Analyse des signaux radars polarimétriques en bandes C et L pour le suivi de l'humidité du sol de sites forestiers

Résumé : Dans les couverts forestiers, le suivi de l’humidité du sol permet de prévenir plusieurs désastres tels que la paludification, les incendies et les inondations. Comme ce paramètre est très dynamique dans l’espace et dans le temps, son estimation à grande échelle présente un grand défi, d’où le recours à la télédétection radar. Le capteur radar à synthèse d’ouverture (RSO) est couramment utilisé grâce à sa vaste couverture et sa résolution spatiale élevée. Contrairement aux sols nus et aux zones agricoles, le suivi de l’humidité du sol en zone forestière est très peu étudié à cause de la complexité des processus de diffusion dans ce type de milieu. En effet, la forte atténuation de la contribution du sol par la végétation et la forte contribution de volume issue de la végétation réduisent énormément la sensibilité du signal radar à l’humidité du sol. Des études portées sur des couverts forestiers ont montré que le signal radar en bande C provient principalement de la couche supérieure et sature vite avec la densité de la végétation. Cependant, très peu d’études ont exploré le potentiel des paramètres polarimétriques, dérivés d’un capteur polarimétrique comme RADARSAT-2, pour suivre l’humidité du sol sur les couverts forestiers. L’effet du couvert végétal est moins important avec la bande L en raison de son importante profondeur de pénétration qui permet de mieux informer sur l’humidité du sol. L’objectif principal de ce projet est de suivre l’humidité du sol à partir de données radar entièrement polarimétriques en bandes C et L sur des sites forestiers. Les données utilisées sont celles de la campagne terrain Soil Moisture Active Passive Validation EXperiment 2012 (SMAPVEX12) tenue du 6 juin au 17 juillet 2012 au Manitoba (Canada). Quatre sites forestiers de feuillus ont été échantillonnés. L’espèce majoritaire présente est le peuplier faux-tremble. Les données utilisées incluent des mesures de l’humidité du sol, de la rugosité de surface du sol, des caractéristiques des sites forestiers (arbres, sous-bois, litières…) et des données radar entièrement polarimétriques aéroportées et satellitaires acquises respectivement, en bande L (UAVSAR) à 30˚ et 40˚ et en bande C (RADARSAT-2) entre 20˚ et 30˚. Plusieurs paramètres polarimétriques ont été dérivés des données UAVSAR et RADARSAT-2 : les coefficients de corrélation (ρHHVV, φHHVV, etc); la hauteur du socle; l’entropie (H), l’anisotropie (A) et l’angle alpha extraits de la décomposition de Cloude-Pottier; les puissances de diffusion de surface (Ps), de double bond (Pd) extraites de la décomposition de Freeman-Durden, etc. Des relations entre les données radar (coefficients de rétrodiffusion multifréquences et multipolarisations (linéaires et circulaires) et les paramètres polarimétriques) et l’humidité du sol ont été développées et analysées. Les résultats ont montré que 1) En bande L, plusieurs paramètres optimaux permettent le suivi de l’humidité du sol en zone forestière avec un coefficient de corrélation significatif (p-value < 0,05): σ[indice supérieur 0] linéaire et σ[indice supérieur 0] circulaire (le coefficient de corrélation, r, varie entre 0,60 et 0,96), Ps (r entre 0,59 et 0,84), Pd (r entre 0,6 et 0,82), ρHHHV_30˚, ρVVHV_30˚, φHHHV_30˚ and φHHVV_30˚ (r entre 0,56 et 0,81) alors qu’en bande C, ils sont réduits à φHHHV, φVVHV et φHHVV (r est autour de 0,90). 2) En bande L, les paramètres polarimétriques n’ont pas montré de valeur ajoutée par rapport aux signaux conventionnels multipolarisés d’amplitude, pour le suivi de l’humidité du sol sur les sites forestiers. En revanche, en bande C, certains paramètres polarimétriques ont montré de meilleures relations significatives avec l’humidité du sol que les signaux conventionnels multipolarisés d’amplitude.Abstract : Over forest canopies, soil moisture monitoring allows to prevent many disasters such as paludification, fires and floods. As this parameter is very dynamic in space and time, its large-scale estimation is a great challenge, hence the use of radar remote sensing. Synthetic aperture radar (SAR) sensor is commonly used due to its wide spatial coverage and its high spatial resolution. Unlike bare soils and agricultural areas, only few investigations focused on the monitoring of soil moisture over forested areas due to the complexity of the scattering processes in this kind of medium. Indeed, the high attenuation of soil contribution by the vegetation and the high vegetation volume contribution significantly reduce the sensitivity of the radar signal to soil moisture. Studies conducted at C-band have shown that the radar signal mainly comes from the upper layer and it quickly saturates with the vegetation density. However, very few studies have explored the potential of polarimetric parameters derived from a fully polarimetric sensor such as RADARSAT-2, to monitor soil moisture over forest canopies. With its large penetration’s depth, vegetation cover effect is less important at L-band, allowing thus to better inform on soil moisture. The main objective of this project is to monitor soil moisture from fully polarime tric L and C bands radar data acquired over forested sites. The data used were collected during the field campaign of Soil Moisture Active Passive Validation EXperiment 2012 (SMAPVEX12) which took place from June 6 to July 17, 2012 in Manitoba (Canada). Four deciduous forested sites were sampled. The main species is the trembling aspen. The data used included measurements of soil moisture, soil surface roughness, characteristics of the forested sites (trees, undergrowth, litter, etc.) and fully polarimetric airborne and satellite radar data respectively acquired at L-band (UAVSAR) with 30 ̊ and at 40 ̊ incidence angles and at C-band (RADARSAT -2) between 20 ̊ and 30 ̊. Several polarimetric parameters were derived from UAVSAR and RADARSAT-2 data: the correlation c oefficients (ρHHVV, φHHVV, etc); the pedestal height; entropy (H), anisotropy (A) and alpha angle extracted from Cloude-Pottier decomposition; surface (Ps) and double bounce (Pd) scattering powers extracted from Freeman-Durden decomposition, etc. Relationships between radar backscattering data (multifrequency and multipolarisation (linear/circular) backscattering coefficients and polarimetric parameters) and soil moisture were developed and analyzed. The results showed that 1) at L-band, several optimal parameters allow soil moisture monitoring over forested sites with a significant correlation coefficient (p-value < 0.05): linear and circular σ[superscript 0] (the correlation coefficient, r, varies between 0.60 and 0.96), Ps (r varies between 0.59 and 0.84), Pd (r varies between 0.60 and 0.82), ρHHHV_30 ̊, ρVVHV_30 ̊, φHHHV_30 ̊ and φHHVV_30 ̊ (r varies between 0.56 and 0.81). However, at C-band, there are only few optimal parameters φHHHV, φVVHV and φHHVV (r is around 0.90) . 2) at L-band, polarimetric parameters did not show any added values for soil moisture monitoring over forested sites compared to multipolarised σ[superscript 0]. Nevertheless, at C-band some polarimetric parameters show better significant relationships with the soil moisture than the conventional multipolarised backscattering amplitudes

Polarimetric analysis through "SAR Polarimetry Target Analysis" as support to obtain Land Cover with the Corine Land Cover methodology.

