Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice

The comprehensive relationship of backscattering coefficient (σ0) values from two current X-band SAR sensors (COSMO-SkyMed and TerraSAR-X) with canopy biophysical variables were investigated using the SAR images acquired at VV polarization and shallow incidence angles. The difference and consistency of the two sensors were also examined. The chrono-sequential change of σ0 in rice paddies during the transplanting season revealed that σ0 reached the value of nearby water surfaces a day before transplanting, and increased significantly just after transplanting event (3 dB). Despite a clear systematic shift (6.6 dB) between the two sensors, the differences in σ0 between target surfaces and water surfaces in each image were comparable in both sensors. Accordingly, an image-based approach using the “water-point” was proposed. It would be useful especially when absolute σ0 values are not consistent between sensors and/or images. Among the various canopy variables, the panicle biomass was found to be best correlated with X-band σ0. X-band SAR would be promising for direct assessments of rice grain yields at regional scales from space, whereas it would have limited capability to assess the whole-canopy variables only during the very early growth stages. The results provide a clear insight on the potential capability of X-band SAR sensors for rice monitoring

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Parameters affecting interferometric coherence and implications for long-term operational monitoring of mining-induced surface deformation

Includes abstract.Includes bibliographical references.Surface deformation due to underground mining poses risks to health and safety as well as infrastructure and the environment. Consequently, the need for long-term operational monitoring systems exists. Traditional field-based measurements are point-based meaning that the full extent of deforming areas is poorly understood. Field-based techniques are also labour intensive if large areas are to be monitored on a regular basis. To overcome these limitations, this investigation considered traditional and advanced differential radar interferometry techniques for their ability to monitor large areas over time, remotely. An area known to be experiencing mining induced surface deformation was used as test case. The agricultural nature of the area implied that signal decorrelation effects were expected. Consequently, four sources of data, captured at three wavelengths by earth-orbiting satellites were obtained. This provided the opportunity to investigate different phase decorrelation effects on data from standard imaging platforms using real-world deformation phenomenon as test-case. The data were processed using standard dInSAR and polInSAR techniques. The deformation measurement results together with an analysis of parameters most detrimental to long-term monitoring were presented. The results revealed that, contrary to the hypothesis, polInSAR techniques did not provide an enhanced ability to monitor surface deformation compared to dInSAR techniques. Although significant improvements in coherence values were obtained, the spatial heterogeneity of phase measurements could not be improved. Consequently, polInSAR could not overcome ecorrelation associated with vegetation cover and evolving land surfaces. However, polarimetric information could be used to assess the scattering behaviour of the surface, thereby guiding the definition of optimal sensor configuration for long-term monitoring. Despite temporal and geometric decorrelation, the results presented demonstrated that mining-induced deformation could be measured and monitored using dInSAR techniques. Large areas could be monitored remotely and the areal extent of deforming areas could be assessed, effectively overcoming the limitations of field-based techniques. Consequently, guidelines for the optimal sensor configuration and image acquisition strategy for long-term operational monitoring of mining-induced surface deformation were provided

Application of RADARSAT-2 Polarimetric Data for Land Use and Land Cover Classification and Crop monitoring in Southwestern Ontario

Timely and accurate information of land surfaces is desirable for land change detection and crop condition monitoring. Optical data have been widely used in Land Use and Land Cover (LU/LC) mapping and crop condition monitoring. However, due to unfavorable weather conditions, high quality optical images are not always available. Synthetic Aperture Radar (SAR) sensors, such as RADARSAT-2, are able to transmit microwaves through cloud cover and light rain, and thus offer an alternative data source. This study investigates the potential of multi-temporal polarimetric RADARSAT-2 data for LU/LC classification and crop monitoring in the urban rural fringe areas of London, Ontario. Nine LU/LC classes were identified with a high overall accuracy of 91.0%. Also, high correlations have been found within the corn and soybean fields between some polarimetric parameters and Normalized Difference Vegetation Index (NDVI). The results demonstrate the capability of RADARSAT-2 in LU/LC classification and crop condition monitoring

Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOSInternational audienceThe objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°)

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development

Apports de données radar pour l'estimation des paramètres biophysiques des surfaces agricoles

