Synergetic use of optical and radar for the estimation of continental surface states

L'agriculture en Tunisie fait partie des secteurs importants sur lesquels reposent l'économie du pays. Elle revêt également son importance par sa contribution à la sécurité alimentaire. Dans un contexte de gestion des ressources naturelles, la caractérisation et le suivi des états de surface est indispensable, particulièrement dans les régions semi-arides où plusieurs contraintes freinent le développement agricole (période de sécheresse, conflits sur le partage des eaux, manque de ressources, surexploitations des nappes, etc.). En Tunisie, près de 80 % des ressources en eau disponibles sont utilisées par l'agriculture avec une efficacité limitée. Là, où les ressources en eau sont très limitées, l'estimation de l'état hydrique de surface est particulièrement nécessaire pour établir les décisions adéquates pour une meilleure gestion de cette ressource. Dans ce contexte, la télédétection fournit une base fondamentale de données pour l'observation de la surface et constitue un outil majeur pour l'acquisition d'informations à distance. Les travaux réalisés au cours de cette thèse sur la plaine de Kairouan, au Centre de la Tunisie et caractérisée par un climat semi aride, contribuent à l'évaluation du potentiel des nouveaux capteurs satellitaires Sentinel-1 (S-1) et Sentinel-2 (S-2) pour la caractérisation des états de surface, spécifiquement l'humidité du sol dans un contexte de gestion durable des ressources en eau et en sol. En effet, ces nouveaux systèmes offrent aujourd'hui des produits opérationnels avec une forte répétitivité temporelle et des résolutions spatiales métriques permettant un suivi régulier. Dans notre contexte, les données radars sont particulièrement sensibles aux conditions de surface, précisément à l'humidité du sol, à la rugosité de surface et à la végétation. Ils se dévoilent comme les outils les plus prometteurs pour un suivi précis à l'échelle de la parcelle ou régionale. Ce travail comprend deux principales parties qui relient directement l'humidité du sol (variable clé pour différents processus) à l'irrigation dans un premier temps, puis à la texture du sol. L'approche adoptée combine les mesures expérimentales à l'utilisation de données de la télédétection multi-capteurs en synergie, ainsi à la modélisation et à la cartographie. La thèse se structure en trois volets. Le premier volet de ce travail évalue le potentiel des données radars en bande C pour une large base de données. Les résultats ont montré à travers des études du comportement et de modélisation que le signal radar permet de suivre la dynamique temporelle et spatiale de l'humidité du sol sur des parcelles de céréales. Le second volet, consiste à évaluer l'utilisation conjointe de données optiques et radars afin de pouvoir prédire l'état hydrique de surface sur une couverture végétale.[...]Agriculture is considered as one of the most important sectors in Tunisia on which the country's economy is predominately based. It is also important because of its contribution to food security. In the context of natural resource management, the characterization and monitoring of surface states is essential, particularly in semi-arid regions where several constraints hamper agricultural development (period of drought, conflicts over water sharing, lack of resources, overpumping of groundwater, etc.). In Tunisia, nearly 80% of available water resources are used by agriculture with limited efficiency. Here, with very limited water resources, the estimation of the surface water state is necessary to establish the appropriate decisions for a better sustainable management. In this context, remote sensing provides a fundamental database for surface observation. It is a major tool for remote sensing data acquisition.The work carried out during this thesis contributes to evaluate the potential of the new Sentinel-1 (S-1) and Sentinel-2 (S-2) satellite for the characterization of surface states, specifically soil moisture in a context of sustainable management of water and soil resources. Indeed, these new systems offer operational products with a high temporal repeatability and metric spatial resolutions allowing regular monitoring. In our context, radar data is particularly sensitive to surface conditions, specifically soil moisture, surface roughness and vegetation cover. They are unveiled as the most promising tools for accurate monitoring at the field or regional scale. This work includes two main parts that directly relate soil moisture (key variable for different processes) to irrigation first, and then to soil texture. The approach adopted combines experimental measurements with the use of different remote sensing data in synergy, modeling and mapping. The thesis is structured in three parts. The first part of this work evaluates the potential of C-band radar data for a large database. The results showed through behavioral and modeling studies that the radar signal could retrieve temporal and spatial dynamics of soil moisture on cereal plots. The second component consists of evaluating the combined use of optical and radar data in order to predict surface water conditions over vegetative cover. With a precision of about 6 vol. %, soil moisture mapping is then proposed at high spatial resolution, by inverting the Water Cloud Model (WCM), a backscattering model for vegetation cover.[...

Synergie optique-radar pour l'estimation des états de surface continentale

Agriculture is considered as one of the most important sectors in Tunisia on which the country's economy is predominately based. It is also important because of its contribution to food security. In the context of natural resource management, the characterization and monitoring of surface states is essential, particularly in semi-arid regions where several constraints hamper agricultural development (period of drought, conflicts over water sharing, lack of resources, overpumping of groundwater, etc.). In Tunisia, nearly 80% of available water resources are used by agriculture with limited efficiency. Here, with very limited water resources, the estimation of the surface water state is necessary to establish the appropriate decisions for a better sustainable management. In this context, remote sensing provides a fundamental database for surface observation. It is a major tool for remote sensing data acquisition.The work carried out during this thesis contributes to evaluate the potential of the new Sentinel-1 (S-1) and Sentinel-2 (S-2) satellite for the characterization of surface states, specifically soil moisture in a context of sustainable management of water and soil resources. Indeed, these new systems offer operational products with a high temporal repeatability and metric spatial resolutions allowing regular monitoring. In our context, radar data is particularly sensitive to surface conditions, specifically soil moisture, surface roughness and vegetation cover. They are unveiled as the most promising tools for accurate monitoring at the field or regional scale. This work includes two main parts that directly relate soil moisture (key variable for different processes) to irrigation first, and then to soil texture. The approach adopted combines experimental measurements with the use of different remote sensing data in synergy, modeling and mapping. The thesis is structured in three parts. The first part of this work evaluates the potential of C-band radar data for a large database. The results showed through behavioral and modeling studies that the radar signal could retrieve temporal and spatial dynamics of soil moisture on cereal plots. The second component consists of evaluating the combined use of optical and radar data in order to predict surface water conditions over vegetative cover. With a precision of about 6 vol. %, soil moisture mapping is then proposed at high spatial resolution, by inverting the Water Cloud Model (WCM), a backscattering model for vegetation cover.[...]L'agriculture en Tunisie fait partie des secteurs importants sur lesquels reposent l'économie du pays. Elle revêt également son importance par sa contribution à la sécurité alimentaire. Dans un contexte de gestion des ressources naturelles, la caractérisation et le suivi des états de surface est indispensable, particulièrement dans les régions semi-arides où plusieurs contraintes freinent le développement agricole (période de sécheresse, conflits sur le partage des eaux, manque de ressources, surexploitations des nappes, etc.). En Tunisie, près de 80 % des ressources en eau disponibles sont utilisées par l'agriculture avec une efficacité limitée. Là, où les ressources en eau sont très limitées, l'estimation de l'état hydrique de surface est particulièrement nécessaire pour établir les décisions adéquates pour une meilleure gestion de cette ressource. Dans ce contexte, la télédétection fournit une base fondamentale de données pour l'observation de la surface et constitue un outil majeur pour l'acquisition d'informations à distance. Les travaux réalisés au cours de cette thèse sur la plaine de Kairouan, au Centre de la Tunisie et caractérisée par un climat semi aride, contribuent à l'évaluation du potentiel des nouveaux capteurs satellitaires Sentinel-1 (S-1) et Sentinel-2 (S-2) pour la caractérisation des états de surface, spécifiquement l'humidité du sol dans un contexte de gestion durable des ressources en eau et en sol. En effet, ces nouveaux systèmes offrent aujourd'hui des produits opérationnels avec une forte répétitivité temporelle et des résolutions spatiales métriques permettant un suivi régulier. Dans notre contexte, les données radars sont particulièrement sensibles aux conditions de surface, précisément à l'humidité du sol, à la rugosité de surface et à la végétation. Ils se dévoilent comme les outils les plus prometteurs pour un suivi précis à l'échelle de la parcelle ou régionale. Ce travail comprend deux principales parties qui relient directement l'humidité du sol (variable clé pour différents processus) à l'irrigation dans un premier temps, puis à la texture du sol. L'approche adoptée combine les mesures expérimentales à l'utilisation de données de la télédétection multi-capteurs en synergie, ainsi à la modélisation et à la cartographie. La thèse se structure en trois volets. Le premier volet de ce travail évalue le potentiel des données radars en bande C pour une large base de données. Les résultats ont montré à travers des études du comportement et de modélisation que le signal radar permet de suivre la dynamique temporelle et spatiale de l'humidité du sol sur des parcelles de céréales. Le second volet, consiste à évaluer l'utilisation conjointe de données optiques et radars afin de pouvoir prédire l'état hydrique de surface sur une couverture végétale.[...

Étalonnage du modèle de nuages d'eau en bande C pour les champs de cultures d'hiver et les prairies

International audienceIn a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in VV and VH polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover

Irrigation mapping using products derived from Sentinel-1 and Sentinel-2 time series over a Mediterranean semi-arid region

International audienceIn order to ensure food security, semi-arid Mediterranean regions are largely dependent on irrigated agriculture. Irrigated agriculture in such areas can be highly productive and can also provide congenial living conditions. Because of the high contribution of irrigation, monitoring of it actual state is the major issue in these regions and knowing the spatial distribution and year-to-year variability in irrigated areas could be imperative for water resources management. With the arrival of Sentinel-1 and Sentinel-2 satellite, operational approaches are developed for monitoring surface states at the field scale with high spatial and temporal resolution. This present paper develops a methodology based on high spatial resolution remote sensing data for irrigation mapping. The inputs of the approach are the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data every 10 days, and soil moisture time series produced by the inversion of the Water Cloud Model (WCM) using a synergy of Sentinel-1, radar data in VV polarization and Sentinel-2 optical data every 6 days, over the Kairouan plain, in Central of Tunisia, North Africa. The first step was to divide an NDVI image into segments to delineate the agricultural fields. Then, a Support Vector Machine (SVM) classification is performed to distinguish between irrigated and non-irrigated areas, using the mean and variance values of soil moisture computed over the training cereal fields. Three cases were used to classify the fields, using a Decision Tree classification. The resulting irrigation maps were validated using ground truth measurements. The first case computed the mean value of NDVI on each segment, using an empirical threshold to delineate between the irrigated and rainfed fields. The overall accuracy of the classification was about 58%, due to the confusion between the two classes. Then, we combined, the mean value of NDVI and the mean and variance of soil moisture to obtain an overall accuracy of approximately 71 %. Finally, we used only the mean and variance values of soil moisture to produce the irrigation map. The best estimation was obtained using only soil moisture parameters with an accuracy of 77 %. This study demonstrates the high potential of combining radar and optical data for soil moisture estimation, which allows the monitoring of irrigation at the field scale

Multi-frequency radar signals for the retrieval of soil roughness parameters in a Mediterranean semi-arid region

International audienceOver bare agricultural areas, backscattered radar signal is very sensitive to physical soil characteristics particularly roughness and soil water content. Hence, different radar backscattering algorithms (theoretical, semi-empirical and empirical) were developed to modeling the relationship between backscattering coefficient and the soil parameters. Moreover, to retrieve these surface characteristics, mainly soil moisture content, by inversing backscattering models. However, the accuracy of the soil moisture estimation is affected by the influence of surface roughness parameter on backscattered radar signals. In this context, we propose to analyze the potential of a synergy between ALOS2 (L band), Sentinel1 (C band) and TerraSAR-X (X band) SAR measurements over bare soils to retrieve surface parameters. Ground campaigns were realized in central Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N) during four years (2013-2017). The climate in this region is semi-arid, with an average annual rainfall of approximately 300 mm per year, characterized by a rainy season lasting from October to May. Ground campaigns were carried over agricultural bare soil fields simultaneously to various radar measurements acquired in different configurations (multi-polarizations, multi-incidences, multi-resolution). Firstly, we analyzed statistically the backscattering coefficient behaviour as a function of various roughness parameters (the root mean surface height Hrms and the Zs parameter developed by (Zribi and Dechambre, 2002) at three radar frequency (L, C and X bands) and different radar configuration (incidence angle and polarization). Results show a high sensitivity of the SAR signals to all roughness parameters (Hrms and Zs). The correlation between backscattering coefficient σ° and roughness parameters increases clearly with increasing values of radar wavelengths. The strongest correlation (R2=0.54) is obtained with L band images. The second axis was a validation of the calibrated version of the Integral Equation Model (IEM) and the new empirical backscattering model of Baghdadi et al (2016). This validation uses different SAR wavelengths, incidence angles and polarizations coupled with in situ measurements (soil moisture and surface roughness) over bare soil. Results have shown that the new model simulates correctly the radar response with a bias better than -1,5 dB for different radar wavelengths (L, C, X). Finally, by inverting the new empirical model in L band, we produced the surface roughness parameter "Hrms" maps at high resolution scale. Our approaches are applied over bare soil class identified from an optical image Sentinel2 acquired in the same period of measurements. Then, we proposed an approach for the retrieval of surface soil moisture from Sentinel1 images. We corrected the sensitivity of the radar backscatter images to the surface roughness variability using the produced roughness map

Soil Moisture Estimation Over Cereal Fields Based on Sar ALOS-2 Data

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Cereal crops soil parameters retrieval using L-Band ALOS-2 and C-Band Sentinel-1 sensors

International audienceThis paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose,multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneouslywith in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). TheL- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. Highcorrelation coefficients were observed between the radar signals and soil roughness values for allprocessed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (SyntheticAperture Radar) data to soil moisture was investigated for three classes of the normalized differencevegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating adecreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture underthe dense cover class is observed in L-band data. For various vegetation properties (leaf area index(LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation isobserved with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical)polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soilmoisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B)and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations.The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysisis also proposed for covered surfaces using different options provided by the water cloud model(WCM) (with and without the soil–vegetation interaction scattering term) coupled with the bestaccuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for theentire dataset. Based on the validated backscattering models, different options of coupled models aretested for soil moisture inversion. The integration of a soil–vegetation interaction component in theWCM illustrates a considerable contribution to soil moisture precision in the HV polarization modein the L-band frequency and a neglected effect on C-band data inversion

ALOS-2 and Sentinel-1 use for retrieving soil moisture over cereal fields in semi-arid area: the Kairouan plain – central Tunisia

International audienceIn the present study, we evaluate the potential of multi-incidence L-band and C-band data to retrieve soil moisture. In -situ measurements were acquired during satellite acquisitions over cereal fields in the Kairouan plain in central Tunisia (semi-arid area). Analysing radar data, L-band Advanced Land Observing Satellite-2 multi-incidence data (28°, 32.5° and 36°) in HH (L-HH) and HV (L-HV) polarizations and C-band like-polarization Sentinel-1data, with an incidence angle of approximately 39°, (C-VV) are strongly impacted by soil roughness. In addition, results highlight the sensitivity of L-band data to soil moisture in dense cover class where Normalized Vegetation Difference Index (NDVI) values are higher than 0.6. Two options of Water Cloud Model (WCM) were used (with and without the integration of soil-vegetation interaction component) to simulate radar signal over cereal fields. Each option of WCM was coupled to the best performance bare soil backscattering models. By inverting WCM, results underline the important contribution of soil-vegetation interaction component to estimate soil moisture with L-HV data compared to a neglected impact on C-band data inversion accuracy and stable accuracy in L-HH

Optical and radar satellite synergy for the estimation of the surface water condition

International audienceThe aim of this study is to optimize an optical and radar data synergy for a regional mapping of soil water content, through experimental campaigns over agricultural fields in the Kairouan plain, in the central of Tunisia, during two agricultural seasons (2015-2016 and 2016-2017). Firstly, a radiative transfer model, Water Cloud Model is calibrated using NDVI index acquired from Sentinel-2 images to eliminate the vegetation effects on radar signal. The second research axe is to propose a semiempirical inversion method, using an inversion of the calibrated Water Cloud Model, and applied over bare soils and wheat fields (Irrigated and non irrigated fields). In this context, a mapping of surface moisture is proposed at 20 m spatial resolution with a six day repeat frequency for the entire studied site. This study reveals the high potential of Sentinel-1 data, when combined in synergy with optical images (Sentinel-2), for the recovery of moisture and vegetation characteristics. In this context, the proposed approach is validated ground truth measurements during the period (2015-2017). The maps produced from radar acquisitions are found to be reasonably correlated with the field measurements