Abstract
Water management and up-to-date soil moisture (SM) information are crucial to ensure agricultural activities in dry-land farming regions. In this context, remote sensing imagery coupled with machine learning techniques can provide large scale SM information if there is enough data for training, which is really limited in reality. In this paper, we explored the potential of cycle-consistent Generative Adversarial Network (GAN) for data augmentation for training machine learning algorithms, which try to model spatial and temporal dependencies between the SM prediction (output) and the remote sensing imagery (input features). Specifically, the freely available SAR (Sentinel-1) and optical (Sentinel-2) time series data were evaluated together to predict SM using GANs. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there is not enough data to train a regression convolutional neural networks (CNN) to predict SM content.
Original language | English |
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Title of host publication | IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 5965-5968 |
Number of pages | 4 |
ISBN (Electronic) | 9781665403696 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2021-July |
Conference
Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
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Country/Territory | Belgium |
City | Brussels |
Period | 12/07/21 → 16/07/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Autoencoders
- CNN
- CycleGAN
- PCA
- Ridge regression
- Sentinel-1
- Sentinel-2
- Soil moisture
- Support vector regression