Les travaux de thèse s'inscrivent au sein du chantier Sud-Ouest, dont le principal objectif est de contribuer à la compréhension et à la modélisation du fonctionnement des surfaces continentales à l'échelle du paysage. Ces travaux visent à améliorer les capacités de suivi et d'analyses de surfaces fortement anthropisées : les agrosystèmes. A la fois acteurs et spectateurs vis-à-vis du changement climatique, ces surfaces sont également dédiées à la production alimentaire. La problématique vise donc à concilier durabilité des ressources et niveau de production suffisant, en identifiant des outils comme la télédétection utiles à la prise de décision à des échelles allant de la parcelle au territoire. Dans ce contexte, les radars à synthèse d'ouverture (RSO) embarqués au sein de satellites, présentent le double avantage d'être sensibles à différents paramètres des surfaces continentales (en lien avec le sol, ou la végétation), et la capacité d'observation par condition nuageuse (à l'inverse des capteurs opérant dans le visible). Depuis les années 90, différentes études basées sur des images acquises avec la technologie RSO ont montré l'intérêt des données micro-ondes pour le suivi des surfaces continentales. Ces dernières années, l'émergence de missions satellites dans les bandes de fréquence X et L vient enrichir les possibilités d'étude autrefois limitées à la seule bande C. Ces couples capteurs-satellites fournissent aujourd'hui des produits à haute résolution spatiale (allant jusqu'au mètre), avec des possibilités de revisite hebdomadaire, critères nécessaires pour le suivi des zones hétérogènes, associées à de fortes dynamiques temporelles. Les travaux effectués dans le cadre de cette thèse visent à établir la complémentarité entre les données radars (TerraSAR-X, Radarsat-2 et Alos, dans les bandes spectrales X, C et L) et optiques (Formosat-2, Spot-4/5) acquises par satellites pour le suivi des agrosytèmes. Ils s'articulent autour de trois axes complémentaires : - Le premier consiste en la mise en oeuvre d'une campagne expérimentale basée sur l'acquisition d'un jeu de données (satellitaire et de terrain), nécessaire au développement de nouvelles approches pour l'analyse du paysage. La zone suivie, caractérisée par une forte anthropisation, est située à 50 km au sud-ouest de Toulouse. Les images satellitaires regroupent trois séries temporelles radar (bandes X, C et L), auxquelles s'ajoutent des acquisitions réalisées dans l'optique (Formosat-2, Spot-4/5). Avec un total d'une centaine d'images acquises dans les hyperfréquences, la zone commune aux différentes scènes couvre une surface de 10×10 km². Conjointement, les protocoles de mesures de terrain ont permis de considérer de manière indépendante les deux éléments clés de la surface : le sol et la culture. En complément des stations météorologiques installées dans le cadre du chantier, des mesures qualitatives et quantitatives ont été réalisés de manière synchrone avec les acquisitions satellites, sur un total de 387 parcelles. Cinq cultures sont principalement étudiées : blé, colza, tournesol, mais et soja. - Les signatures temporelles de chacune des cultures sont ensuite établies à chaque longueur d'onde d'acquisition satellitaire (optique et radar) à travers une approche originale de normalisation angulaire des signaux radar (combinaison de l'information radar et optique). Les résultats obtenus durant le cycle phénologique des cultures d'hiver (blé et colza) et d'été (maïs, soja et tournesol) montrent clairement la complémentarité des approches multi-capteurs, et la spécificité des signaux radars (en lien avec les états de polarisations et les fréquences considérées). Deux paramètres biophysiques relatifs à la végétation sont enfin estimés (LAI et hauteur), les données micro-ondes montrant à la fois une importante sensibilité et de bonnes performances. - La modélisation électromagnétique sur sol nu a tout d'abord permis d'évaluer différents formalismes, à savoir : les modèles de Dubois et d'Oh (1992 et 2004) ayant comme caractéristiques communes une description simplifiée des processus. Ils sont confrontés à un modèle reposant sur des bases physiques, le modèle IEM (Integral Equation Model). L'application des modèles dans les différentes bandes spectrales (X, C et L), montre des résultats très hétérogènes, les meilleures performances étant obtenue en bande X, avec le modèle d'Oh 1992. Par la suite, l'amélioration des modèles tire parti de l'analyse des résidus (vis-à-vis des variables d'entrée), afin de réduire la dispersion observée. Les modèles testés sont optimisés et validés selon une approche de type résidus. Une forte amélioration est observée pour la plupart des modèles. Les résultats mettent en évidence l'intérêt des données multi-capteurs pour le suivi des surfaces dédiées à l'agriculture. Dans un futur proche, les missions spatiales telles que Tandem-X, Sentinel-1/-2, Radarsat Constellation ou Alos-2 devraient pérenniser l'accès à ces données, et préciser ainsi les résultats obtenus dans le cadre de cette thèse.The thesis fall within the "SudOuest" project, whose main objective is to contribute to the understanding and the modeling of the land surface functioning, at the landscape scale. This work aims to improve the capacity of monitoring and analysis of highly anthropic surfaces: agrosystems. Both actors