Clément Mallet is a senior scientist in remote sensing for land-cover mapping issues at the LaSTIG Laboratory (Gustave Eiffel University, IGN, French Mapping Agency), France. He is also Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing.Nesrine Chehata is a senior lecturer in geomatics and AI for Earth observation at ENSEGID-Bordeaux INP, France. She is also President and Co-founder of AGEOS (African Association for Geospatial Development), President of FNAACC (National Forum of Climate Change Adaptation Actors in Tunisia) and an IEEE GRSS senior member.

• Foreword xiFrancesca BOVOLOChapter 1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales 1Denis FEURER, Sean BEMIS, Guillaume COULOUMA, Hatem MABROUK, Sylvain MASSUEL, Romina Vanessa BARBOSA, Yoann THOMAS, Jérôme AMMANN and Fabrice VINATIER1.1. Introduction 11.2. The Time-SIFT method 41.3. Case studies 81.4. Conclusion 341.5. References 35Chapter 2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks 41Mehmet OZGUR TURKOGLU, Stefano D’ARONCO, Konrad SCHINDLER and Jan Dirk WEGNER2.1. Introduction 412.2. Literature 442.3. Background: sequence modeling with recurrent neural networks 492.4. Hierarchical multi-stage convolutional recurrent network 542.5. Experiment 572.6. Summary and future outlook 692.7. References 72Chapter 3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece 81Christina KARAKIZI, Konstantinos KARANTZALOS and Zacharias KANDYLAKIS3.1. Introduction 813.2. From raw data to analysis ready datasets 833.3. Classification and mapping 963.4. Data handling and computational challenges 1103.5. Conclusions 1123.6. Acknowledgments 1143.7. References 114Chapter 4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series 123Hassan BAZZI and Nicolas BAGHDADI4.1. Introduction 1234.2. Fundamentals behind remote sensing for irrigation mapping 1254.3. New methodologies for irrigation mapping using S1 and S2 time series 1314.4. Limits and perspectives 1424.5. Conclusions 1454.6. References 146Chapter 5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring 151Santiago BELDA, Luca PIPIA and Jochem VERRELST5.1. Introduction 1525.2. Time series processing for gap filling 1545.3. Time series processing for phenology indicators estimation 1615.4. Fusion of time series products for improved gap filling 1635.5. Time series processing toolbox: DATimeS 1705.6. Discussion 1735.7. Conclusions 1765.8. Acknowledgments 1775.9. References 177Chapter 6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers 185Maxime BEAUCHAMP and Ronan FABLET6.1. Notations 1856.2. Introduction 1866.3. Data assimilation 1886.4. Data-driven methods 1966.5. Application to satellite-derived ocean surface topography datasets 2106.6. Conclusion and discussion 2166.7. References 218Chapter 7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing 223Sophie GIFFARD-ROISIN7.1. Background 2247.2. Handling spatiotemporal data for TC forecasting 2297.3. Application 1: intensity forecasting from spatiotemporal reanalysis, a hackathon experiment 2317.4. Application 2: trajectory forecasting using fused deep learning 2367.5. Applications using recurrent neural networks–convolutional neural networks 2427.6. Applications using remote sensing data 2467.7. Conclusion, current limitations and open problems 2477.8. References 248List of Authors 253Index 257

Clément Mallet is a senior scientist in remote sensing for land-cover mapping issues at the LaSTIG Laboratory (Gustave Eiffel University, IGN, French Mapping Agency), France. He is also Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing.Nesrine Chehata is a senior lecturer in geomatics and AI for Earth observation at ENSEGID-Bordeaux INP, France. She is also President and Co-founder of AGEOS (African Association for Geospatial Development), President of FNAACC (National Forum of Climate Change Adaptation Actors in Tunisia) and an IEEE GRSS senior member.

• Foreword xiFrancesca BOVOLOChapter 1. Broader Application of the Time-SIFT Method: Proof-of-Concept of 3-D-Monitoring Study Cases with Various Spatiotemporal Scales 1Denis FEURER, Sean BEMIS, Guillaume COULOUMA, Hatem MABROUK, Sylvain MASSUEL, Romina Vanessa BARBOSA, Yoann THOMAS, Jérôme AMMANN and Fabrice VINATIER1.1. Introduction 11.2. The Time-SIFT method 41.3. Case studies 81.4. Conclusion 341.5. References 35Chapter 2. Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks 41Mehmet OZGUR TURKOGLU, Stefano D’ARONCO, Konrad SCHINDLER and Jan Dirk WEGNER2.1. Introduction 412.2. Literature 442.3. Background: sequence modeling with recurrent neural networks 492.4. Hierarchical multi-stage convolutional recurrent network 542.5. Experiment 572.6. Summary and future outlook 692.7. References 72Chapter 3. Exploiting Multitemporal Multispectral High-resolution Satellite Data toward Annual Land Cover and Crop Type Mapping: A Case Study in Greece 81Christina KARAKIZI, Konstantinos KARANTZALOS and Zacharias KANDYLAKIS3.1. Introduction 813.2. From raw data to analysis ready datasets 833.3. Classification and mapping 963.4. Data handling and computational challenges 1103.5. Conclusions 1123.6. Acknowledgments 1143.7. References 114Chapter 4. Irrigation Monitoring Using High Spatial and Temporal Resolutions Remote Sensing Time Series 123Hassan BAZZI and Nicolas BAGHDADI4.1. Introduction 1234.2. Fundamentals behind remote sensing for irrigation mapping 1254.3. New methodologies for irrigation mapping using S1 and S2 time series 1314.4. Limits and perspectives 1424.5. Conclusions 1454.6. References 146Chapter 5. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring 151Santiago BELDA, Luca PIPIA and Jochem VERRELST5.1. Introduction 1525.2. Time series processing for gap filling 1545.3. Time series processing for phenology indicators estimation 1615.4. Fusion of time series products for improved gap filling 1635.5. Time series processing toolbox: DATimeS 1705.6. Discussion 1735.7. Conclusions 1765.8. Acknowledgments 1775.9. References 177Chapter 6. Data-Driven Spatio-Temporal Interpolation for Satellite-Derived Geophysical Tracers 185Maxime BEAUCHAMP and Ronan FABLET6.1. Notations 1856.2. Introduction 1866.3. Data assimilation 1886.4. Data-driven methods 1966.5. Application to satellite-derived ocean surface topography datasets 2106.6. Conclusion and discussion 2166.7. References 218Chapter 7. Recent Advances in Tropical Cyclone Forecasting Using Machine Learning on Reanalysis and Remote Sensing 223Sophie GIFFARD-ROISIN7.1. Background 2247.2. Handling spatiotemporal data for TC forecasting 2297.3. Application 1: intensity forecasting from spatiotemporal reanalysis, a hackathon experiment 2317.4. Application 2: trajectory forecasting using fused deep learning 2367.5. Applications using recurrent neural networks–convolutional neural networks 2427.6. Applications using remote sensing data 2467.7. Conclusion, current limitations and open problems 2477.8. References 248List of Authors 253Index 257