Applications of SAR Interferometry in Earth and Environmental Science Research

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions

Insar Role in the Study of Earth's Surface and Synergic Use with Other Geodetic Data: the 2014 South Napa Earthquake

This work focuses on the role of SAR Interferometry (InSAR) in the study of many phenomena characterizing the Earth's surface. We propose an advanced integration method in order to merge the InSAR data with other geodetic data, i.e. Multiple Aperture Interferometry (MAI), Pixel Offset Tracking (POT) and Global Positioning System (GPS). We apply the method to constrain the full 3D displacement field produced by the Mw 6.1 2014 South Napa Valley earthquake and then we used the results from the integration to perform the source modeling. The first Chapter is meant to introduce the topic of the progressive use of Remote Sensing geodetic data to support the activities of monitoring and hazard mitigation related to natural phenomena. Chapter 2 shows the application of the InSAR technique to reconstruct and model surface displacement fields induced by several phenomena. In Chapter 3, the 3D coseismic displacement map due to the 2014 Mw 6.1 South Napa earthquake, close the San Andreas Fault system (California), is estimated by using a method to merge InSAR and GPS data. InSAR data are provided by the latest satellite of the European Space Agency (ESA), i.e. Sentinel-1, whereas the GPS data were obtained from the BARD network and several online archives. In Chapter 4 we propose an improved algorithm for the data integration and test it on the Napa earthquake. Geodetic data from MAI and POT are added in the processing chain and the GPS data interpolation is modified according to the specific phenomenon. Futhermore, the source modeling is performed by inversion of the obtained 3D displacement component. The best fit is obtained by simulating a fracture in the fault segment in agreement with previous works. Finally, in the last chapter we discuss about the advantages and disadvantages of the data integration and the future perspectives

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery

Accurate flood delineation is crucial in many disaster management tasks, such as risk map production and update, impact estimation, claim verification, or planning of countermeasures for disaster risk reduction. Open remote sensing resources such as the data provided by the Copernicus ecosystem enable to carry out this activity, which benefits from frequent revisit times on a global scale. In the last decades, satellite imagery has been successfully applied to flood delineation problems, especially considering Synthetic Aperture Radar (SAR) signals. However, current remote mapping services rely on time-consuming manual or semi-automated approaches, requiring the intervention of domain experts. The implementation of accurate and scalable automated pipelines is hindered by the scarcity of large-scale annotated datasets. To address these issues, we propose MMFlood, a multimodal remote sensing dataset purposely designed for flood delineation. The dataset contains 1,748 Sentinel-1 acquisitions, comprising 95 flood events distributed across 42 countries. Along with satellite imagery, the dataset includes the Digital Elevation Model (DEM), hydrography maps, and flood delineation maps provided by Copernicus EMS, which is considered as ground truth. To provide baseline performances on the MMFlood test set, we conduct a number of experiments of the flood delineation task using state-of-art deep learning models, and we evaluate the performance gains of entropy-based sampling and multi-encoder architectures, which are respectively used to tackle two of the main challenges posed by MMFlood, namely the class unbalance and the multimodal setting. Lastly, we provide a future outlook on how to further improve the performance of the flood delineation task

Combinaison de données optique et radar pour l’estimation de l’humidité du sol en milieu agricole

