An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets:high sensitivity of L-VOD to above-ground biomass in Africa

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (LVOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse (similar to 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass

Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales

Abstract The relationship between vegetation optical depth (VOD) retrieved by L-band SMOS radiometer and forest above ground biomass (AGB) was investigated in tropical areas of Africa and South America. VOD was retrieved from the latest version of level 2 SMOS algorithm, while reference AGB was obtained from a pantropical database, encompassing a large number of ground plot data derived from field surveys conducted on both continents. In Africa and South-America, VOD increased with AGB, reaching saturation at about 350 Mg ha−1. The strength of the relation was improved selecting VOD data in appropriate seasons, characterized by a higher dynamic range of values. The capability of VOD data to estimate AGB was further investigated using Random Forest decision trees, adding to VOD selected climate variables from the Climatic Research Unit (temperature, potential evapotranspiration, and precipitation) and water deficit data, and validating regression tests with ground data from the reference AGB database. The results for the five analyzed years indicate that the best estimates of AGB are obtained by the joined use of VOD and potential evapotranspiration input data, but all climate variables brought an improvement in AGB estimates. AGB estimates were relatively stable for the considered period, with limited variations possibly due to changes in biomass and to data quality of VOD and of climate variables. The VOD signal and estimated AGB were also analyzed according to ecological homogeneous units (ecoregions), evidencing data clusters, partially overlapped to each other, in the VOD - AGB plane

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

Statistical analysis and combination of active and passive microwave remote sensing methods for soil moisture retrieval

Knowledge about soil moisture and its spatio-temporal dynamics is essential for the improvement of climate and hydrological modeling, including drought and flood monitoring and forecasting, as well as weather forecasting models. In recent years, several soil moisture products from active and passive microwave remote sensing have become available with high temporal resolution and global coverage. Thus, the validation and evaluation of spatial and temporal soil moisture patterns are of great interest, for improving soil moisture products as well as for their proper use in models or other applications. This thesis analyzes the different accuracy levels of global soil moisture products and identifies the major influencing factors on this accuracy based on a small catchment example. Furthermore, on global scale, structural differences betweenthe soil moisture products were investigated. This includes in particular the representation of spatial and temporal patterns, as well as a general scaling law of soil moisture variability with extent scale. The results of the catchment scale as well as the global scale analyses identified vegetation to have a high impact on the accuracy of remotely sensed soil moisture products. Therefore, an improved method to consider vegetation characteristics in pasive soil moisture retrieval from active radar satellite data was developed and tested. The knowledge gained by this thesis will contribute to improve soil moisture retrieval of current and future microwave remote sensors (e.g. SMOS or SMAP)

Land-atmosphere interactions and their effect on Australian precipitation

The aim of the research presented in this thesis is to determine the influence of land-atmosphere interactions on Australian precipitation, both under average conditions and during drought. This aim is addressed using a combination of statistical and numerical atmospheric water accounting techniques. The first part of the research examines soil moisture, a key variable that underpins the analysis of land-atmosphere interactions. Due to the range of estimation techniques and variety of applications utilising soil moisture information, numerous data sets are available. This thesis evaluates soil moisture from ground, satellite and model estimates across Australia and identifies data sets suitable to the study of land-atmosphere interactions and other applications. Soil moisture information was then combined with observations of precipitation to identify where land-atmosphere interactions have a detectable influence on Australian precipitation. Analysing the statistical relationship between soil moisture and subsequent precipitation, the results showed detectable relationships in north and southeast Australia and the importance of scale in interpreting physical relationships with a statistical metric. With regions of land-atmosphere interaction identified, the next stage of the research quantified the interaction with the precipitation recycling ratio - a measure of how much of a region's precipitation is derived from evaporation in that same region. Precipitation recycling was quantified using a "back-trajectory" model that identified the evaporative moisture sources of Australia's precipitation. Strongest land-atmosphere interactions and recycling were found in the north and southeast of the continent in spring and summer, along with long term trends in regional moisture sources. The importance of land-atmosphere interaction during drought was the subject of the final stage of the research. Focusing on the Murray-Darling Basin in southeast Australia, the research analysed the sources of moisture supplying precipitation and the degree to which the land surface amplified precipitation anomalies during drought onset, persistence and termination. The results indicate that major droughts were driven by reduced moisture supply from the ocean, as moisture was circulated away from the region, combined with an absence of precipitation-generating mechanisms over land. Droughts terminated when moist easterly flows from the Tasman and Coral Seas strengthened, promoting high precipitation. Terrestrial moisture sources played a secondary role, amplifying precipitation anomalies by less than 6%. In summary, the research presented in this thesis has determined the influence of land-atmosphere interactions on Australian precipitation, both under average conditions and during drought. The analysis demonstrates that Australian precipitation is predominantly driven by large scale processes transporting marine moisture to the continent for precipitation, with terrestrial moisture sources forming an important contribution to precipitation in the north and southeast of the continent. In the southeast, drought is driven by atmospheric circulation anomalies redirecting ocean moisture away from the region, with land-atmosphere interactions playing a secondary role

Etude du panache du fleuve Rouge dans le golfe du Tonkin à partir d'une analyse en clusters et de simulations d'ensemble

Cette étude vise à mieux comprendre la variabilité du panache du Fleuve Rouge dans le Golfe du Tonkin (GOT) dans la zone proche de l'embouchure et plus au large, en utilisant la modélisation numérique. Comprendre la variabilité du panache et le devenir des eaux du delta est d'une importance capitale pour une connaissance approfondie et une meilleure capacité de prédiction de la circulation océanique et de l'hydrologie dans le GOT, ainsi que pour une meilleure gestion des eaux côtières et surveillance des écosystèmes côtiers. Dans la première partie de la thèse, une configuration du modèle SYMPHONIE est mise en place avec des forçages réalistes et une grille à haute-résolution variable, sur la base de la configuration de V. Piton (2019). Une simulation sur une période de 6 ans (2011-2016) est réalisée pour étudier la variabilité journalière à interannuelle du panache du Fleuve Rouge et de trois rivières dont l'embouchure est voisine. La simulation est ensuite comparée à plusieurs sources d'observations. Ensuite, le panache est identifié à l'aide de traceurs passifs injectés dans la simulation. En utilisant un algorithme d'apprentissage automatique non supervisé (K-means), les principaux régimes du panache et leur évolution dans le temps sont classifiés et analysés selon quatre clusters, puis liés à différentes conditions environnementales. En hiver, le panache est étroit et reste la plupart du temps le long de la côte en raison du courant côtier et du vent de nord-est. Au début de l'été, le vent de la mousson du sud-ouest fait s'écouler le panache vers le large. Le panache atteint sa plus grande couverture en septembre, après le pic du débit. Sur la verticale, l'épaisseur du panache montre également des variations saisonnières. En hiver, le panache est mélangé sur toute la colonne d'eau, alors qu'en été, le panache peut être détaché à la fois du fond et de la côte. Le panache peut s'approfondir au large en été, en raison de vents forts (en mai, juin) ou spécifiquement en raison d'un tourbillon récurrent se produisant près de 19°N (en août). Cette première partie a fait l'objet d'une publication en 2021. L'analyse en clusters ci-dessus montre que, quel que soit le cluster, le panache est fortement affecté par le vent. Par conséquent, dans la deuxième partie de cette thèse, j'utilise un ensemble de simulations pour évaluer la réponse du modèle aux perturbations ajoutées au vent forçant. La sensibilité de la simulation présentée dans la première partie est évaluée statistiquement en calculant la dispersion et la distribution des variables d'intérêt à partir d'un ensemble de 50 membres. En raison des contraintes de calcul et de mémoire, cette étude est réalisée sur une courte période, de juin à août 2015, correspondant à la saison de fort débit. Tout d'abord, l'erreur sur le vent forçant est estimée par comparaison avec un produit satellitaire. Ensuite, son impact sur le modèle est évalué pour les variables de surface et de subsurface. Pour la température et la salinité de surface, l'incertitude est plus élevée près de la côte vietnamienne et du delta du fleuve Rouge. Sur la verticale, l'incertitude est la plus forte à la surface pour la salinité et en sub-surface pour la température. J'analyse ensuite la sensibilité du panache de la rivière. La dispersion de la surface du panache est maximale en août, qui est aussi la période où la surface du panache est la plus grande. L'analyse en clusters montre quelques changements de clusters entre les différents membres de l'ensemble, mais le cluster le plus susceptible de se produire est toujours celui de la simulation de référence (avec le vent non perturbé). Ces changements limités suggèrent que les résultats de la partie I sont effectivement robustes aux erreurs de forçage du vent. Enfin, l'ensemble est vérifié en utilisant les jeux d'observations disponibles.This study aims at better understanding the variability of the Red River plume in the Gulf of Tonkin (GOT) in the mid and far field area using a numerical modeling approach. Understanding the plume variability and the fate of the delta waters is of primary importance for an in-depth knowledge and a better prediction capacity of the ocean circulation and hydrology in the GOT, for an improved management of coastal waters and monitoring of the coastal ecosystems. In the first part of the thesis, the SYMPHONIE model is configured with realistic forcings and a high-resolution variable grid relying on the configuration of V. Piton (2019). It is then run over a 6-year (2011-2016) period to study the daily to interannual variability of the Red River plume and of three rivers whose mouths are nearby. It is then validated with several observational data sources. Then, the plume is identified using simulated passive tracers. Using a K-means unsupervised machine learning algorithm, the main patterns of the plume and their evolution in time are classified in four clusters, analyzed and linked to different environmental conditions. In winter, the plume is narrow and sticks along the coast most of the time due to the downcoast current and northeasterly wind. In early summer, the southwest monsoon wind makes the plume flow offshore. The plume reaches its highest coverage in September after the peak of runoff. Vertically, the plume thickness also shows seasonal variations. In winter, the plume is mixed over the whole water depth, while in summer, the plume can be detached both from the bottom and the coast. The plume can deepen offshore in summer, due to strong wind (in May, June) or specifically due to a recurrent eddy occurring near 19°N (in August). This first part was published in 2021. The clustering analysis above shows that whatever the cluster, the plume is strongly affected by the wind. Therefore, in the second part of this thesis, I use an ensemble of simulations to assess the model response to perturbations added to the wind forcing. The sensitivity of the simulation presented in the first part is statistically evaluated by calculating the spread and the distribution of the variables of interest from an ensemble of 50 members. Due to computing and memory constraints, this study is performed over a short period, from June to August 2015, corresponding to the high runoff season. Firstly, the error of the forcing wind is estimated by comparing it with a satellite product. Then, its impact onto the model is assessed for surface and subsurface variables. For the sea surface temperature and salinity, the uncertainty is higher near the Vietnamese coast and the Red River delta. Vertically, the uncertainty is highest at the surface for salinity and at the sub surface for temperature. The sensitivity of the river plume is then analyzed. The spread of the plume area is highest in August, which is the same time when the plume area reaches its peak. The clustering analysis shows some cluster shifts between different members of the ensemble, but the cluster that is most likely to occur is still the one from the reference simulation (with unperturbed wind). These limited changes suggest that the results of part I are indeed robust to the wind forcing errors. Finally, the ensemble is verified using the available observational datasets