and audience to climate change, these surfaces are also dedicated to the food production. So the problem is to reconcile sustainability of resources and sufficient level of production, identifying tools, such as remote sensing, useful in making decision at scales ranging from plot to land. In this context, the Synthetic Aperture Radar (SAR) embedded in satellites have the twofold advantages of being sensitive to different parameters of the land surface (related to soil, and vegetation), and the ability to observe by cloudy condition (unlike sensors operating in the visible). Since the 90s, several studies based on images acquired with SAR technology have shown the interest of microwave data for the monitoring of land surface. In recent years, the emergence of satellite missions at X- and L-bands enriches study opportunities once only limited to the C-band. These sensor/satellite couples now provide products with high spatial resolution (up to a meter), with the possibility of weekly revisits, necessary criteria for the monitoring of heterogeneous areas associated with high temporal dynamics. Works done in this thesis aim to establish the complementarities between the radar (TerraSAR-X, Radarsat-2 and Alos, at X-, C- and L-bands) and optical data (Formosat-2, Spot-4/-5) acquired by satellites for the monitoring of agrosystems. They revolve around three complementary areas: - The first is the implementation of an experimental campaign based on the acquisition of a set of data (satellite and ground), necessary for the development of new approaches to landscape analysis. The studied area, characterized by a strong human impact, is located near Toulouse (at 50 km in the South West). Satellite images include three radar time series acquired at X-, C- and L-bands, and images acquired in the optical (Formosat-2, Spot-4/-5). With a total of one hundred images acquired in the microwave domain, the common area to the different scenes covering a region of 10×10 km². Together, the protocols used for field measurements consider independently the two key elements of the surface: the soil and the culture. In addition to the weather stations (part of the "SudOuest" project), qualitative and quantitative measurements are performed synchronously with the satellite acquisitions, on a total of 387 plots. Five crops are mainly studied: wheat, rapeseed, sunflower, corn and soybean. - The temporal signatures of these crops are then established for each satellite wavelength (optical and radar), through an original approach based on an angular normalization of radar signals (combining the optical and radar information). The results obtained during the phenological cycle of winter (wheat and rapeseed) and summer crops (corn, soybean and sunflower) clearly show the complementarity of multi-sensor approaches and the specificity of radar signals (associated with the considered polarization states and frequencies). Two biophysical parameters related to vegetation are finally estimated (leaf area index and height), the microwave data showing both high sensitivity and good performances. - The electromagnetic modeling of bare soil is first used to evaluate different formalisms, namely Dubois and Oh (1992 and 2004) models, with common characteristics, a simplified description of the process. They are confronted with a model based on the physical laws, the IEM (Integral Equation Model). The application of models in different spectral bands (X, C and L), shows very mixed results; the best performances are obtained at X-band with Oh 1992 model. Thereafter, the enhancement of the models takes advantage of the residue analysis (as a function of the input variables), to reduce the observed dispersion. The tested models are optimized and validated using an approach such residues. A significant improvement is observed for most models. The results highlight the interest of multi-sensor data for the monitoring of continental surfaces dedicated to agriculture. In the near future, satellite missions such as Tandem -X, Sentinel-1/-2, Radarsat Constellation or Alos-2 should sustain access to these data, and define the results obtained in this thesis

Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval

A set of ten RADARSAT-2 images acquired in fully polarimetric mode over a test site with rice fields in Seville, Spain, has been analyzed to extract the main features of the C-band radar backscatter as a function of rice phenology. After observing the evolutions versus phenology of different polarimetric observables and explaining their behavior in terms of scattering mechanisms present in the scene, a simple retrieval approach has been proposed. This algorithm is based on three polarimetric observables and provides estimates from a set of four relevant intervals of phenological stages. The validation against ground data, carried out at parcel level for a set of six stands and up to nine dates per stand, provides a 96% rate of coincidence. Moreover, an equivalent compact-pol retrieval algorithm has been also proposed and validated, providing the same performance at parcel level. In all cases, the inversion is carried out by exploiting a single satellite acquisition, without any other auxiliary information.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and European Union FEDER under Project TEC2011-28201-C02-02