv.1701.07875,
  doi       = {10.48550/ARXIV.1701.07875},
  url       = {https://arxiv.org/abs/1701.07875},
  author    = {Arjovsky, Martin and Chintala, Soumith and Bottou, Léon},
  keywords  = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {Wasserstein GAN},
  publisher = {arXiv},
  year      = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{CycleGAN2017,
  title     = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
  author    = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle = {Computer Vision (ICCV), 2017 IEEE International Conference on},
  year      = {2017}
}

@misc{arxiv.1411.1784,
  doi       = {10.48550/ARXIV.1411.1784},
  url       = {https://arxiv.org/abs/1411.1784},
  author    = {Mirza, Mehdi and Osindero, Simon},
  keywords  = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {Conditional Generative Adversarial Nets},
  publisher = {arXiv},
  year      = {2014},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{chollet2015keras,
  author       = {Chollet, Fran\c{c}ois},
  title        = {Keras},
  year         = {2015},
  howpublished = {\url{https://keras.io}},
  note         = {Accessed on 1 February 2018}
}

@article{CHEN2022102762,
  title    = {A joint learning Im-BiLSTM model for incomplete time-series Sentinel-2A data imputation and crop classification},
  journal  = {International Journal of Applied Earth Observation and Geoinformation},
  volume   = {108},
  pages    = {102762},
  year     = {2022},
  issn     = {1569-8432},
  doi      = {https://doi.org/10.1016/j.jag.2022.102762},
  url      = {https://www.sciencedirect.com/science/article/pii/S0303243422000885},
  author   = {Baili Chen and Hongwei Zheng and Lili Wang and Olaf Hellwich and Chunbo Chen and Liao Yang and Tie Liu and Geping Luo and Anming Bao and Xi Chen},
  keywords = {Crop classification, Multi-temporal, Data imputation, Joint learning, Bidirectional LSTM, Interpretation}
}

@misc{wiki:sentinel2,
  author = {{Sentinel-2}},
  title  = {Sentinel-2 --- {W}ikipedia{,} The Free Encyclopedia},
  url    = {https://en.wikipedia.org/wiki/Sentinel-2}
}

@inproceedings{rouse1974monitoring,
  title        = {Monitoring vegetation systems in the Great Plains with ERTS},
  author       = {Rouse, J W and Haas, R H and Schell, J A and Deering, D W},
  booktitle    = {Third ERTS-1 Symposium},
  volume       = {1},
  pages        = {309--317},
  year         = {1974},
  organization = {NASA},
  address      = {Washington DC},
  series       = {NASA SP}
}

@article{doi:10.1080/01431169608948714,
  author    = { S. K.   McFEETERS },
  title     = {The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features},
  journal   = {International Journal of Remote Sensing},
  volume    = {17},
  number    = {7},
  pages     = {1425-1432},
  year      = {1996},
  publisher = {Taylor & Francis},
  doi       = {10.1080/01431169608948714},
  url       = {https://doi.org/10.1080/01431169608948714}
}

@article{rs9010095,
  author         = {Inglada, Jordi and Vincent, Arthur and Arias, Marcela and Tardy, Benjamin and Morin, David and Rodes, Isabel},
  title          = {Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series},
  journal        = {Remote Sensing},
  volume         = {9},
  year           = {2017},
  number         = {1},
  article-number = {95},
  url            = {https://www.mdpi.com/2072-4292/9/1/95},
  issn           = {2072-4292},
  abstract       = {A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.},
  doi            = {10.3390/rs9010095}
}

@article{doi:10.1080/01431160600589179,
  author    = { Hanqiu   Xu },
  title     = {Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery},
  journal   = {International Journal of Remote Sensing},
  volume    = {27},
  number    = {14},
  pages     = {3025-3033},
  year      = {2006},
  publisher = {Taylor & Francis},
  doi       = {10.1080/01431160600589179},
  url       = {https://doi.org/10.1080/01431160600589179}
}

@article{1996RSEnv..58..257G,
  author  = {{Gao}, Bo-cai},
  title   = {{NDWI{\textemdash}A normalized difference water index for remote sensing of vegetation liquid water from space}},
  journal = {Remote Sensing of Environment},
  year    = 1996,
  month   = dec,
  volume  = {58},
  number  = {3},
  pages   = {257-266},
  doi     = {10.1016/S0034-4257(96)00067-3},
  adsurl  = {https://ui.adsabs.harvard.edu/abs/1996RSEnv..58..257G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{barnes2000coincident,
  title        = {Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data},
  author       = {Barnes, E M and Clarke, T R and Richards, S E and Colaizzi, P D and Haberland, J and Kostrzewski, M and ... and Lascano, R J},
  booktitle    = {Proceedings of the Fifth International Conference on Precision Agriculture},
  volume       = {1619},
  pages        = {},
  year         = {2000},
  month        = {July},
  organization = {ASA, CSSA, and SSSA},
  address      = {Bloomington, MN, USA},
  url          = {https://naldc.nal.usda.gov/download/4190/PDF}
}