Etude diagnostique de la variabilité de la salinité de surface de l'Océan Pacifique. Apport des données SMOS

La salinité est un paramètre essentiel de l'océan car elle impacte les processus océaniques de la sous-meso échelle à l'échelle du bassin et interannuelle. Son rôle a été souligné dans la dynamique du phénomène El Nino ainsi que dans le déplacement de masses d'eaux telles que les eaux intermédiaires subtropicales et les eaux profondes. Elle est considérée comme une Variable Climatique Essentielle par l'Organisation Météorologique Mondiale. La distribution du sel dans l'océan est le résultat d'un équilibre subtil entre le forçage de surface (évaporation, précipitation et ruissellement), l'advection horizontale de sel et les échanges avec la sub-surface (entrainement et mélange), chacun de ces termes étant d'égale importance. Même si ces processus sont connus de façon qualitative, quantifier l'effet de chacun d'entre eux est toujours une question ouverte. Cette thèse a pour but de : a) quantifier les mécanismes responsables de la variabilité de la salinité de surface dans l'Océan Pacifique tropical (principalement aux échelles saisonnières et interannuelles), b) décrire et évaluer les processus à l'origine des variations de salinité de surface pendant l'évènement La Nina de 2010-2011 et c) analyser la formation et la variabilité du noyau de maximum de sel de l'Océan Pacifique subtropical (aux mêmes échelles de temps). Différents jeux de données sont utilisés conjointement : des observations de salinité in situ (bateaux marchands, profileurs Argo ...), des données de salinité de surface dérivées du nouveau satellite SMOS et d'autres produits issus de mesures satellitaires (précipitations, évaporation et courants de surface) ainsi qu'une simulation spécifique d'un modèle forcé.Salinity is one of the key parameters of the ocean impacting its dynamics through density. It is considered as an Essential Climate Variable. The salinity patterns result from a subtle balance between surface forcing (E-P, Evaporation minus Precipitation), horizontal salt advection (at low and high frequencies) and subsurface forcing (entrainment and mixing), all terms being of analogous importance. While processes responsible for sea surface salinity (SSS) changes are qualitatively well known, quantifying those mechanisms is very challenging and hence still under debate. My Ph.D. research work aims at: a) quantifying mechanisms responsible for the tropical Pacific Ocean SSS variability (mainly at seasonal and ENSO time scale), b) describing and assessing mechanisms behind the 2010-2011 La Niña SSS changes, and c) analysing the formation and variability of the south Pacific subtropical high SSS core (at the same time scales). In order to do so, various datasets are used conjointly: in-situ salinity observations mainly from voluntary observing ships and Argo profilers, satellite based surface salinity (from SMOS), precipitation, evaporation and near-surface currents as well as a specific forced model simulation

Integration Frameworks for Merging Satellite Remote Sensing Observations with Hydrological Model Outputs

With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of hydrological processes by integrating them with hydrological models. In this regard, data assimilation technique can be used to constrain the dynamic of a model with available observations in order to improve its estimates. In this thesis, a comprehensive data assimilation framework containing multiple stages is proposed and tested over various areas

Statistical analysis and combination of active and passive microwave remote sensing methods for soil moisture retrieval

Knowledge about soil moisture and its spatio-temporal dynamics is essential for the improvement of climate and hydrological modeling, including drought and flood monitoring and forecasting, as well as weather forecasting models. In recent years, several soil moisture products from active and passive microwave remote sensing have become available with high temporal resolution and global coverage. However, for the improvement of a soil moisture product and for its proper use in models or other applications, validation and evaluation of its spatial and temporal patterns are of great importance. In chapter 2 the Level 2 Soil Moisture and Ocean Salinity (SMOS) soil moisture product and the Advanced Scatterometer (ASCAT) surface soil moisture product are validated in the Rur and Erft catchments in western Germany for the years 2010 to 2012 against a soil moisture reference created by a hydrological model, which was calibrated by in situ observations. Correlation with the modeled soil moisture reference results in an overall correlation coefficient of 0.28 for the SMOS product and 0.50 for ASCAT. While the correlation of both products with the reference is highly dependent ontopography and vegetation, SMOS is also strongly influenced by radiofrequency interferences in the study area. Both products exhibit dry biases as compared to the reference. The bias of the SMOS product is constant in time, while the ASCAT bias is more variable. For the investigation of spatio temporal soil moisture patterns in the study area, a new validation method based on the temporal stability analysis is developed. Through investigation of mean relative differences of soil moisture for every pixel the temporal persistence of spatial patterns is analyzed. Results indicate a lower temporal persistence for both SMOS and ASCAT soil moisture products as compared to modeled soil moisture. ASCAT soil moisture, converted to absolute values, shows highest consistence of ranks and therefore most similar spatio-temporal patterns with the soil moisture reference, while the correlation of ranks of mean relative differences is low for SMOS and relative ASCAT soil moisture products. Chapter 3 investigates the spatial and temporal behavior of the SMOS and ASCAT soil moisture products and additionally of the ERA Interim product from a weather forecast model reanalysis on global scale. Results show similar temporal patterns of the soil moisture products, but high impact of sensor and retrieval types and therefore higher deviations in absolute soil moisture values. Results are more variable for the spatial patterns of the soil moisture products: While the global patterns are similar, a ranking of mean relative differences reveals that ASCAT and ERA Interim products show most similar spatial soil moisture patterns, while ERA and SMOS products show least similarities. Patterns are generally more similar between the products in regions with low vegetation. [...