Semi-empirical calibration of the Integral Equation Model for SAR data in C-band and cross polarization using radar images and field measurements

The estimation of surface soil parameters (moisture and roughness) from Synthetic Aperture Radar (SAR) images requires the use of well-calibrated backscattering models. The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed by Baghdadi et al. (2004 and 2006) for HH and VV polarizations to HV polarization. The approach consisted in replacing the measured correlation length by a fitting/calibration parameter so that model simulations would closely agree with radar measurements. This calibration in C-band covers radar configurations with incidence angles between 24° and 45.8°. Good agreement was found between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM

Comparison between backscattered TerraSAR signals and simulations from the radar backscattering models IEM, Oh, and Dubois

The objective of this paper is to evaluate on bare soils the surface backscattering models IEM, Oh, and Dubois in X-band. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture and surface roughness). Oh's model correctly simulates the radar signal for HH and VV polarizations whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR data and model. The backscattering Integral Equation Model (IEM) model simulates correctly the backscattering coefficient only for rms1.5 cm in using Gaussian function. However, the results are not satisfactory for a use of IEM in the inversion of TerraSAR data. A semi-empirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR data and the simulations using the calibrated version of the IEM

Explaining anomalies in SAR and scatterometer soil moisture retrievals from dry soils with sub-surface scattering

This paper presents the results of a laboratory investigation to explain anomalously-high soil moisture estimates observed in retrievals from SAR and scatterometer backscatter, affecting extensive areas of the world associated with arid climates. High resolution C-band tomographic profiling was applied in experiments to understand the mechanisms underlying these anomalous retrievals. The imagery captured unique high-resolution profiles of the variations in the vertical backscattering patterns though a sandy soil with moisture change. The relative strengths of the surface and sub-surface returns were dependent upon both soil moisture and soil structure, incidence-angle, and polarization. Co-polarised returns could be dominated by both surface and sub-surface returns at times, whereas cross-polarised returns were strongly associated with sub-surface features. The work confirms suspicions that anomalous moisture estimates can arise from the presence of sub-surface features. Diversity in polarization and incidence angle may provide sufficient diagnostics to flag and correct these erroneous estimates, allowing their incorporation into global soil moisture productsMorriso

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

A potential use for the C-band polarimetric SAR parameters to characterise the soil surface over bare agriculture fields

The objective of this study was to analyze the potential of the C-band polarimetric SAR parameters for the soil surface characterization of bare agricultural soils. RADARSAT-2 data and simulations using the Integral Equation Model (IEM) were analyzed to evaluate the polarimetric SAR parameters' sensitivities to the soil moisture and surface roughness. The results showed that the polarimetric parameters in the C-band were not very relevant to the characterization of the soil surface over bare agricultural areas. Low dynamics were often observed between the polarimetric parameters and both the soil moisture content and the soil surface roughness. These low dynamics do not allow for the accurate estimation of the soil parameters, but they could augment the standard inversion approaches to improve the estimation of these soil parameters. The polarimetric parameter alpha_1 could be used to detect very moist soils (>30%), while the anisotropy could be used to separate the smooth soils

Soil moisture estimation using Sentinel-1 radar data

Soil moisture estimation using Sentinel-1 radar data The main aim of this diploma thesis was to find and quantify the relationship between the intensity of backscatter from the Sentinel-1 radar data and the volume soil moisture at the level of agricultural fields. The research was conducted in three areas, in the first part there were two vegetation-free fields near the Thessaloniki (Greece), and information about soil moisture was obtained from own measurements using a thermogravimetric method. The second part drew data from the freely available ISMN database and the research was carried out on agricultural fields during the vegetation season in northwest Germany. The third part used soil moisture data from the Czech Hydrometeorological Institute (ČHMÚ) and the area of interest was two grassed areas of the airport and one agricultural field. Correlation was measured by calculating the determination coefficient and by using the linear regression an equation for calculating the soil moisture from the radar backscatter was compiled. High dependence has been confirmed when VV polarization with constant surface roughness were examined. In the case of surfaces with varying roughness and vegetation cover, only low correlation was found, similarly with using VH polarization. Key words: radar, SAR, Sentinel-1, soil...Určování vlhkosti půdy s využitím radarových dat Sentinel-1 Hlavním cílem této diplomové práce bylo najít a kvantifikovat souvislost mezi intenzitou odraženého záření z radarových dat Sentinel-1 a vlhkostí půdy v měřítku na úrovni zemědělských polí. Výzkum probíhal na třech územích, v první části byla zájmovým územím dvě pole bez vegetace v blízkosti řecké Soluně a informace o vlhkosti půdy byly získány z vlastního měření pomocí termogravimetrické metody. Druhá část čerpala data z volně dostupné databáze ISMN a výzkum probíhal na zemědělských polích během vegetační sezóny v severozápadním Německu. Třetí část využívala data o vlhkosti půdy od ČHMÚ a zájmovými územími byly dvě zatravněné plochy letiště a jedno zemědělské pole. Závislost byla měřena výpočtem koeficientu determinace a pomocí lineární regrese byla sestavena rovnice pro výpočet vlhkosti půdy z intenzity radarového odrazu. Potvrdila se vysoká závislost v případě použití polarizace VV a konstantní drsnosti povrchu. V případě ploch s měnící se drsností a vegetačním krytem byla nalezena jen nízká závislost, obdobně při použití VH polarizace. Klíčová slova: radar, SAR, Sentinel-1, vlhkost půdy, ISMN, ČHMÚDepartment of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

A Typical Review of Current and Prospective Microwave and Optical Remote Sensing Datasets for Soil Moisture Retrieval

Soil Moisture content is a vital indicator of both the weather and the water cycle. It has been a long-standing difficulty for the field of remote sensing to make sense of soil moisture's spatial and temporal distribution. For over five decades, researchers across the world have exclusively investigated the optical and microwave datasets for estimating soil moisture by developing various models, and algorithms. Nevertheless, challenges are faced in the consistent retrieval of SM at local, and global scales with higher accuracy in space and time resolution. The review was conducted in-depth, looking at the methods using optical and microwave data to determine soil moisture, and outlining the benefits and drawbacks considering the current needs.  With this research, a new age of widespread use of space technology for remote sensing of soil moisture has been ushered in. The study also acknowledges the scientific challenges of utilizing remote sensing datasets for soil moisture measurement

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

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe