Jamal Elfarkh

Une évaluation précise de l'évapotranspiration (ET) est indispensable à l'échelle du bassin pour étudier les processus hydrologiques et le bilan hydrique, en particulier de l'amont vers l'aval. Dans les montagnes, ce terme est mal connu en raison de divers défis, notamment la diversité de la végétation, la variabilité topographique, le manque de données disponibles et la difficulté d'effectuer des mesures sur ces terrain complexe. Dans ce contexte, l'objectif principal de cette thèse est d'étudier le potentiel de différents modèles basés sur la télédétection, à savoir TSEB (Two-Source-Energy-Balance model), SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), Shuttleworth-Wallace (SW) and SAMIR (SAtellite Monitoring of IRrigation), pour estimer l'ET sur une région du piémont du montagne. Premièrement, les modèles ont été testés à l'échelle locale en comparant avec les mesures de deux systèmes d'Eddy covariance (EC). Deuxièmement, la performance des modèles à l'échelle de la zone d'étude a été évaluée en pondérant les estimations par le footprint d'un scintillomètre à grande ouverture (LAS) et en les comparant aux mesures. D'abord TSEB basé sur les données de télédétection, MODIS et Landsat, a été testé à l'échelle locale en comparant ses estimations aux mesures EC. Quant à l'échelle locale, les résultats de comparaison des flux de chaleur latente simulés par TSEB basé sur les données Landsat sont très encourageants en comparant aux mesurés LAS, tandis qu'une dispersion plus importante a été observée lors de l'utilisation des produits MODIS. En suit, SW a été testé à l'échelle locale et de bons accords avec la mesure ont été démontrés. Cependant, ce modèle a besoin de nombreuses variables d'entrée pour calculer les résistances du sol (rss) et de la végétation (rsv), qui sont souvent difficiles à estimer à grande échelle, en particulier l'humidité du sol. Pour ce fait, une nouvelle approche pour spatialiser rss et rsv basée sur deux proxys de variables thermiques a été proposée. La comparaison entre les valeurs ET estimées par la nouvelle approche et mesurées confirme la capacité de l'approche proposée à fournir des cartes ET satisfaisantes. SPARSE a montré des performances acceptables pour l'estimation des flux de chaleur latente et sensible. Cependant, il est limité par plusieurs hypothèses et incertitudes. Notamment, la fiabilité des données de température de surface. De plus, le fait de prendre des paramètres calibrés constants sur toute l'année dans différentes conditions de végétation et d'environnement n'est pas représentatif d'une surface aussi complexe, en particulier la résistance stomatique minimale (rstmin). Le modèle SAMIR (Satellite Monitoring of Irrigation), est utilisé pour effectuer un suivi journalier du bilan hydrique de la zone étudiée. SAMIR a montré une très bonne performance pour la prévision de l'ET.

Land Surface Temperature (LST) is a key variable used across various applications, including irrigation monitoring, vegetation health assessment and urban heat island studies. While satellites offer moderate-resolution LST data, unmanned aerial vehicles (UAVs) provide high-resolution thermal infrared measurements. However, the continuous and rapid variation in LST makes the production of orthomosaics from UAV-based image collections challenging. Understanding the environmental and meteorological factors that amplify this variation is necessary to select the most suitable conditions for collecting UAV-based thermal data. Here, we capture variations in LST while hovering for 15–20 min over diverse surfaces, covering sand, water, grass, and an olive tree orchard. The impact of different flying heights and times of the day was examined, with all collected thermal data evaluated against calibrated field-based Apogee SI-111 sensors. The evaluation showed a significant error in UAV-based data associated with wind speed, which increased the bias from −1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. Different surfaces, albeit under varying ambient conditions, showed temperature variations ranging from 1.4 to 6 °C during the flights. The temperature variations observed while hovering were linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. Irrigation and atmospheric conditions (i.e., thin clouds) also contributed to observed temperature variations. This research offers valuable insights into LST variations during standard 15–20 min UAV flights under diverse environmental conditions. Understanding these factors is essential for developing correction procedures and considering data inconsistencies when processing and interpreting UAV-based thermal infrared data and derived orthomosaics.

Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground sampling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution (<10 m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420–1000 nm) and 10 nm in the SWIR range (900–2450 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD.

Land surface temperature (LST) is crucial information that helps to understand and assess the interactions between the surface and the atmosphere. LST is a key parameter used in various applications including studies of irrigation, water use, vegetation health, urban heat island effects, and building insulation. In addition to several satellites that provide periodic images of surface temperature, unmanned aerial vehicle (UAV) platforms have been adapted to obtain higher spatio-temporal resolution thermal infrared (TIR) data. In fact, numerous research studies have investigated the accuracy and the processing method of UAV-based TIR images given its complexity and sensitivity to ambient conditions. However, the surface temperature is characterized by continuous and rapid variation over time, which is difficult to take into consideration in the processing of UAV-based orthomosaics. Here, we quantify this variation and discuss the environmental factors that lead to its amplification. Thermal images were collected over a fixed hovering position during periods of 15-20 min, representing the common duration of UAV flights. At different times of the day, we flew at different altitudes over sand, water, grass and olive trees. Before the quantification of the surface temperature variation, the thermal infrared data were evaluated against field-based measurements using calibrated Apogee sensors. The evaluation showed a significant error in the UAV-based thermal infrared data linked to wind speed, which increased the bias from -1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. The assessment of the LST values collected over the different surfaces showed a temperature variation while hovering ranging between 1.4 and 5 °C. In addition to wind effects, temperature variations while hovering were strongly linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. This research provides insights into the LST variation expected for standard UAV flights of 15-20 min under different environmental conditions, which should be taken into account during UAV-based thermal infrared data processing and may help interpret and quantify inconsistencies in UAV-based orthomosaics of LST.