La constante investigación y desarrollo que la comunidad científica ha hecho para desarrollar algoritmos que permitan obtener información polarimétrica de objetivos captados con imágenes SAR, se ha convertido en una herramienta útil en la obtención de información de coberturas de la tierra; es así como en esta investigación, se ha utilizado una imagen del programa RADARSAT 2 completamente polarizada (Quad Pol) tomada el 1 de septiembre de 2008, ubicada en la sabana de Bogotá, la que se procesó con el programa CATALYST Profesional, antes PCIGeomatics, con el objeto de determinar las características o firmas polarimétricas de coberturas terrestres obtenidas con la metodología CORINE Land Cover (tejido urbano continuo, cultivos anuales o transitorios, cultivos confinados, pastos limpios, bosque natural denso, bosque plantado, herbazal denso, arbustal denso y aguas continentales). Para ello, se aplicaron procesos de filtrado de la imagen SAR para reducir el ruido o speckle, como el filtro de Lee 5 X 5 y BoxCar 3 X 3, además se ejecutaron los siguientes algoritmos de descomposición polarimétrica: Krogager, Cloude & Pottier (Entropy, alpha, beta, and anisotropy), Freeman & Durden, PHDW, Touzi y Van Zyl, así como algoritmos de transformación de matrices: C4R6C y C3R3C. A las capas resultado se les realizó un análisis visual con el objeto de inferir características particulares y relevantes como (intensidad, textura, compacidad, forma) para cada tipo de cobertura terrestre, posteriormente dichas capas resultado se intentaron desplegar en el módulo SAR Polarimetry Target Analysis – SPTA por su sigla en inglés-, no obstante, solo los dos archivos con filtro y los dos archivos de transformación de matrices se lograron desplegar y analizar debido a que son totalmente polarizadas. A partir de estas cuatro capas, se obtuvieron características polarimétricas de las coberturas de la tierra, antes mencionadas, por los métodos de co-polarización, Cloude & Pottier (H/A/α/β) y Freeman & Durden. Por último, se realizó la clasificación supervisada a través de análisis de objetos por los métodos de Máquinas de Soporte Vectorial – VSM, por su sigla en inglés- y Bosques Aleatorios – RF, por su sigla en inglés- sobre las imágenes SAR con descomposición de Krogager, Freeman & Durden y PHDW, obteniendo coeficientes Kappa de 0.634, 0.600 y 0.637 respectivamente, los que entran en el rango de clasificación de calidad muy buena.The constant research and development carried out by the scientific community to develop algorithms to obtain polarimetric information from targets captured with SAR imagery has become a useful tool for obtaining information on land cover; thus, in this research, a fully polarized image of the RADARSAT 2 programme (Quad Pol) taken on 1st September 2008 was used, located on the Bogota, which was processed with the CATALYST Professional program, formerly PCIGeomatics, in order to determine the characteristics or polarimetric signatures of land cover obtained with the CORINE Land Cover methodology (continuous urban fabric, annual or transient crops, confined crops, clean pastures, dense natural forest, planted forest, dense grassland, dense shrubland and inland waters). For this purpose, SAR image filtering processes were performed to reduce noise or speckle, such as the Lee filter 5 X 5 and BoxCar 3 X 3, and the following polarimetric decomposition algorithms were applied: Krogager, Cloude & Pottier (Entropy, alpha, beta, and anisotropy), Freeman & Durden, PHDW, Touzi and Van Zyl, as well as matrix transformation algorithms: C4R6C and C3R3C. The result layers were visually analyzed in order to infer particular characteristics and relevant as (intensity, texture, compactness, shape) for each type of land cover. Subsequently, these result layers were deployed in the SAR Polarimetry Target Analysis (SPTA) module. However, only the two filtered files and two matrix transformation files could be deployed and analyzed due to that they are totally polarized. From these four layers, polarimetric characteristics of the above-mentioned land cover were obtained by the co-polarization methods, Cloude & Pottier (H/A/α/β) and Freeman & Durden. Finally, the supervised classification was carried out through object analysis by the methods of Vector Support Machines (VSM) and Random Forests (RF) on SAR images with decomposition of Krogager, Freeman & Durden and PHDW, obtaining Kappa coefficients of 0. 634, 0. 600 and 0. 637 respectively, those that fall into the very good quality classification range

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale