The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Earth observation-based operational estimation of soil moisture and evapotranspiration for agricultural crops in support of sustainable water management

Global information on the spatio-temporal variation of parameters driving the Earth’s terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission

SATELLITE MICROWAVE MEASUREMENT OF LAND SURFACE PHENOLOGY: CLARIFYING VEGETATION PHENOLOGY RESPONSE TO CLIMATIC DRIVERS AND EXTREME EVENTS

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earthÆs ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators

Satellite studies of the lower atmosphere

Meteorological satellite measurements of lower atmospher

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology, and climatic modeling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) climate change initiative (CCI), using finer spatial resolution synthetic aperture radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse-scale CCI essential climate variable (ECV) soil moisture (ECV SM) product in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture data sets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture data sets at the Finnish sites. Overall, the two different satellite-derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse-scale ECV SM product to track soil moisture variability in time

Assimilation des données GRACE dans le modèle MESH pour l’amélioration de l'estimation de l'équivalent en eau de la neige

Abstract: Water storage changes over space and time play a major rule in the Earth’s climate system through the exchange of water and energy fluxes among the Earth’s water storage compartments and between atmosphere, continents, and oceans. In many parts of northern-latitude areas spring meltwater controls the availability of freshwater resources. With respect to terrestrial hydrologic process, snow water equivalent (SWE) is the most critical snow characteristic to hydrologists and water resource managers. The first objective of this study examined the spatiotemporal variations of terrestrial water storages and their linkages with SWE variabilities over Canada. Terrestrial water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE), the WaterGAP Global Hydrology Model (WGHM), and the Global Land Data Assimilation System (GLDAS) were employed. SWE anomaly (SWEA) products were provided by the Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The grid cell (1°×1°) and basin-averaged analyses were applied to find any possible relationship between TWSA and SWEA over the Canadian territory, from December 2002 to March 2011. Results showed that GRACE versus CMC provided the highest percentage of significant positive correlation (62.4% of the 1128 grid cells), with an average significant positive correlation coefficient of 0.5, and a maximum of 0.9. In western Canada, GRACE correlated better with multiple SWE data sets than GLDAS. Yet, over eastern Canada, mainly in the northern Québec area (~ 55ºN), GRACE provided weak or insignificant correlations with all snow products, while GLDAS appeared to be significantly correlated. For the TWSA-SWEA analysis at the basin-averaged scale, significant relationships were observed between TWSA and SWEA for most of the fifteen basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products, compared to satellite-based SWE data. Stronger relationships were found in snow-dominated basins (Rs >= 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in northern Québec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behavior was observed in the western Hudson Bay Basin. In both regions, it was found that the contribution of non-SWE compartments, including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the WGHM simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products (CMC, GlobSnow2, AMSR-E). The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). In general, results revealed the importance of SWE changes in association with the terrestrial water storage (TWS) variations. The second objective of this thesis investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from GRACE, can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). This study examined the incorporation and development of the ensemble-based GRACE data assimilation framework into the MESH modeling framework for the first time. The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5 % and improved unbiased root-mean-square difference (ubRMSD) by 23 %. At the grid scale, the DA method improved ubRMSD values and correlation coefficients of SWE estimates for 85 % and 97 % of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it could effectively improve the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced the simulation of streamflow estimates.Les variations dans l'espace et le temps du stock d'eau à travers jouent un rôle important dans le système climatique de la Terre à travers l'échange des flux d'eau et d'énergie entre les compartiments du stock d’eau de la Terre, et entre l'atmosphère, les continents et les océans. Dans les régions nordiques, la fonte de la neige contrôle la disponibilité des ressources en eau. Concernant le processus hydrologique terrestre, l'équivalent en eau de la neige (SWE) est la caractéristique de neige la plus importante pour les hydrologues et les gestionnaires des ressources en eau. Le premier objectif de cette étude a examiné les variations spatio-temporelles des réservoirs terrestres d'eau et leurs liens avec les variabilités de SWE au Canada. Des anomalies de stockage d'eau terrestre (TWSA) provenant de GRACE (Gravity Recovery and Climate Experiment), du modèle hydrologique mondial WaterGAP (WGHM) et du modèle GLDAS (Global Land Data Assimilation System) ont été utilisées. Les produits du SWEA (Snow Water Equiavalent Anomaly) sont fournis par le GlobSnow2 (Global Snow Monitoring for Climate Research version 2), le AMSR-E (Advanced Microwave Scanning Radiometer‐Earth Observing System) et le Centre météorologique canadien (CMC). L'analyse par cellule de grille (1°×1°) a été appliquée pour trouver toute relation possible entre TWSA et SWEA sur le territoire canadien, de décembre 2002 à mars 2011. Les résultats montrent que GRACE par rapport à CMC a fourni le pourcentage le plus élevé de corrélation positive significative (62,4% des 1128 cellules de la grille), avec un coefficient de corrélation positif significatif moyen de 0,5 et un maximum de 0,9. Dans la partie ouest du pays, GRACE a montré un meilleur accord avec plusieurs produits SWE que GLDAS. Pourtant, dans l'est du Canada, principalement dans le nord du Québec (~ 55° N), GRACE a fourni des corrélations faibles ou insignifiantes avec tous les produits SWE, contrairement à GLDAS qui semblait être significativement corrélé. Dans le cas de l’analyse à l'échelle du bassin versant, les relations significatives ont été observées entre TWSA et SWEA pour la plupart des quinze bassins considérés (53% à 80% des bassins, selon les produits SWE considérés). Les meilleurs résultats ont été obtenus avec les produits CMC SWE, par rapport aux données SWE satellitaires. Des relations plus fortes ont été trouvées dans les bassins dominés par la neige (Rs> = 0,7), tels que le bassin versant de Liard [erreur quadratique moyenne (RMSE) = 21,4 mm] et le bassin versant de Peace (RMSE = 26,76 mm). Cependant, malgré une forte accumulation de neige dans le nord du Québec, GRACE a montré des corrélations faibles ou insignifiantes avec SWEA, peu importent les sources de données. Le même comportement a été observé dans le bassin versant ouest de la Baie d’Hudson. Dans les deux régions, il a été constaté que la contribution des compartiments non-SWE, y compris les zones humides, les eaux de surface, ainsi que les stocks d'eau du sol a un effet significatif sur les variations du stock total. Ces composantes ont été estimées à l'aide des simulations du modèle WGHM, puis soustraites des observations GRACE. Ces résultats de corrélation SWEA dérivés de GRACE ont montré une amélioration des relations avec les trois produits SWE (CMC, GlobSnow2, AMSR-E). L'amélioration est particulièrement importante dans les sous-bassins de la Baie d’Hudson, où des résultats très faibles et insignifiants avaient été précédemment trouvés avec les données GRACE TWSA. La SWEA dérivée de GRACE a montré une relation significative avec les données CMC dans 93% des bassins (13% de plus que GRACE TWSA). En somme, les résultats obtenus dans ce premier objectif ont montré le rôle important du SWE dans les variations du stock terrestre de l'eau dans la région d’étude. Le deuxième objectif de cette thèse examine si l'intégration des informations de TWS (terrestrial water storage) dérivées de GRACE (Gravity Recovery and Climate Experiment), peut améliorer les simulations du SWE et du débit d’eau dans un modèle hydrologique semi-distribué de schéma de surface. Un cadre d'assimilation de données (DA) a été développé pour combiner les observations TWS avec le modèle MESH (Modélisation Environnementale Communautaire - Hydrologie de Surface) en utilisant un ensemble Kalman Smoother (EnKS). Cette étude était la première du genre à tenter une assimilation des données GRACE dans le modèle MESH pour améliorer l’estimation du SWE. Le bassin versant de la Liard dominé par la neige a été choisi pour le site d’étude. À l’échelle du bassin versant, la méthodologie d'assimilation proposée a réduit le biais des simulations mensuelles de SWE à 17,5% et amélioré le ubRMSD (unbiased root-mean-square difference) de 23%. À l'échelle de la grille, la méthode DA a amélioré l’estimation du SWE pour les valeurs ubRMSD et les coefficients de corrélation pour 85% et 97% des cellules de la grille, respectivement. Les effets de GRACE DA sur les simulations de débit ont été évalués par rapport aux observations de trois stations des débits, où il pourrait effectivement améliorer la simulation des débits élevés pendant la saison de fonte de la neige d'avril à juin. L'influence de GRACE DA sur le volume total et les faibles débits d’eau a été trouvée variable. En général, l'utilisation des observations GRACE dans le cadre d'assimilation non seulement a amélioré la simulation de SWE, mais a également influencé efficacement la simulation des estimations de débit

Data Fusion and Synergy of Active and Passive Remote Sensing; An application for Freeze Thaw Detections

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The dielectric change between frozen ice and melted water can dramatically affect the brightness temperature (TB) signal when water transits from the liquid to the solid phase which makes satellite-based microwave remote sensing unique for characterizing the surface freeze thaw status. Passive microwave (PMW) sensors with coarse resolution (about 25 km) but more frequent observations (at least twice a day and more frequent in polar regions) have been successfully utilized to define surface state in terms of freeze and thaw in the past. Alternatively, active microwave (AMW) sensors provide much higher spatial resolution (about 100 m or less) though with less temporal resolution (each 12 days). Therefore, an integration of microwave data coming from different sensors may provide a more complete estimation of land freeze thaw state. In this regard, the overarching goal of this research is to explore estimating high spatiotemporal freeze and thaw states using the combination of passive and active microwave observations. To obtain a high temporal resolution TB, this study primarily builds an improved diurnal variation of land surface temperature from integration of infrared sensors. In the next step, a half an hourly diurnal cycle of TB based on fusion of different passive sensors is estimated. It should be mentioned that each instrument has its own footprint, resolution, viewing angle, as well as frequency and consequently their data need to be harmonized in order to be combined. Later, data from an AMW sensor with fine spatial resolution are merged and compared to the corresponding passive data in order to find a relation between TB and backscatter data. Subsequently, PMW TB map can be downscaled to a higher spatial resolution or AMW backscatter timeseries can be generalized to high temporal resolution. Eventually, the final high spatiotemporal resolution TB product is used to examine the freeze thaw state for case studies areas in Northern latitudes