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Lesson 1
1.
Lecture 1
1.1. Introduction to multisource RS data in agriculture
1.2. Overview of EO use in agriculture
1.2.1. Novel Opportunities in Optical Remote Sensing for Agricultural Remote Sensing Towards 2030
1.2.2. Synergistic Use of Artificial Intelligence and Multi-Source Remote Sensing in Agriculture
1.3. Optical and radar RS advantages and limitations (e.g., Sentinel-1, Sentinel-2, PlanetScope, and UAVs)
1.4. From Overview to Applications: What Comes Next?
Multiple choice quiz
2.
Lecture 2
2.1. Sentinel-2 and Planet Scope data, Vegetation indices
2.2. Wrap-up on optical data fusion methods
2.3. LSP definition and retrieval methods
2.4. Maize phenological stages and the BBCH Plant Classification System
2.5. Reference data and validation
2.6. Discussion on methods advantages and limitations
2.7. The use of remote sensing based phenometrics (classification crop rotation, climate change impact assessment and yield estimation)
2.8. Summary and final thoughts
Multiple choice quiz
3.
Practical Part 1
Session 1
Session 2
Session 3
4.
Lecture 3
4.1. Data preprocessing and downloading on Google Earth Engine (GEE)
4.2. Image Mapping, Train Test Dataset Creation
4.3. Feature Engineering and Data Fusion
4.4. Training a Unet model to create a synthetic NDVI
4.5. Generation of a denser timeseries combining the Sentinel 2 NDVI and Predicted Synthetic NDVI
4.6. References
5.
Practical Part 2
Session 1
Session 2
Session 3
Lesson 2
1.
Module 0
Action
2.
Module 1
Action
3.
Module 2
Action
4.
Module 3
Action
5.
Module 4
Action
6.
References
Lesson 3
1.
Lecture 1
1.1. Introduction to problems and challenges in groundwater Data
1.2. Challenges in complete and accurate Groundwater Assessments
1.3. Overview of spatiotemporal data and its relevance in spatial interpolation
Summary and final thoughts
2.
Lecture 2
2.1. Machine learning to fill spatiotemporal data gaps in hydrological monitoring networks
2.2. Machine learning as a generic framework for spatial prediction
2.3. Challenges in Implementing Random Forest and Integrating Spatial Data
Discussion Questions
Summary and final thoughts
3.
Practical Part
1. Overview of project setup and data sourcing
2. Curate and harmonize groundwater time series; identify and address quality issues
3. Geospatial data handling; groundwater time series and covariates
4. Conventional ML – RF
5. Conventional ML – RFsp
6. Conventional ML – RFSI
7. Performance evaluation of ML models
8. Time Series Mapping of Groundwater Levels
Lesson 4
1.
Lecture 1
1.1. Introduction to the small data problem in Remote Sensing for agriculture
1.2. Challenges of limited ground truth data in socio-environmental applications
1.3. Overview of emerging Deep Learning techniques for small data in Remote Sensing
1.4. Practical applications of these techniques for improving model performance in agricultural Remote Sensing tasks
1.5. Introduction to spatial k-fold cross-validation for robust evaluation of limited data
Summary and final thoughts
Multiple choice quiz
Understanding Assessment
2.
Lecture 2
2.1. Introduction to the small data problem in Remote Sensing for agriculture
2.2. Key Self-Supervised Learning models and techniques
2.3. Integration of SSL models with traditional Deep Learning models to improve generalization in Remote Sensing tasks
2.4. Challenges, best practices, and evaluation strategies for SSL in remote sensing
Summary and final thoughts
Multiple choice quiz
Understanding Assessment
3.
Practical Part
1. Overview: From theory to practice
2. Supervised learning with labeled remote sensing da
3. Self-Supervised Learning – MoCo
4. Self-Supervised Learning – SimCLR
5. Self-Supervised Learning – SimSiam
6. Wrap-up and comparative reflections
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