Analyse des cycles gel/dégel des régions nordiques par télédétection micro-ondes passives en bande L

Le réchauffement climatique dans les régions nordiques, fort important depuis le milieu du siècle dernier, a de multiples impacts sur la dynamique des écosystèmes, notamment sur les cycles gel/dégel de surface qui influencent les flux de carbone, l'activité biogéochimique des sols, l'hydrologie et le pergélisol aux hautes latitudes. La télédétection satellitaire du gel/dégel par micro-ondes passives est un outil très prometteur permettant un suivi continu et global, mais comporte des difficultés souvent reliées à l’effet d’hétérogénéité spatiale intra-pixel relié aux résolutions grossières des capteurs micro-ondes passives à basse fréquence. L’objectif principal du projet est d’évaluer l’utilisation de la télédétection micro-onde passive en bande L (1.4 GHz) pour le suivi de l’état de gel/dégel de la surface en forêt boréale. Un premier objectif spécifique est d’évaluer un nouveau produit des cycles de gel/dégel de surface estimée à partir des radiomètres bande L satellitaires Aquarius. Cette base de données de 3.5 années a été mise en ligne au National Snow and Ice Data Center (NSIDC). Le deuxième objectif spécifique est d’analyser l’effet de la variabilité spatiale intrapixel de l’état de gel du sol et de son impact sur les températures de brillance (TB) mesurées par le radiomètre de la mission Soil Moisture Active Passive (SMAP) en période de transition afin de quantifier la fraction de sol gelé. Les résultats pour le premier objectif montrent que la nouvelle base de données possède une bonne capacité à estimer l’état de gel/dégel de la surface sur l’ensemble de l’Hémisphère Nord (> 50°N). Cette recherche offre également une rare intercomparaison entre produits de gel/dégel satellitaires en comparant le produit Aquarius au Freeze/Thaw-Earth System Data Record (FT-ESDR) développé avec les données à plus hautes fréquences du capteur SSM/I. Pour le deuxième objectif, des capteurs de température distribués le long de transects de plusieurs kilomètres sur deux différents sites de taïga montrent que la variabilité spatiale du gel à l’automne peut être de 7.5 à 9.5 semaines. Il est également démontré que les mesures de SMAP sont sensibles à cette variabilité et un algorithme développé permet d’estimer le pourcentage intrapixel de sol gelé avec des coefficients de détermination (R2) entre 0.63 et 0.88 lorsque comparé aux mesures in situ. Ces résultats offrent de nouveaux outils pour mieux comprendre et quantifier les cycles de gel/dégel de l’environnement boréal et leurs impacts sur les processus biogéophysiques, hydrologiques et sur le pergélisol.Abstract: Climate change in nordic regions, which has been of growing significance over the past century has multiple impacts on the dynamic of ecosystems, notably on the surface freeze/thaw cycles, which influences carbon flux, soil biogeochemical activity, hydrology and permafrost at high latitudes. Satellite remote sensing of freeze/thaw with passive microwaves is a promising tool to offer continuous and global monitoring, but can also entail some difficulties due to intra-pixel spatial variability effects coming from the low resolution of low-frequency passive microwave sensors. The primary objective of the project is to evaluate the use of passive microwave remote sensing in L-band (1.4 GHz) for monitoring of the surface freeze/thaw in the boreal forest. A first specific objective is to evaluate a new surface freeze/thaw product estimated by the Aquarius satellite L-band radiometers. This 3.5 year-old database has been put online at the National Snow and Ice Data Center (NSIDC) website. The second specific objective is to analyse the effect of intra-pixel spatial variability of freeze/thaw and its impact on brightness temperatures (TB) measured by the Soil Moisture Active Passive (SMAP) radiometer during transition periods in order to quantify the frozen soil fraction. Results for the first objective show that the new database possesses a good capacity to estimate the surface freeze/thaw state for the entirety of the Northern Hemisphere (>50°N). This research also offers a rare intercomparison between freeze/thaw satellite products by comparing the Aquarius product to the Freeze/Thaw-Earth System Data Record (FT-ESDR) product developed with higher frequencies data of the SSM/I sensor. For the second objective, temperature sensors distributed along transects of several kilometers on two different taiga sites show that the spatial variability of autumn soil freeze onset can be between 7.5 and 9.5 weeks. It demonstrates that SMAP measurements are sensitive to this variability and a developed algorithm offers estimations of the intrapixel soil frozen fraction with coefficients of determination (R2) between 0.63 and 0.88 when compared to in situ measurements. These results offer new tools for a better understanding and quantification of freeze/thaw cycles in boreal environments and their impacts on biogeochemical and hydrologic processes and on permafrost

The simulation of L-band microwave emission of frozen soil during the thawing period with the Community Microwave Emission Model (CMEM)