L'humidité du sol est essentielle pour les surfaces agricoles. Des variations importantes d’'humidité du sol peuvent affecter grandement le rendement agricole. L'état hydrique du sol devient donc un facteur important pour la croissance des cultures. Les changements climatiques affectent la disponibilité de l'eau dans les couches du sol. Pour une meilleure gestion du stress hydrique ou de l'asphyxie des plantes, dus respectivement à un manque ou à un excès d'humidité du sol, la mise en œuvre de méthodes de surveillance de l'humidité du sol est nécessaire. Or, l'humidité du sol est très variable dans le temps et l'espace en raison de sa dépendance à plusieurs paramètres dont la texture du sol, les précipitations et la topographie. Par conséquent, la connaissance de la teneur en eau de surface du sol à une échelle spatiale et temporelle élevée présente un grand défi. Ainsi, l’objectif de cette étude est de contribuer à l’amélioration de l’estimation de l’humidité du sol sur les zones agricoles, et ce, en combinant les données satellites radar et optiques à l’aide d’une méthode de détection des changements. L'étude repose sur la complémentarité des données optiques et radar et porte sur l'application d’un algorithme existant sur un site présentant une variabilité suffisante en termes de types de cultures et de texture du sol. L’humidité du sol est estimée à deux niveaux d’échelle : à l’échelle du champ et à l’échelle du pixel (30 m). En outre, une étude est menée pour analyser les limites de l'algorithme. La zone d'étude est le site de l'expérience terrain Soil Moisture Active and Passive (SMAP) tenue au Manitoba en 2016 (SMAPVEX16-MB) en vue de supporter les activités de validation des produits du satellite SMAP. Au cours de cette campagne, les caractéristiques du sol (humidité du sol et rugosité) et les données de végétation (biomasse, LAI, etc.) ont été collectées dans 50 champs, d'environ 800 m x 800 m, chaque. De plus, l’étude bénéficie de données in situ issues de stations permanentes de mesures d’humidité du sol du réseau Realtime In-situ Soil Monitoring for Agriculture (RISMA) et de stations temporaires. La base de données satellitaires est constituée de données radar (Radarsat-2 et Sentinel-1) et de données optiques multi-sources (Sentinel-2, Landsat-8, Rapideye et, Planetscope) afin de garantir une fréquence temporelle élevée (environ sept jours). Les images radar ont été normalisées à un angle d'incidence de 33° pour réduire la dépendance angulaire des coefficients de rétrodiffusion, suivi d'une réduction du chatoiement. Les données optiques fournissent des informations sur la végétation dont la prise en compte facilite l'estimation de l'humidité du sol à partir des données radar. Le coefficient de rétrodiffusion est extrait à l'échelle du champ et du pixel en utilisant les images radar préalablement prétraitées. Ensuite, la différence des coefficients de rétrodiffusion est calculée entre deux acquisitions consécutives, pour une cellule donnée (i, j). L'indice de végétation par différence normalisée (NDVI) calculé à partir des images optiques multi capteurs a été harmonisé pour réduire l'effet des différents paramètres des capteurs, aux deux niveaux d'échelle. Cet indice est également moyenné entre deux acquisitions consécutives pour une cellule donnée (i, j). Pour la méthode de détection des changements adoptée dans cette étude, nous avons représenté, pour chaque niveau d'échelle, la différence des coefficients de rétrodiffusion en fonction du NDVI moyenné entre deux acquisitions consécutives. Enfin, la relation obtenue à partir de cette représentation est utilisée dans une approche itérative pour déterminer l'humidité du sol, à l’échelle du champ et à l’échelle du pixel, pour la prochaine date d'acquisition. Pour effectuer l'itération, l’algorithme considère une valeur d'entrée d'humidité initiale du sol connue. À l'échelle du champ, pour l’ensemble des données, nous avons obtenu un RMSE = 0,07 m^3.m^(-3) et un coefficient de corrélation significatif R = 0,7 (p-value 0,6). Cependant, pour des conditions de faible végétation (NDVI < 0,6), les résultats sont meilleurs avec RMSE = 0,05 m3.m-3 et R = 0,83. À l'échelle du pixel, les résultats sont mauvais, avec un RMSE de 0,13 m3/m3, un coefficient de corrélation faible et non significatif de 0,14 (valeur p < 0,38) pour les NDVI inférieurs à 0,6; et un RMSE de 0,14 m3/m3, un coefficient de corrélation très faible et non significatif de 0,069 (valeur p < 0,48) pour les NDVI supérieurs à 0,6.Abstract : Soil moisture is critical to agricultural land. Variations in soil moisture (low or high) can greatly affect crop yields. Soil moisture status becomes a limiting factor for crop growth. Climate change affects the availability of water in the soil layers. For a better management of water stress or plant asphyxia, due respectively to a lack or an excess of soil moisture, the implementation of soil moisture monitoring methods is necessary. Soil moisture is highly variable in time and space due to its dependence on several parameters including soil texture, precipitation and topography. Therefore, knowledge of soil surface water content at a high spatial and temporal scale presents a great challenge. Thus, the objective of this study is to contribute to the improvement of soil moisture estimation over agricultural areas by combining radar and optical satellite data using a change detection method. The study is based on the complementarity of optical and radar data and focuses on the application of an existing algorithm on a site with sufficient variability in terms of crop types and soil texture. Soil moisture is estimated at two scales: field scale and pixel scale (30 m). In addition, a study is conducted to analyze the limitations of the algorithm. The study area is the site from the 2016 Soil Moisture Active and Passive (SMAP) mission soil moisture validation experiment conducted in Manitoba, Canada (SMAPVEX16-MB). During this campaign, soil characteristics (soil moisture and roughness) and vegetation data (biomass, LAI, etc.) were collected from 50 fields, of approximately 800 m x 800 m, each. In addition, the study benefits from in-situ data from permanent soil moisture stations of the Realtime In-situ Soil Monitoring for Agriculture (RISMA) network and from temporary stations. The satellite database is composed of radar data (Radarsat-2 and Sentinel-1) and multi-source optical data (Sentinel-2, Landsat-8, Rapideye, and Planetscope) in order to ensure a high temporal frequency (about seven days). Radar images were normalized to an incidence angle of 33° to reduce the angular dependence of the backscatter coefficients, followed by speckle reduction. The optical data provide vegetation information that facilitates the estimation of soil moisture from the radar data. The backscatter coefficient is extracted at the field and pixel scales using the pre-processed radar images. Then, the difference in backscatter coefficients is calculated between two consecutive acquisitions, for a given cell (i, j). The normalized difference vegetation index (NDVI) calculated from the multi-sensor optical images was harmonized to reduce the effect of different sensor parameters, at both scale levels. This index is also averaged between two consecutive acquisitions for a given cell (i, j). For the change detection method adopted in this study, we plotted, for each scale level, the difference in backscatter coefficients as a function of NDVI averaged between two consecutive acquisitions. Finally, the relationship obtained from this representation is used in an iterative approach to determine the soil moisture, both at the field and pixel scales, for the next acquisition date. To perform the iteration, the algorithm considers a known initial soil moisture input value. At the field scale, for the entire data set, we obtained an RMSE = 0.07 m3.m−3 and a significant correlation coefficient R = 0.7 (p-value 0.6). However, for low vegetation conditions (NDVI < 0.6), the results are better with RMSE = 0.05 m3.m−3and R = 0.83. At the pixel scale, very poor results are obtained with an RMSE of 0.13 m3/m3, a low and insignificant correlation coefficient of 0.14 (p-value < 0.38) for NDVI below 0.6; and an RMSE of 0.14 m3/m3, a very low and insignificant correlation coefficient of 0.069 (p-value < 0.48) for NDVI above 0.6

A Human-Centered Framework for the Understanding of Synthetic Aperture Radar Images

The limited usage of SAR data in the end-user community and in applicative contexts testified the failure of the recent literature, in which the research privileged the automatic extraction of information at the expense of users' experience with data. The development of new products and processing frameworks providing user-friendly representations and extraction of the physical information is a necessary condition for the full exploitation of SAR sensors. In this Book, the necessity to restore users’ centrality in remote sensing data analysis is analyzed and achieved through the introduction of two new classes of RGB SAR products obtained via multitemporal processing, whose principal characteristics are the ease of interpretation and the possibility to be processed with simple, end-user-oriented technique. These proposed approach aims to definitely fill the gap between the academy and the applications. The rationale is to provide ready-to-use images, in which the technical expertise with electromagnetic models, SAR imaging and image processing has been absorbed in the products formation phase. In such way, the idea that SAR images are too complicated to be interpreted and processed without a high technical expertise in order to extract physical information is overcame