One-third of the Earth's land surface experiences seasonal freezing and thawing. Freezing-thawing transitions strongly impact land-atmosphere interactions and, thus, also the lower atmosphere above such areas. Observations of two L-band satellites, the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions, provide flags that characterize surfaces as either frozen or not frozen. However, both state transitions-freezing and thawing (FT)-are continuous and complex processes in space and time. Especially in the L-band, which has penetration depths of up to tens of centimeters, the brightness temperature (TB) may be generated by a vertically-mixed profile of different FT states, which cannot be described by the current version of the Community Microwave Emission Model (CMEM). To model such complex state transitions, we extended CMEM in Fresnel mode with an FT component by allowing for (1) a varying fraction of an open water surface on top of the soil, and (2) by implementing a temporal FT phase transition delay based on the difference between the soil surface temperature and the soil temperature at 2.5 cm depth. The extended CMEM (CMEM-FT) can capture the TB progression from a completely frozen to a thawed state of the contributing layer as observed by the L-band microwave radiometer ELBARA-III installed at the Maqu station at the northeastern margin of the Tibetan Plateau. The extended model improves the correlation between the observations and CMEM simulations from 0.53/0.45 to 0.85/0.85 and its root-mean-square-error from 32/25 K to 20/15 K for H/V-polarization during thawing conditions. Yet, CMEM-FT does still not simulate the freezing transition sufficiently.</p

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions

Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.publishedVersio

Detection of soil permittivity and soil freezing using satellite microwave radars

Remote sensing of soil permittivity and soil freezing was investigated using two different satellite based microwave radars: ASCAT and ASAR. ASCAT is a scatterometer with a good temporal resolution but coarse spatial resolution. ASAR is a synthetic aperture radar and has fine spatial resolution, but lacks good temporal coverage. Soil permittivity is related to soil moisture, which is considered an essential climate vari- able since it has an effect on both weather and climate. Soil freezing affects hydrological and carbon cycles, surface energy balance, photosynthesis of vegetation and the activity of soil microbes. A semi-empirical model for backscattering of forested land was used to acquire soil permittivity retrievals from satellite measurements using the method of least squares. The onset of soil freezing was determined from the permittivity retrievals using a simple threshold method. A five year time series of satellite observations from July 2007 to June 2012 (April 2012 for ASAR) was investigated in Sodankylä in Northern Finland. The satellite based retrievals were compared against in situ measurements of soil permittivity, soil temperature, soil frost and snow depth. According to the results the satellite permittivity retrievals correlate with each other, but not with in situ permittivity measurements. ASCAT retrieval shows some correlation with in situ temperature measurements, which could impair its correlation with in situ permittivity. The explanation for this phenomenon needs further research. Comparison of soil freezing onset dates from satellite retrievals with in situ soil temperature and soil frost measurements showed quite good agreement for most years, and did not seem to be affected by first snowfall, even though the permittivity retrievals appeared to react in a similar way to snow cover and soil freezing. This indicates that with better calibration of the permittivity threshold limit this method could be used for soil freeze detection. Auxiliary information about air temperature and snow cover could also be used to filter out possible false estimates before freezing and after the snow cover starts to affect the satellite retrievals

Simulation of SMAP and AMSR2 observations and estimation of multi-frequency vegetation optical depth using a discrete scattering model in the Tibetan grassland

Passive microwave observation at multiple frequencies has received increasing research interests due to its capability to provide comprehensive information of land surface properties. This paper contributes to the simulation of land surface emission and estimation of vegetation optical depth (VOD) at multiple frequencies using a discrete scattering model with a single set of model parameter values. Validity of the Tor Vergata (TVG) discrete scattering model in simultaneously reproducing the Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) and Advanced Microwave Scanning Radiometer 2 (AMSR2) C- (6.925 GHz) and X-band (10.7 GHz) observations over the Tibetan grassland ecosystem is evaluated. Frequency-specific and multi-frequency calibration strategies are implemented to find the suitable set of model parameter values and to isolate the impact of frequency on parameter values. On this basis, the calibrated TVG model is further used to estimate the VOD, and to investigate the impact of microwave frequency and observation angle on the emission simulations and VOD parameterization. The results show that both frequency-specific and multi-frequency calibration strategies achieve comparable and reasonable simulations of SMAP and AMSR2 observations, confirming the feasibility of using an identical physically-based model (i.e. the calibrated TVG model) to simulate multi-frequency land emission driven by a single set of model parameter values. As such, the dependence of emission components and VOD on frequency can be elaborated after isolating the impact of frequency on parameter values. The VOD values derived from the TVG simulations generally increase with increasing frequency and can be linearly correlated to the LAI variations, while current satellite-based retrievals have almost the same magnitude at the L-, C-, and X-band. The explanation for this can be that the retrieved VOD is different from the theoretical definition. Sensitivity test performed using the calibrated TVG model further shows that polarization-dependence of VOD becomes more apparent with the increasing observation angle and frequency. New parameterization has thus been developed to characterize the dependence of VOD on the frequency, observation angle, and polarization for grassland based on the results of sensitivity test. This study may provide new insights in improving model of land emission and retrievals of SM and VOD with physical interpretability based on multi-frequency satellite observations.</p

Monitoring Water and Energy Cycles at Climate Scale in the Third Pole Environment (CLIMATE-TPE)

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented

Détection des cycles de gel/dégel de la couche active du sol en toundra arctique à partir d’imageries radar à synthèse d’ouverture (RSO) multicapteur en bande C

L’augmentation de la température de l’air moyenne annuelle, chiffrée à +2,3 °C pour les régions de l’arctique Canadien entre 1948 et 2016, a des impacts considérables sur le couvert nival arctique et sur la végétation en place. Ces deux paramètres influencent le régime thermique du sol et donc, les cycles de gel/dégel de sa couche active dans l’écosystème arctique. L’importance du suivi de ces cycles réside dans leur influence sur plusieurs paramètres de la cryosphère tels que le cycle hydrologique et du carbone, la saison de croissance de la végétation, l’état du pergélisol sous-jacent ainsi que l’épaisseur de sa couche active. L’utilisation de données ponctuelles ou provenant de capteurs micro-onde passive à basse résolution présente un enjeu pour le suivi spatial et temporel de ces cycles. Le projet vise à développer un algorithme de détection des cycles de gel/dégel du sol en toundra arctique à partir d’imageries RSO multicapteur (i.e., Sentinel-1 et RADARSAT-2) ayant une couverture temporelle quasi journalière en bande C, afin d’évaluer l’impact de la variabilité spatiale et temporelle des paramètres influençant le régime thermique du sol tel que, les écosystèmes terrestres (i.e., écotype) et la présence de neige. L’étude se concentre sur une zone à l’intérieur du bassin versant du lac Greiner à proximité de la ville de Cambridge Bay au Nunavut. La normalisation de l’angle d’incidence a permis de diminuer le bruit dans les séries temporelles ainsi que de rendre possible l’utilisation d'images acquises à l'intérieur de plusieurs orbites d’observation. Cela a aussi permis d’uniformiser les données des deux capteurs pour les combiner en une seule série temporelle. Deux algorithmes de détections ont été utilisés, soit un algorithme de seuil saisonnier (STA) ainsi qu’un algorithme de détection de changement (CPD). La validation s’est faite à partir des données spatialement distribuées de température du sol et de l’air indépendamment sous forme de précision (%) et de délai (#jours) de détection. Les deux algorithmes ont permis d’obtenir une précision de détection de plus de 97% sur les sites de référence. Une spatialisation, pixel par pixel, de la méthode STA a permis la création de cartes de jour de gel/dégel pour le site d’étude. La combinaison des cartes de jour de transition avec la carte d’écotype a permis de modéliser l’impact des caractéristiques des écotypes sur le jour de transition. Les résultats obtenus dans ce projet démontrent clairement le potentiel de l’utilisation des données RSO en bande C pour la détection des cycles de gel/dégel, ce qui constitue un résultat important en raison de la quantité grandissante de données à cette fréquence (e.g., RCM, Sentinel-1A-C-D). La méthode présentée dans ce projet pourrait permettre de créer des cartes de transition pour tout le bassin versant du lac Greiner à partir de données RSO en bande C.Abstract : The observed average annual surface temperature increase of 2.3°C in the Canadian Arctic regions between 1948 and 2016 has significant effects on the Arctic snow cover and on the vegetation in place. Those two parameters influence the thermal regime of the ground and therefore the freeze and thaw (F/T) cycles of the soil active layer in the Arctic tundra ecosystem. The importance of monitoring these cycles lies in their influence on several parameters of the cryosphere such as the hydrological and carbon cycle, the vegetation growing season, the state of the underlying permafrost and the thickness of its active layer. The use of punctual data or low-resolution passive microwave sensors presents a challenge for the spatial and temporal monitoring of these cycles. The project aims to develop an algorithm for soil freeze/thaw cycles detection in arctic tundra from multisensor C-band imagery (i.e., Sentinel-1 and RADARSAT-2) to assess the impact of the spatial and temporal variability of the parameters influencing the thermal regime of the ground, such as the terrestrial ecosystems (i.e., ecotype) and the snow cover. The study focused on a region of the Greiner lake watershed on Victoria Island in Nunavut. An incidence angle normalization was applied to the backscatter time series to remove influence of the acquisition angle on backscatter and to allow for the use of images acquired within several orbits of observation. This also standardized the data from the two sensors to combine them into a single time series. Two detection algorithms were used on the normalized backscatter coefficient data, namely a seasonal threshold algorithm (STA) and a change point detection algorithm (CPD). A spatially distributed network of soil and air temperature were used for validation in the form of accuracy (%) and delay (#days) of detection. Both algorithms achieved a detection accuracy of more than 97% for the entire analysis period on the reference sites. A pixel-by-pixel spatialization of the STA method allowed to create F/T transition maps for the extended study site. The combination of the transition maps with the ecotype data made it possible to model the impact of ecotype characteristics on the day of transition. The results obtained in this project clearly demonstrate the potential of using C-band for the detection of F/T cycles, which is an important aspect due to the increasing number of data at this frequency (e.g., RCM, Sentinel -1A-C-D). The method presented in this project could then make it possible to create transition maps for the entire Greiner Lake watershed from C-band SAR data and thus improve the integration of this parameter in climate models